# Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link
Prediction

Maxwell A. Weinzierl<EMAIL_ADDRESS>[ Sanda M. Harabagiu
<EMAIL_ADDRESS>[ Human Language Technology Research Institute, Department
of Computer Science, The University of Texas at Dallas, Richardson, TX, USA

###### Abstract

Enormous hope in the efficacy of vaccines became recently a successful reality
in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled
by exposure to social media misinformation about COVID-19 vaccines became a
major hurdle. Therefore, it is essential to automatically detect where
misinformation about COVID-19 vaccines on social media is spread and what kind
of misinformation is discussed, such that inoculation interventions can be
delivered at the right time and in the right place, in addition to
interventions designed to address vaccine hesitancy. This paper is addressing
the first step in tackling hesitancy against COVID-19 vaccines, namely the
automatic detection of known misinformation about the vaccines on Twitter, the
social media platform that has the highest volume of conversations about
COVID-19 and its vaccines. We present CoVaxLies, a new dataset of tweets
judged relevant to several misinformation targets about COVID-19 vaccines on
which a novel method of detecting misinformation was developed. Our method
organizes CoVaxLies in a Misinformation Knowledge Graph as it casts
misinformation detection as a graph link prediction problem. The
misinformation detection method detailed in this paper takes advantage of the
link scoring functions provided by several knowledge embedding methods. The
experimental results demonstrate the superiority of this method when compared
with classification-based methods, widely used currently.

††journal: International Journal of Biomedical Informatics

url]www.utdallas.edu/ Maxwell.Weinzierl url]www.utdallas.edu/ sanda

##  1 Introduction

Enormous hope in the vaccines that inoculate against the SARS-CoV-2 virus, the
causative agent of COVID-19, has been building, starting with 2020. When
several vaccines have become available, millions signed up and received the
vaccines enthusiastically. However, too many remain hesitant. Much hesitancy
is driven by misinformation about the COVID-19 vaccines that is spread on
social media. In fact, recent research by [1] has shown that exposure to
online misinformation around COVID-19 vaccines affects intent to vaccinate in
order to protect oneself or others. Therefore, it is essential to
automatically detect where misinformation about COVID-19 vaccines on social
media is spread and what kind of misinformation is discussed, such that
inoculation interventions can be delivered at the right time and in the right
place, in addition to interventions designed to address vaccine hesitancy.

Figure 1: Example of tweets containing misinformation about COVID-19 vaccines
and the Misinformation Targets referred to by them.

In this paper we address the first step in tackling hesitancy against COVID-19
vaccines, namely the automatic detection of known misinformation about the
vaccines on Twitter, the social media platform that has the highest volume of
conversations about COVID-19 and its vaccines. Conversations about the
COVID-19 vaccines on Twitter from January to April 2021 were prevalent, with
more than 31 million mentions, followed by news, forums, blogs, Reddit, and
Tumblr, according to research at www.iqvia.com, discussed on one of their
blogs [2]. The detection of misinformation about COVID-19 vaccines is
fundamental in understanding its impact on vaccine hesitancy, by discovering
which misinformation is adopted or rejected and how it may influence the
attitudes with respect to vaccination. As with misinformation about COVID-19
in general, there are several misconceptions that are targeted when spreading
misinformation about vaccines. These Misinformation Targets (MisTs) address
commonly known misconceptions about the vaccines. As illustrated in Figure 1,
where two MisTs are illustrated, tweets containing misinformation may be
referring to one or multiple MisTs. For example, Tweet1 refers only to MisTA,
whereas Tweet2 refers both to MisTA and MisTB. In order to discover
automatically which tweets contain misinformation and to which MisT they
refer, we need to design a supervised misinformation discovery method that can
be trained on a sufficiently large collection of tweets annotated with
misinformation judgements. However, state-of-the-art methods use deep learning
techniques, which require a very large training dataset, which is expensive to
build. Nevertheless, such a dataset could be bootstrapped from a seed dataset
of high quality when a method of detecting misinformation could operate on it.

In this paper we introduce a tweet dataset annotated with misinformation about
COVID-19 vaccines, called CoVaxLies, which was inspired by the recently
released COVIDLies dataset [3], as well as a method of discovering
misinformation on it which can predict links between tweets and MisTs, similar
to the links illustrated in Figure 1. Our framework of discovering
misinformation has several novelties. First, it considers that the
misinformation about COVID-19 vaccines can be represented as a Misinformation
Knowledge Graph (MKG), in which nodes are tweets that contain misinformation,
while edges correspond to the MisTs shared by tweets. Secondly, we propose a
representation of the MKG through knowledge embeddings that can be learned by
several possible knowledge embeddings models. Thirdly, we use the link ranking
functions available from each such knowledge embedding models for predicting a
link between any tweet that may contain misinformation and tweets that share a
known MisT. Finally, we project the linguistic content of tweets in the
embedding space of the MKG to account not only for the misinformation
structure, but also for the language that expressed it. The neural
architecture that accounts for all these novelties, a system for Twitter
Misinformation Detection through Graph Link Prediction (TMD-GLP) has produced,
in our experiments, very promising results on the CoVaxLies dataset,
especially when compared with a neural method that casts misinformation
detection as a classification problem, as most current methods do.

The remainder of the paper is organized as follows. Section 2 describes the
related work while Section 3 details the approach used for retrieving tweets
relevant to known MisTs regarding COVID-19 vaccines, as well as the expert
judgements produced on the relevant data. Section 4 describes our graph-based
bootstrapping for misinformation detection and details the neural architecture
for Twitter Misinformation Detection through Graph Link Prediction (TMD-GLP).
Section 5 presents the experimental results while Section 6 is providing
discussions of the results. Section 7 summarizes the conclusions.

##  2 Related Work

There are two schools of thought for detecting misinformation on social media,
based on (1) the identification of whether a social media posting contains or
not misinformation, sometime qualified as a rumour; or (2) taking into account
known misconceptions and discovering those postings that propagate a certain
misconception. Most of the work belongs to the first school of thought.

Misinformation Detection as Rumour Identification on Social Media: Early work
aiming the identification of social media postings that contain misinformation
(without being interested in its misinformation target) focused on finding
useful features for detecting misinformation, e.g. special characters,
specific keywords and expression types, [4], [5], [6] or the characteristics
of users involved in spreading the misinformation, e.g. the number of
followers, the users’ ages and genders [4]; [7], and the news’ propagation
patterns [4], [8]. More recent work embraced several deep learning methods.
These deep learning methods were informed by the textual content of the tweet
containing the misinformation, capturing its semantics [9], or by encoding the
content of the tweets responding to the misinformation [10]. Moreover, a joint
recurrent and convolutional network model (CRNN) was reported in [11], to
better represent the profile of retweeters. Other deep learning-based methods
for the identification of misinformation leveraged the propagation structure
in the social network. [12] created a kernel-based method that captures high-
order interactions differentiating different forms of misinformation while
[13] designed a tree-structured recursive neural network to learn the
embedding of the rumor propagation structure. Another interesting deep
learning framework, reported in [14], considered the prevalence of
deliberately promoted misinformation campaigns, which can be identified by
relying on Generative Adversarial Networks. Most of these misinformation
detection methods were influenced by the datasets on which they were
developed.

Several well-known benchmark datasets for misinformation detection on Twitter
were used previously. For example, the Twitter15 [15] and Twitter16 [10]
datasets consist of a collection of 1490, and 818 respectively, source tweets,
along with their corresponding sequences of retweets and replies, forming
propagation threads. There were a total of 331,612 propagation threads
involving 276,663 users in Twitter15 and 204,820 propagation threads involving
173,487 users in Twitter16. The source tweets were all annotated in balanced
sets (of 370 tweets in Twitter15 and 205 tweets in Twitter16) as true rumors,
false rumors, unverified rumors or non-rumors. These combined datasets allowed
several researchers to develop promising methods trained on the tweets labeled
as true or false rumors, while modeling the not only the content of the
tweets, but also the retweet/reply sequence of users, along with user
profiles. For example, in [16] a graph-aware representation of user
interactions was proposed for detecting the correlations between the source
tweet content and the retweet propagation though a dual co-attention
mechanism. The same idea was explored on the same dataset in the dEFEND system
[17]. The PHEME dataset [18] consists of Twitter conversation threads
associated with 9 different newsworthy events such as the Ferguson unrest, the
shooting at Charlie Hebdo, or Michael Essien contracting Ebola. A conversation
thread consists of a tweet making a true and false claim, and a series of
replies. There are 6,425 conversation threads in PHEME, while only 1,067
claims from tweets were annotated as true, 638 were annotated as false and 697
as unverified. A fraction of the PHEME dataset was used in the RumourEval task
[19], having only 325 threads of conversations and 145 claims from tweets
labeled as true, 74 as false and 106 as unverified. Misinformation detection
methods operating both on PHEME and on the RumourEval data sets used either a
sifted multi-task learning model with a shared structure for misinformation
and stance detection [20], Bayesian Deep Learning models [21] or Deep Markov
Random Fields [22]. However, none of these benchmark datasets contain any
misinformation about COVID-19 or the vaccines used to protect against it.

Detection of Known COVID-19-Related Misinformation: Very recently, a new
dataset of tweets containing misinformation about COVID-19, called COVIDLies
was released [3]. COVIDLies is a dataset, which unlike previous datasets that
considered a large set of “popular” claims, which were later judged as true,
false or unverifiable, was generated by starting with 86 known misconceptions
about COVID-19, available from a Wikipedia article dedicated to misinformation
about COVID-19. The misconceptions informed the retrieval of 6761 related
tweets from COVID-19-related tweets identified by Chen et al[23]. The
retrieved tweets were further annotated by researchers from the University of
California, Irvine School of Medicine with stance information, reflecting
their judgement whether the author of the tweet agreed with a given
misconception, rejected the misconception, or the tweet had no stance.
Furthermore, COVIDLies enabled the design of a system that could identify
misinformation and also infer its stance through a form of neural entailment,
as reported in [3]. We also used the COVIDLies dataset in recent work to infer
automatically when misinformation about COVID-19 is rejected or adopted, by
automatically discovering the stance of each tweet against the 86 available
misconceptions, which were organized in a taxonomy of misconception themes and
concerns. When using a neural architecture that benefits from stacked Graph
Attention Networks (GATs) for lexico-syntactic, semantic and emotion
information, we have obtained state-of-the-art results for stance detection on
this dataset, as we report in [24].

We were intrigued and inspired by the COVIDLies dataset, and believed that if
we could create a similar dataset containing misinformation about COVID-19
vaccines, which would not only complement the COVIDLies data, but it would
also enable the development of novel techniques for misinformation detection.
Therefore, in this paper we present the CoVaxLies dataset as well as a novel
methodology of automatically detecting misinformation using it. We
deliberately decided to generate the CoVaxLies dataset using a similar
methodology as the one employed in the creation of the COVIDLies dataset,
namely by starting with misconceptions or myths about the vaccines used to
immunize against COVID-19 available on a Wikipedia article dedicated to them.
But, we cast the misinformation detection problem differently. We still
considered the retrieval phase essential for finding relevant tweets for the
known vaccine myths, but we explored two different retrieval methods: one
using the classic, BM25 [25] scoring function, and the other using the same
neural scoring method that was used in the creation of COVIDLies. This allowed
us to discover that classical scoring functions outperform scoring functions
using BERT-informed methods. We then focused on producing high-quality
judgements for 7,246 tweets against 17 Misinformation Targets (MisTs) about
COVID-19 vaccines of interest. Once the CoVaxLies dataset was generated, we
were able to design a novel, simple and elegant method for discovering
misinformation which was cast as learning to predict links in a Misinformation
Knowledge Graph. Although our method for automatically detection
misinformation in a collection of tweets uses deep learning techniques, as
most of the recent approaches, it is the first method that represents
misinformation as a knowledge graph, which can be projected in an embedding
space through one of several possible knowledge embedding models.

ID |  Misinformation Target |  Information Source
---|---|---
1 |  RNA alters a person’s DNA when taking the COVID-19 vaccine. |  Wikipedia (BBC) [26]
2 |  The COVID-19 vaccine causes infertility or miscarriages in women. |  Wikipedia (Science-Based Medicine) [26]
3 |  Natural COVID-19 immunity is better than immunity derived from a COVID-19 vaccine. |  UC Davis Health [27]
4 |  The COVID-19 vaccine causes Bell’s palsy. |  Wikipedia (Snopes) [26]
5 |  The immune system overreacts to COVID-19 after taking the COVID-19 vaccine through antibody-dependent enhancement. |  Wikipedia (Health Feedback) [26]
6 |  The COVID-19 vaccine contains tissue from aborted fetuses. |  Wikipedia (Snopes) [26]
7 |  The COVID-19 vaccine was developed to control the general population either through microchip tracking or nanotransducers in our brains. |  Mayo Clinic [28]
8 |  More people will die as a result of a negative side effect to the COVID-19 vaccine than would actually die from the coronavirus. |  Mayo Clinic [28]
9 |  There are severe side effects of the COVID-19 vaccines, worse than having the virus. |  Mayo Clinic [28]
10 |  The COVID-19 vaccine is not safe because it was rapidly developed and tested. |  Mayo Clinic [28]
11 |  The COVID-19 vaccine can cause COVID-19 because it contains the live virus. |  University of Missouri Health Care [29]
12 |  The COVID-19 vaccine causes people to test positive for COVID-19. |  University of Missouri Health Care [29]
13 |  The COVID-19 vaccine can increase risk for other illnesses. |  University of Missouri Health Care [29]
14 |  Many people already have died from the COVID-19 vaccine trials. |  University of Alabama at Birmingham [30]
15 |  The COVID-19 vaccine can cause autism. |  University of Alabama at Birmingham [30]
16 |  The COVID-19 vaccine should not be taken by people who are allergic to eggs. |  Mayo Clinic [31]
17 |  Vaccines contain unsafe toxins such as formaldehyde, mercury or aluminum. |  PublicHealth.org [32]
Table 1: COVID-19 Misinformation Targets and their sources of information.

##  3 Retrieving Misinformation about COVID-19 Vaccines from Twitter

###  3.1 COVID-19 Misinformation Targets

Misinformation about the COVID-19 vaccines has propagated widely, and has been
shown to decrease vaccination intent in the UK and USA [1]. Medical, public
health, and news organizations have scrambled to collect and rebut
misinformation surrounding the COVID-19 vaccine in response to this spread.
Organizations such as the Mayo Clinic, University of Missouri Health Care,
University of California (UC) Davis Health, University of Alabama at
Birmingham, Science-Based Medicine, PublicHealth.org, Snopes, and the British
Broadcast Corporation (BBC) have been actively collecting misinformation about
the COVID-19 vaccines and debunking them on public websites. Wikipedia also
has an entire page dedicated to COVID-19 misinformation, which collects many
misconception claims, including those referring to the vaccines developed for
immunization against the COVID-19 virus. The Wikipedia page available at
en.wikipedia.org/wiki/COVID-19_misinformation#Vaccines also provides citations
to scientific articles that debunk the misconceptions. For example, the BBC is
cited on the Wikipedia page above mentioned for identifying and debunking the
misconception that “RNA alters a person’s DNA when taking the COVID-19
vaccine.” In the cited article [33] the authors claim that “The fear that a
vaccine will somehow change your DNA is one we’ve seen aired regularly on
social media.” They immediately debunk this misinformation by claiming: “The
BBC asked three independent scientists about this. They said that the
coronavirus vaccine would not alter human DNA.”

We took advantage of the existing efforts of pinpointing the misconceptions
related to the COVID-19 vaccines and debunking them. We selected 17
misinformation claims, which we considered as Misinformation Targets, because
the propagation of misinformation on social media targets one or several such
misconceptions. In Table 1 we list all the Misinformation Targets (MisTs) that
we considered in this study along with their source of information.

###  3.2 Retrieval Methods for Identifying Tweets with Potential
Misinformation Content

Before using the Twitter streaming API to collect tweets discussing the
COVID-19 vaccine, approval from the Institutional Review Board at the
University of Texas at Dallas was obtained: IRB-21-515 stipulated that our
research met the criteria for exemption #8(iii) of the Chapter 45 of Federal
Regulations Part 46.101.(b). When using the search query “(covid OR
coronavirus) AND vaccine” on the Twitter streaming API, we collected 840,299
English tweets and we ignored all retweets. Our investigation of the Twitter
platform revealed that Twitter’s tokenization of tweets splits up terms like
“covid19” and “covid-19” into “covid” and “19”. Therefore we selected “covid”
as a search term that matches the tokenization not only of mentions of ”covid”
in tweets, but also mentions of ”covid-19” or ”covid19”, thus optimizing the
recall of relevant tweets, when combined with the keywords “coronavirus” and
“vaccine”. The retrieved tweets were authored in the time frame from December
18th, 2019, to January 4th, 2021. A large fraction of these tweets were
duplicates, likely due to spam bots, which required filtering. Locality
Sensitive Hashing (LSH) [34] is a well-known method used to remove near-
duplicate documents in large collections. We perform LSH, with term trigrams,
100 permutations, and a Jaccard threshold of 50%, on our collection to produce
$\cal{C}_{T}$= 753,017 unique tweets.

We found that approximately 35% of the unique tweets in $\cal{C}_{T}$ referred
to an external news article, YouTube video, blog, or other website. Therefore,
we also crawled these external links and parsed their contents with
Newspaper3k [35] to include their titles with the original tweets. We found
these titles added significantly more context to many of these tweets, and
allowed us to identify many more instances of misinformation during the human
judgement phase. For example, the tweet “Who still want to be vaccinated? URL:
“Vaccine causing bells palsy: Pfizer vaccine side effects, covid” would be
impossible to identify as pertaining to MisT4 without the article’s context.

In order to identify in $\cal{C}_{T}$ those tweets which potentially contain
language relevant to the MisTs of interest, listed in Table 1, we relied on
two information retrieval systems: (1) a retrieval system using the BM25 [25]
scoring function; and (2) a retrieval system using BERTScore [36] with Domain
Adaptation (DA), identical to the one used in [3]. Both these retrieval
systems operated on an index of $\cal{C}_{T}$, obtained by using Lucene [37].
Each retrieval system produced a ranked list of tweets when queried with the
textual content of any of the MisTs of interest, listed in Table 1. At most
200 top scored tweets were selected for each of these queries. We selected
only 200 best scored tweets because (1) the same number of tweets was
considered in the most similar prior work [3]; and (2) it was a number of
tweets that did not overwhelm our human judges. Additional tweets were also
retrieved when replacing the word “COVID-19” with the word “coronavirus” in
each query. From the top ranked tweets deemed relevant to the modified query,
at most 200 tweets were also considered. This approach produced a maximum of
400 tweets, but the list of retrieved tweets returned by the retrieval systems
often contained less than 400 tweets. For example, when MisT10 was used as a
query, we were able to retrieve only 213 distinct tweets, whereas when MisT11
was used as a query, we retrieved 282 distinct relevant tweets. Some of the
tweets retrieved when the query that was used was one of the MisTs listed in
Table 1 were also retrieved when the modified query was used. In the end, we
retrieved a total of 4,153 tweets that were deemed relevant to at least one
MisT of interest.

When using the retrieval system which relies on the BERTScore (DA), we were
able to benefit from its domain-adaptive pre-training on 97 million COVID-19
tweets. In this way, semantic relevancy to the domain of COVID-19 was
preferred over keyword matching when scoring the tweets against the MisTs of
interest. As with the first retrieval system, at most 200 top scored tweets
were selected for the original queries and at most other 200 tweets when the
modified queries, replacing the word “COVID-19” with the word “coronavirus”,
were used. In this way, the second retrieval system enabled us to collect
4,689 tweets deemed relevant to at least one MisT of interest. Obviously, the
second retrieval system enabled us to collect a larger set of tweets deemed
relevant to the MisTs of interest than the first retrieval system (4,689
tweets vs. 4,153 tweets), which we attribute to significant sensitivity in
BERTScore when replacing the word “COVID-19” with “coronavirus” in the query.
As in the case when the retrieval system using the BM25 scoring function, some
of the tweets retrieved by the retrieval system using when BERTScore (DA),
when the query was one of the MisTs listed in Table 1, were also retrieved
when the modified query was used. This time, the fewest distinct tweets were
returned for the MisT4 (only 234 distinct tweets), whereas when the query used
the MisT3, we collected 332 distinct tweets. We finally combined the
collection of unique tweets retrieved with both systems, obtaining a set
$\cal{T}_{R}$ of 7,246 tweets deemed relevant to our MisTs of interest. A
total of 1,596 tweets from $\cal{T}_{R}$ were considered relevant to the same
query (any MisT) by both retrieval systems. It is to be noted that some tweets
from $\cal{T}_{R}$ were relevant to more than one of the MisTs listed in Table
1.

Figure 2: Misinformation Detection as Graph-Link Prediction

###  3.3 Producing Human Judgements for the Misinformation Contained in Tweets

Given each tweet from the $\cal{T}_{R}$ collection of 7,246 tweets deemed
relevant to our MisTs of interest, researchers from the Human Language
Technology Research Institute (HLTRI) at the University of Texas at Dallas
produced judgements of either: Relevant or Not_Relevant against the MisTs
listed in Table 1. Each tweet was read and when it contained a discussion
about one of the MisTs of interest, it was considered relevant. It is to be
noted that sometimes, the tweet could discuss more than one MisT, therefore it
was judged relevant to each MisT it addressed. A total of 836 tweets from
$\cal{T}_{R}$ were judged relevant to two or more MisTs. Alternatively, when
the tweet did not discuss any MisT of interest, it was judged non-relevant to
any of the MisTs. It should be noted that, when a tweet was judged relevant,
no additional judgements about the stance of the tweet with respect to the
MisT were considered, disregarding if the tweet agreed, disagreed with the
MisT, or had no stance about it. This decision was made because stance
detection was regarded as a separate task, as in [3],[24], which requires each
tweet to be first found relevant to the MisT, before inferring its stance. As
it is illustrated in Figure 2, Tweet A is relevant to MisT1, agreeing with it,
while Tweet B is relevant to MisT6, disagreeing with it.

To evaluate the quality of judgements, we randomly selected a subset of 1,000
tweets (along with the MisT against which they have been judged to be relevant
or non-relevant), which have been judged by at least three different language
experts. Percent agreement between annotators was 92%. Fleiss’ Kappa score was
0.83, which indicates strong agreement between annotators (0.8-0.9) [38].
There were high levels of agreement between the annotators of tweets against
specific MisTs: MisT1, Mist2, MisT4, MisT6, MisT7, MisT10, MisT14 and MisT17.
This can be explained by the unique interpretation of these MisTs and their
direct reference in tweets. However, the inter-annotator agreement for the
other MisTs were lower, with the lowest Fleiss’ Kappa score of 0.70 obtained
for MisT8, and the highest Fleiss’ Kappa score of 0.89 obtained for MisT14.
Disagreements in annotations were discussed, but largely came down to
interpretation. For example, the tweet “we dont need a covid vaccine” was
interpreted by one expert as relevant to the MisT3: “Natural COVID-19 immunity
is better than immunity derived from a COVID-19 vaccine”, while another judge
found the tweet non-relevant to the same MisT. For the first annotator, the
statement that no vaccine is necessary for COVID-19 implied that the author of
the tweet entailed that that natural immunity from catching COVID-19 is better
than any vaccine-induced immunity. The second judge took a more strict
interpretation, recognizing that there may be other reasons that the tweet
author might have when stating that a COVID-19 vaccine is unnecessary,
unrelated to the MisT3. The differences of interpretation were analyzed and
resolved, and judges were instructed to be strict in determining relevant
tweets for each MisT of interest. In the end, this collection of judged
tweets, which we call CoVaxLies, contains 7,246 [tweet, MisT] pairs, where
each tweet was judged as either relevant or non-relevant against the MisT it
is paired with.

We split the CoVaxLies into three distinct collections: (a) a training
collection; (b) a development collection; and (c) a test collection. The
training collection, which consists of 5,267 [tweet, MisT] pairs, was utilized
to train our automatic misinformation detection systems, described in Section
4. The development collection, which consists of 527 [tweet, MisT] pairs, was
used to select model hyper-parameters, such as threshold values, as well as
for building the seed of the Misinformation Knowledge Graph, described in
Section 4.1. The test collection, which consists of 1,452 [tweet, Mist] pairs,
was used to evaluate misinformation detection approaches, described in Section
5.

##  4 Graph-Based Bootstrapping of Misinformation Detection

###  4.1 Misinformation Detection as Graph-Link Prediction

The way misinformation spreads on social media platforms inspired us to
consider that misinformation can be represented as a Misinformation Knowledge
Graph (MKG), in which nodes represent tweets, containing misinformation about
COVID-19 vaccines, while links between tweets indicate that they share the
same misinformation, and thus are labeled with the corresponding
Misinformation Target (MisT). Therefore, for each MisT a separate, fully-
connected graph (FCG) can be generated, spanning all tweets that discuss that
MisT. Some tweets may be connected to multiple MisT-specific FCGs, if they
contain information pertaining to more than one MisT. Furthermore, tweets with
text that is not relevant to any of the MisTs of interest shall remain as
unconnected nodes in the MKG. Figure 2 illustrates visualizations of the MKG
with FCGs pertaining to several MisTs, each labeled with a different number,
while the content of the MisT labeled with the same number is illustrated in
the Figure as well. For example, the FCG(MisT4) includes as nodes tweets which
are relevant to the MisT4: “The COVID-19 vaccine causes Bell’s palsy.” The
edges in FCG(MisT4) are all labeled as MisT4. It is to be noted that in Figure
2 some tweets are connected to multiple MisT-informed FCGs. For example, the
tweet “Omg this! URL QT: “It makes no sense. You are ok with people getting
Covid-19 (a dangerous virus) because they are unlikely to die from it in order
to create herd immunity, but not people getting a vaccine that might have some
minor side effects for a small portion to create that immunity?” is relevant
to both MisT8 and MisT9, and thus is represented as a node participating in
both FCG(MisT8) and FCG(MisT9).

The goal of identifying MisT-specific misinformation is achieved by generating
and bootstrapping the MKG for a collection of tweets, such as CoVaxLies, in
two steps:
STEP 1: Generate the seed FCG for each MisT of interest. The seed FCGs, which
are illustrated in Figure 2, were informed by the development set of
CoVaxLies, described in Section 3.3. Whenever two tweets were judged Relevant
to the same MisT, they were connected with a link labeled by that MisT. In
this way, all tweets judged relevant to the same MisT become nodes in an FCG
corresponding to that MisT. Some of the MisT-informed FCGs are larger than
others, because there are more tweets relevant to that MisT in the development
data from CoVaxLies. The largest FCG has 27 tweets, all pertaining to MisT9
while the smallest FCG has only 1 tweet pertaining to MisT16. From the total
of 527 [tweet, MisT] pairs available in the development set of CoVaxLies, in
this step, a total of 245 tweets were assigned to all 17 FCGs, while 170
tweets were left unconnected because they were judged not to be relevant to
any MisT. The number of tweets either connected or not connected to any FCG is
smaller than the number of pairs from the development set of CoVaxLies because
some tweets were relevant to multiple MisTs.
STEP 2: Bootstrap the seed MisT-specific FCGs to span additional tweets
sharing the same misinformation target. The bootstrapping is enabled by
learning to predict if any link(s) can be established between any of the FCGs
and tweets from a collection of tweets. In our experiments, we shall consider
CoVaxLies as the collection of tweets, since CoVaxLies is already split into a
training collection, a development collection and a testing collection,
enabling a machine learning framework. The development collection was used to
create the seeds of the FCGs, in Step 1. In Step 2, we learn to predict links
to the FCGs by considering the training collection of CoVaxLies, and then we
test the quality of predictions on the testing collection of CoVaxLies. This
entails that the bootstrapping has two phases, the first phase relying on the
training collection, while the second phase relying on the testing collection
of CoVaxLies.

In Phase 1 of the bootstrapping of the MKG, the training collection of
CoVaxLies, comprising 5,267 [Tweet, MisT] pairs is used to bootstrap the seed
FCGs that were generated in Step 1. For each [Tweetx, MisTy] pair, a link is
established between the tweetx and the FCG(MisTy), providing a training
example for learning to predict links between other, unconnected tweets and
the various MisT-specific FCGs from the MKG. A consequence of linking any
Tweetx to the FCG(MisTy) is that a node for Tweetx is added to FCG(MisTy),
along with all the MisTy-labeled edges between Tweetx and the remaining nodes
from FCG(MisTy), such that FCG(MisTy) remains a fully connected graph after
incorporating Tweetx. There are only 3,735 unique tweets in the training
collection of CoVaxLies, because many of them were linked to more than one
MisT-specific FCG. Therefore, in Phase 1 of Step 2, in our experiments, we
added initially 5,267 links to the FCGs generated in Step 1, which enabled the
addition of 2,114 tweets to the FCGs produced in Step 1.

In Phase 2 of the bootstrapping of the MKG, the test set of CoVaxLies,
comprising 1,452 [Tweet, MisT] pairs, is considered. However, there are only
1,038 unique tweets in the test collection of CoVaxLies, as many of these
tweets were judged to be relevant to multiple MisTs. All these 1,038 tweets
are initially unconnected to any MisT-specific FCGs, because knowledge about
their relevance to any of the MisTs is withheld, such that the evaluation of
the link predictions can be performed. In Phase 2, bootstrapping of MisT-
specific FCGs is performed by predicting if a link exists from an unconnected
tweet to any FCG. When a link is predicted, misinformation is discovered, and
moreover, its misinformation target is also discerned. If no link is
predicted, then no misinformation pertaining to any of the seventeen MisTs of
interest is discovered. In this way, we cast the problem of misinformation
detection as a graph link prediction problem. For example, given the
$Tweet_{A}$, illustrated in Figure 2, which was initially unconnected to any
FCG in the MKG, we learn to detect that it contains misinformation
characteristic of MisT1, based on the training data produced in the Phase 1 of
Step 2 of bootstrapping. Consequently, a link is predicted between
$Tweet_{A}$, and the FCG(MisT1). Similarly, we learn to predict a link between
$Tweet_{B}$ and the FCG(MisT6), thus discovering new misinformation sharing
that MisT. As link predictions are performed at test time, in Phase 2 of the
bootstrapping of the MKG, tweets are connected to one or multiple of the MisT-
informed FCGs and the final MKG emerges. Moreover, Phase 2 of the Step 2 of
bootstrapping enables us to evaluate the quality of the link prediction models
that we have considered, while Phase 1 of the Step 2 enabled us to train these
models on the training collection of CoVaxLies. It is to be noted that the MKG
can be bootstrapped by considering all the 753,017 tweets from $\cal{C_{T}}$
which do not belong to CoVaxLies, however, the quality of the link prediction
in Phase 2 of the Step 2 could not be evaluated, as no misinformation
relevance judgements are available for those tweets.

When formalizing the MKG as a knowledge graph $MKG=(V;E)$, the tweets are
represented as $t_{i}\in V$, where $V$ represents the entire collection of
tweets from CoVaxLies, while $E$ represents the misinformation links that
might exist between these tweets. For example, all tweets that were linked to
an FCG(MisTy) (either in Step 1 or Step 2 of the bootstrapping of the MKG)
will share edges labeled MisTy, shared by each pair of tweets. This
formalization of the MKG enables us to learn to predict links between
unconnected tweets and the FCGs from the MKG.

For more than a decade, knowledge graphs were represented in a continuous
vector space called the embedding space by learning knowledge embeddings for
its nodes and edges. Given the MKG, knowledge embedding models learn an
embedding $te_{i}$ for each tweet $t_{i}$ and an embedding $me_{j}$ for each
MisTj, with $j$ ranging over all MisTs used as labels in the MKG. More
importantly, knowledge embedding models use a link scoring function for
assigning a plausibility score to any potential link between two tweets of the
MKG, $t_{i}$ and $t_{k}$, relying on the embeddings of the pair of tweets as
well as on the embedding learned for any MisTj : $f(te_{i},me_{j},te_{k})$.
Because knowledge embedding models provide a link scoring function, we adopted
them as an ideal framework for resolving the problem of graph link prediction
and apply it to misinformation detection in tweet collections. In Section 4.2
we discuss several knowledge embedding models and provide their link scoring
functions.

However, the link scoring function is necessary but not sufficient for
bootstrapping the MKG in Phase 2 of Step 2. When deciding to which FCG an
unconnected tweet could be linked, we are presented with two options, each
yielding different computational complexities for computing the link scoring
function:
Option 1: Select all of the tweets from any FCG; or
Option 2 Select a prototypical tweet from each FCG. Because the nodes of each
FCG are represented by their knowledge embeddings, thus vectors, the centroid
of these embeddings can be cast as the prototypical representation of each
FCG. Thus the representation of the prototypical tweet for each FCG needs to
take into account the knowledge embeddings of all the tweets connected to the
FCG. Therefore, for any FCG(MisTx) having $n_{x}$ nodes, corresponding to its
tweets $t^{x}_{i}$, encoded by the knowledge embedding ${te^{x}_{i}}$, the
representation of the prototypical tweet of FCG(MisTx) is provided by its
embedding $pte^{x}$, computed as:

$pte^{x}=\frac{1}{n_{x}}\sum_{k=1}^{n_{x}}{{te^{x}_{k}}}$ (1)

When considering option 1, the link scoring function is computed between a
new, unconnected tweet $t^{y}$ and each of the $n_{x}$ tweets $t^{x}_{i}$ of
each FCG(MisTx). Then, the ConditionALL must be satisfied, where ConditionALL
stipulates that if more than $N_{x}$ number of times the value returned by the
link prediction function is superior to a threshold $T_{x}$, the link is
predicted between $t^{y}$ and FCG(MisTx). Clearly, the number of tweets
$n_{x}$, and $N_{x}$ and $T_{x}$ are dependent on each MisTx, varying across
MisTs. We assign $N_{x}$ and $T_{x}$ automatically by maximizing
misinformation detection performance of the system for MisTx on the
development collection, which is further detailed in Section 5.2. However, the
number of times the link scoring function $f$ needs to be computed when
considering this option is equal to the number of tweets that are already
connected in the MKG at the time of attempting to link a new tweet. This
number easily grows in the thousands - and thus it renders this options
computationally inefficient.

The option 2 presents the advantage that the link prediction function $f$ is
evaluated only once for each MisT encoded in the MKG, and a link is predicted
when the value returned by $f$ is superior to a pre-defined threshold for each
MisT, $T_{x}$. Moreover, in both options, different link scoring functions,
available from different knowledge embedding models, may predict different
graph links in the MKG, and thus discover differently the misinformation in a
collection of tweets.

Our misinformation detection framework using graph link prediction allows for
rapid experimentation with multiple knowledge embedding models to explore
their performance. Each of them enables the learning of misinformation
knowledge embeddings, in the form of knowledge embeddings of the tweets that
may contain misinformation, as well as knowledge embeddings for each MisT of
interest.

Figure 3: Neural Architecture for Twitter Misinformation Detection through
Graph Link Prediction (TMD-GLP).

###  4.2 Learning Misinformation Knowledge Embeddings

Several knowledge embedding models have been widely used in the past decade,
e.g. TransE [39], TransD [40]. In addition to TransE and TransD, several other
knowledge graph embedding models have shown promise in recent years, e.g.
TransMS [41] and TuckER [42]. We have explored how all these four different
knowledge embedding models perform in our framework for misinformation
detection as graph link prediction. We briefly describe them before discussing
how they were used in a novel neural architecture for twitter misinformation
detection as graph link detection.

TransE, like all knowledge embedding models, learns an embedding, for each
node in the knowledge graph and an embedding for each relation type. Given the
MKG, TransE learns an embedding ${te_{i}}$ for each tweet $t_{i}$ and an
embedding ${me_{j}}$ for each MisTj, with $j$ ranging over all MisTs
represented in the MKG. Moreover, TransE considers that the link embedding is
a translation vector between the two tweet embeddings representing its
arguments. This means that for any tweet $t_{i}$, the tweet most likely to be
linked to $t_{i}$ by a link labeled MisTj should be the tweet whose embedding
is closest to $(te_{i}+me_{j})$ in the embedding space. By modeling the tweets
as points in the embedded space and the MisTs they share as translation
vectors, it is possible to measure the plausibility of any potential link
labeled as MisTj between any pair of tweets $t_{i}$ and $t_{k}$ using the
geometric structure of the embedding space:

$f(te_{i},me_{j},te_{k})=-||te_{i}+me_{j}-te_{k}||_{L1}$ (2)

where $||\cdot||_{L1}$ is the $L1$ norm. The plausibility of a relation
between an unconnected tweet $t_{u}$ and tweet $t_{d}$ connected to (or
prototypical of a) FCG-x with labels MisTx, represented as a triple, $\langle
t_{u},MisT_{x},t_{d}\rangle$, is inversely proportional to the distance in the
embedding space between the point predicted by the TransE model,
($te_{u}+me_{x}$), and the point in the embedding space representing the
destination argument of the link labeled as MisTx, i.e. ($te_{d}$). TransE has
the advantage that it is extremely simple to utilize, but interactions between
node embeddings and edge embeddings are limited.

TransD extends TransE by learning two knowledge embeddings for each node and
each edge from a knowledge graph such that the first embedding represents the
“knowledge meaning” of the node or relation while the second embedding is a
projection vector (with superscript p), used to construct a dynamic mapping
matrix for each node/link pair. Thus, for each tweet from the MKG, $t_{i}$
TransD learns the pair of embedding $(te_{i},te^{p}_{i})$ and for each link
labeled as MisTj, it learns the pair of embeddings $(me_{j},me^{p}_{j})$. The
pair of knowledge embeddings for the tweet and for the link are learned by
using a scoring function that measures the plausibility of a link labeled
MisTj between a tweet $t_{i}$ and a tweet $t_{k}$, defined as:

$\begin{split}f(te_{i},me_{j},te_{k})=-\left\lVert(\bm{I}+me^{p}_{j}\times({te^{p}_{i}})^{\top})\times
te_{i}+me_{j}\right.\\\
-\left.(\bm{I}+me^{p}_{j}\times({te^{p}_{k}})^{\top})\times
te_{k}\right\rVert_{L1}\end{split}$ (3)

where $\bm{I}$ is the identity matrix.

TransD improves upon TransE by modeling the interactions between tweets and
the links that span them through their respective knowledge embeddings, such
that tweet embeddings change depending on which MisT is being considered for
labeling a link.

TransMS recognizes the importance of capturing non-linear interactions between
nodes and edges in a knowledge graph, and therefore expands on the approach of
TransD. TransMS introduces non-linear interactions on both the node and edge
knowledge embeddings before the additive translation of TransE is performed,
and also adds an edge-specific threshold parameter $\alpha_{j}$. When
considering the MKG, the knowledge embeddings for the tweet and for the link
are learned by using a scoring function that measures the plausibility of a
link labeled MisTj between a tweet $t_{i}$ and a tweet $t_{k}$, defined as:

$\begin{split}f(te_{i},me_{j},te_{k})=-\left\lVert-tanh(te_{k}\otimes
me_{j})\otimes te_{i}+me_{j}\right.\\\ +\left.\alpha_{j}\cdot(te_{i}\otimes
te_{k})-tanh(te_{i}\otimes me_{j})\otimes te_{k}\right\rVert_{L1}\end{split}$
(4)

where $tanh(x)$ is the non-linear hyperbolic tangent function and $\alpha_{j}$
is a real numbered parameter dependent on each MisT. The operator $\otimes$
represents the Hadamard product.

TransMS improves upon TransD by allowing both the nodes to influence the edge
embeddings and the edges to influence the node embeddings. TransMS also
introduces non-linearities in these interactions, and allows edge type-
specific $\alpha_{j}$ thresholds to be learned.

TuckER utilizes a multiplicative approach to learning knowledge embeddings, as
opposed to the additive approach of TransE, TransD, and TransMS. TuckER
introduces a learned “core tensor” $\mathcal{W}\in\mathbb{R}^{z\times v\times
z}$ which encodes some of the learned knowledge outside of the knowledge
embeddings. When considering the MKG, the knowledge embeddings for tweets and
for links spanning them are learned by using a scoring function that measures
the plausibility of a link labeled MisTj between a tweet $t_{i}$ and a tweet
$t_{k}$. The pair of knowledge embeddings for the tweet and for the link are
learned in TuckER by using a scoring function that measures the plausibility
of a link labeled MisTj between a tweet $t_{i}$ and a tweet $t_{k}$, defined
as:

$\begin{split}f(te_{i},me_{j},te_{k})=\mathcal{W}\times_{1}te_{i}\times_{2}me_{j}\times_{3}te_{k}\end{split}$
(5)

where $\times_{n}$ indicates the tensor product along the n-th mode. TuckER
approaches the problem of learning knowledge embeddings from a multiplicative
perspective, with an additional component in the $\mathcal{W}$ tensor which
allows for additional shared interactions to be learned between the nodes and
edges of the knowledge graph through tensor products.

In addition to the knowledge embedding models, we also considered a K-Nearest
Neighbors (KNN) baseline approach. The KNN approach ignores entirely the edge
information available in the FCC(Mistj) of each Mistj. Instead, this approach
favors tweets that are closest in their representation in the embedding space.
Thus, when scoring a link between an unconnected tweet $t_{i}$, represented by
a knowledge embedding $te_{i}$, and any tweet $t_{k}$, from the FCC(MisTj),
represented as $te_{k}$, it computes:

$f(te_{i},me_{j},te_{k})=-||te_{i}-te_{k}||_{L2}$ (6)

where $||\cdot||_{L2}$ is the $L2$ norm. To predict the link to any MisT of
interest, e.g. MisTx, the ConditionALL, defined in Section 4.1, must be met.
This condition is applied because all nodes that are already assigned to all
FCGs from the MKG need to be considered when searching in the embedding space
for the tweets that are closest to $t_{i}$. For the KNN approach, this
condition requires that there must be at least $N_{x}$ values of $f$ which are
larger than a given threshold $T_{x}$ for all nodes $n_{x}$ connected in the
FCG(Mistx). When the condition is satisfied, a link is predicted between
$t_{i}$ and MisTx. But the condition may be satisfied by more than one MisT of
interest, and thus multiple links may be predicted. This baseline is
equivalent to a KNN approach, where the distance metric is the Euclidean
distance, and the value of $K$ is selected automatically based on (a) the
number of MisTs for which ConditionALL is satisfied, which implicitly depends
on (b) the development collection available from CoVaxLies, which was used to
generate the seed FCGs from the MKG.

Any of the scoring functions of these four knowledge embedding models or the
baseline model using KNN can be used to predict a graph link in the MKG. But,
because the tweets use natural language to communicate their message, and each
of the MisTs also are expressed in natural language, the subtleties and deep
connections expressed in language also need to be captured. The language used
in two different tweets from the MKG, that might be linked together need to
accounted for. In addition, the language describing the MisT they share needs
also to be accounted for when predicting if there is a link. With this in
mind, we have designed a neural architecture for Twitter Misinformation
Detection through Graph Link Prediction (TMD-GLP), illustrated in Figure 3.

Given a MisT $m_{j}$, the TMD-GLP system first performs Word-Piece
Tokenization [43] on the textual description of the misinformation target,
producing tokens $m_{1}^{j},m_{2}^{j},...,m_{l}^{j}$, where $l$ is the number
of word-piece tokens produced. Word-Piece Tokenization segments text into
word-pieces, where the segmentation is data-driven and based on maximizing the
size of frequently occurring sub-word tokens. Common English words remain
intact as whole tokens, while uncommon words are segmented into more common
sub-word components. This process ensures every word can be segmented into a
known vocabulary of sub-word units, and no words are left out-of-vocabulary.
The $[CLS]$ and $[SEP]$ tokens are placed at the beginning and end of the
token sequence respectively, as was done in prior work to denote the beginning
and end of the token sequence [43].

The word-piece tokens $[CLS]^{j},m_{1}^{j},m_{2}^{j},...,m_{l}^{j},[SEP]^{j}$
are then provided to the BERT [43] COVID-19 Language Model COVID-Twitter-
BERT-v2 [44]. COVID-Twitter-BERT-v2 is a pre-trained domain-specific language
model, which means that it started with neural weights equal to those of BERT,
but was additionally pre-trained on the masked language modeling task [43] for
97 million COVID-19 tweets. This process of further pre-training has been
shown to improve performance on downstream tasks in various scientific [45],
biomedical [46], and social media [47] domains. COVID-Twitter-BERT-v2
therefore produces contextualized embeddings
$mc_{1}^{j},mc_{2}^{j},...,mc_{l+2}^{j}$ for the word-piece tokens in the MisT
$m_{j}$ along with the $[CLS]^{j}$ and $[SEP]^{j}$ tokens. In this way, we
encode the language describing the MisT using a contextualized embedding
$mr_{j}\in\mathbb{R}^{1024}$, where $1024$ is the contextual embedding size
for COVID-Twitter-BERT-V2, which is the first contextualized embedding
$mc_{1}^{j}$, representing the initial $[CLS]^{j}$ token embedding.

Similarly, the language used in the tweets $t_{i}$ and $t_{k}$ is processed
through Word-Piece Tokenization and then represented by contextual embeddings
$tr_{i}$ and $tr_{k}$ after being processed through COVID-Twitter-BERT-v2.
But, it is important to note, that the scoring function $f$ of any of the
knowledge embedding models that we considered, illustrated in Figure 3, cannot
operate directly on the contextual embeddings $tr_{i}$, $mr_{j}$ or $tr_{k}$,
as they do not have the same dimensions of the knowledge embeddings these
models learn. Therefore, in TMD-GLP we needed to consider two forms of
projection encoders, capable to project from the contextualized embedding
space into the knowledge embedding space. For this purpose, we have relied on
the Misinformation Knowledge Embedding Projection Encoder (M-KEPE), using a
fully-connected layer to project from $mr_{j}$ into the necessary knowledge
embedding $me_{j}\in\mathbb{R}^{v}$ of any of the knowledge embedding models
considered. Similarly, we have relied on the Tweet Knowledge Embedding
Projection Encoder (T-KEPE) using a different fully-connected layer than
M-KEPE. As shown in Figure 3, these encoders produce the arguments of the
scoring function, which informs the decision whether the link between the
unconnected tweet $t_{i}$ and a tweet from the FCG for MisTj (or its
prototypical tweet) $t_{k}$ can be predicted, according to the two options
described in Section 4.1. If a link is predicted, it receives the label of
MisTj.

We trained the TMD-GLP system to perform graph-link prediction on the training
collection of CoVaxLies described in Section 3.3. Labeled links within the
training data were used as positive examples, and we perform negative link
sampling to construct negative examples. Negative link sampling consists of
corrupting a link triplet $(t_{i},m_{j},t_{k})\in E$. This process is
performed by randomly sampling a different tweet $t_{s}\in V_{T}$, where
$V_{T}$ represents all training tweets, to replace $t_{k}$. We ensure
$(t_{i},m_{j},t_{s})\not\in E$ by re-sampling if we ever sample a link in $E$.
This process guarantees that corrupted triplets are not real links. We utilize
these negative links for learning, with the goal being to utilize the TMD-GLP
system to score labeled links higher than corrupted links. Moreover, we
optimized the following margin loss to train TMD-GLP when performing graph-
link prediction:

$\mathcal{L}=\sum\limits_{\begin{subarray}{c}(t_{i},m_{j},t_{k})\in
E\end{subarray}}{\sum\limits_{\begin{subarray}{c}(t_{i},m_{j},t_{s})\not\in
E\end{subarray}}\left[\gamma-f(te_{i},me_{j},te_{k})+f(te_{i},me_{j},te_{s})\right]_{+}}$
(7)

where $\gamma$ is a training score threshold which represents the differences
between the score of correct links and the incorrect predicted links. The loss
$\mathcal{L}$ is minimized with the ADAM[48] optimizer, a variant of gradient
descent.

Figure 4: Neural Architecture for Twitter Misinformation Detection as a Binary
Classification with BERT (TMD-BC-BERT).

###  4.3 Misinformation Detection as Neural Classification

Since most existing systems that tackle misinformation detection are binary
classifiers, we also generated a baseline binary classification system that
can operate on the same data as the TMD-GLP system. Therefore we designed a
simple neural architecture, following prior work [49], which directly
classifies tweets as containing or not containing the misinformation expressed
(explicitly or implicitly) in one of the MisTs encoded in the MKG. This
classification uses the text of a tweet along with the description of a MisT
to perform sequence-pair classification. This simple neural architecture,
which we call Twitter Misinformation Detection as Binary Classification with
BERT (TMD-BC-BERT), is illustrated in Figure 4.

Joint Word-Piece Tokenization is performed for both a MisT $m_{j}$ and a tweet
$t_{i}$. This produces a single sequence of word-piece tokens for both the
misinformation target and the tweet, with the misinformation target text and
the tweet text separated by a special $[SEP]$ token. The beginning $[CLS]$
token and end $[SEP]$ token are placed at the beginning and end of the joint
sequence respectively, as is done in prior work [43] with multiple BERT
sequences. These tokens are provided to COVID-Twitter-BERT-v2, which produces
contextualized embeddings for each word-piece token. A single contextualized
embedding $r^{i,j}$ represents the entire sequence of tokens from MisT $m_{j}$
and from $t_{i}$, which is the first contextualized embedding $rc_{1}^{i,j}$,
representing the initial $[CLS]^{j}$ token embedding. This embedding is
provided to a fully-connected layer with a softmax activation function which
outputs a probability distribution over $P(Misinformation|t_{i},m_{j})$. As
Figure 4 shows, misinformation is recognized when the probability is larger
than a predefined threshold. In our experiments, the value of the threshold
$T$ was determined on the development data to be $0.9995$. The TMD-BC-BERT
system is trained to classify tweets that contain misinformation concerning a
given MisT, while using the same training data that was used for training the
TMD-GLP system. In addition, the TMD-BC-BERT system was trained end-to-end
using the cross-entropy loss function:

$\mathcal{L}=-\sum_{(r,t_{i},m_{j})\in D}{log{P(r|t_{i},m_{j})}}$ (8)

where $r\in\\{Misinformation,NO\\_Misinformation\\}$ and $D$ is a set of all
training tweets judged to contain (or not contain) misinformation addressed by
a certain MisT of interest. The loss $\mathcal{L}$ is minimized with ADAM
[48], a variant of gradient descent.

We also compare the TMD-GLP system against a system implemented to use Long
Short-Term Memory (LSTM) [50] cells instead of COVID-Twitter-BERT-v2 in the
architecture illustrated in Figure 4. Specifically, we use $2$ layers of Bi-
LSTMs [51] of size $1024$. We call this baseline system TDM-BC-LSTM.

##  5 Experimental Results

Because we believe that it is critical to detect misinformation about COVID-19
vaccines only in tweets that are truly relevant to the MisTs of interest, we
first conducted experiments to evaluate the quality of retrieval and then we
separately evaluated the quality of misinformation detection.

###  5.1 Evaluation of the Tweets Potentially Containing Misinformation about
COVID-19 Vaccines

Retrieval System | Judged Tweet Count |  |  | Percent of Tweets
---|---|---|---|---
| _Relevant_ | _Not_Relevant_ | Total |  Judged _Relevant_
BM25 [25] | 1,979 | 2,174 | 4,153 | 47.7%
BERTScore (DA) [36] | 1,475 | 3,214 | 4,689 | 31.5%
Total | 3,075 | 4,171 | 7,246 | 42.4%
Table 2: Distribution of human judgements of tweet relevance against
Misinformation Targets (MisTs).

The evaluation of relevant tweets for each MisT from our CoVaxLies dataset was
performed by considering two retrieval systems: (1) one using the BM25 scoring
function, and (2) one using the BERTScore (DA). The methods used by these
systems was described in Section 3.3. In order to conduct the retrieval
evaluations, each MisT was used to formulate a query, processed by both
retrieval systems. Out of the 753,017 unique tweets from $\cal{C}_{T}$ that we
have collected from the Twitter API, the retrieval system using BM25 returned
4,153 tweets it deemed relevant to the 17 MisTs of interest. In contrast, the
retrieval system based on BERTScore returned 4,689 tweets that it deemed
relevant. The difference in number of tweets deemed relevant by each system
can be explained by how each system treated the replacement of the term
“COVID-19” with “coronavirus” in the query. The retrieval system using the
BM25 scoring function was much less sensitive to the query change, returning
only few more than 200 tweets deemed relevant (on average 244 relevant tweets
per MisT), while the system using BERTScore returned slightly more tweets
deemed relevant when the query was modified (on average 276 relevant tweets
per MisT).

Figure 5: Number of tweets judged relevant or non-relevant by human experts
for MisTs of interest. MisTs are presented in descending order of their number
of tweets judged relevant.

Human judgements of the relevance of both retrieval systems were performed on
the unique tweets from the $\cal{T_{R}}$ collection first mentioned in Section
3.2. $\cal{T_{R}}$ contains 7,246 tweets deemed relevant to our MisTs of
interest by at least one of the retrieval systems. Each human judgement has
established the relevance or non-relevance of each tweet against the MisTs of
interest. Three natural language experts participated in the judgements of
relevance. Their inter-annotator agreement was discussed in Section 3.2. Table
2 lists the judgement results on the $\cal{T_{R}}$ collection. We see that the
human judges have found a similar number of MisT-_Relevant_ tweets from the
results returned by the retrieval system using the BM25 scoring function when
compared to the retrieval system using BERTScore (DA) (1,979 vs 1,475), while
the number of retrieved tweets judged non-relevant by the system using
BERTScore (DA) is significantly larger than the number corresponding to the
tweets judged as _Not_Relevant_ from the tweets retrieved by the system using
the BM25 scoring function (3,214 vs 2,174). This indicates, as shown in Table
2 that the percent of tweets deemed relevant by the system using the BM25
scoring function and also judged relevant by human experts is much higher than
the same percent of tweets deemed relevant by the system using BERTScore.
Therefore, retrieving tweets with the system that uses the BM25 scoring
function is far better than using a retrieval system informed by BERTScore.

To provide additional details, Figure 5 illustrates the total number of tweets
judged _Relevant_ and _Not_Relevant_ for each MisT. As shown in the Figure,
for some MisTs, such as MisT7, MisT9, or MisT4, the majority of the tweets
retrieved using both retrieval systems were judged relevant. In contrast, for
other MisTs, the results of the retrieval systems were judged mostly
_Not_Relevant_ , e.g. for MisT5, MisT14, MisT13, or MisT16.

###  5.2 Evaluation of Detecting Misinformation about COVID-19 Vaccines

Evaluation was performed by considering that misinformation detection was cast
as multi-label binary classification. This means that multiple MisTs could be
predicted for every tweet $t_{i}$ from the test collection of CoVaxLies, some
correct and others not. Predictions of graph links of a tweet $t_{i}$ to
various MisTs were compared against the MisTs with which the tweet $t_{i}$ was
paired in the test collection of CoVaxLies. For example, if the test
collection contained only one pair in which $t_{i}$ was associated with one
MisT, e.g. [$t_{i}$, MisTa], then during evaluation, we compared MisTa against
the MisTs predicted for $t_{i}$. If MisTa was predicted, then that prediction
was correct and counted as a true positive ($tp$). If MisTa was not predicted,
then that prediction was incorrect and counted as a false negative ($fn$). If
other MisTs were predicted which were different from MisTa, then each of those
predictions were incorrect and counted as a false positive ($fp$).

In a similar way we have evaluated the prediction of graph links for tweets
that were paired with multiple MisTs in the testing data. For example, if the
test collection contains a tweet $t_{i}$ paired with $k$ MisTs, e.g. [$t_{i}$,
MisT1], [$t_{i}$, MisT2], …, [$t_{i}$, MisTk], then during evaluation, we
compared these MisTs against the MisTs predicted for $t_{i}$. Each predicted
MisT which coincides with one of the $k$ MisTs paired with $t_{i}$ in the test
collection is counted as a true positive. If any of the $k$ MisTs paired with
$t_{i}$ was not predicted, then it is counted as a false negative. Any
predicted MisT which was different from the $k$ MisTs paired with $t_{i}$ in
the test collection counted as a false positive. The evaluation metrics of
Precision and Recall using the true positives, false positives, and false
negatives were computed as:

$Precision=\frac{tp}{tp+fp};Recall=\frac{tp}{tp+fn};$ (9)

Because the number of paired MisTs varies across tweets in the testing
collection, this learning task could not be cast as a multi-class
classification problem, but rather as a multi-label binary classification
problem. System performance was evaluated using Micro averaged Precision (P),
Recall (R) and F1 111F1 is defined as $F_{1}=2\times P\times R/(P+R)$ score.
The evaluation results are provided in Table 3.

When evaluating the Twitter Misinformation Detection as Graph Link Prediction
(TMD-GLP) system, we have considered (a) four possible knowledge embedding
models (TransE; TransD; TuckER; and TransMS), which provide different graph-
link scoring functions; and (b) two possible options of selecting a tweet from
a FCG corresponding to a MisT to which an unconnected tweet could be linked.
In option 1: we select all tweets connected to the FCG, while in option 2: we
select a prototypical tweet of the FCG. We also evaluate the KNN baseline
configuration of the TM-GLP system, which does not consider edge types between
connected tweets in the _Misinformation Knowledge Graph_. Hence, we have
evaluated nine different configurations of the TMD-GLP system. In addition, we
have evaluated the system for Twitter Misinformation Detection as Binary
Classification, with both BERT (TM-BC-BERT) and LSTM cells (TMD-BC-LSTM). The
evaluation results of all these configurations of the TMD-GLP system are
listed in Table 3 along with the results of the TMD-BC-BERT and TM-BC-LSTM
systems. The bolded numbers represent the best results obtained across all
systems.

Misinformation Detection | Micro |  |
---|---|---|---
System | Precision | Recall |  F1-Score
BM25-BC [3] | 42.9 | 63.7 | 51.2
BERTScore(DA)-BC [3] | 25.9 | 32.9 | 29.0
TMD-BC-LSTM | 47.6 | 64.8 | 54.9
TMD-BC-BERT | 68.9 | 86.7 | 76.8
TMD-GLP |  |  |
\+ KNN | 80.2 | 82.2 | 81.2
\+ TransE-All | 77.8 | 87.5 | 82.4
\+ TransE-Prototypical | 77.0 | 85.7 | 81.1
\+ TransD-All | 71.9 | 84.8 | 77.8
\+ TransD-Prototypical | 68.8 | 88.3 | 77.4
\+ TuckER-All | 64.0 | 83.8 | 72.6
\+ TuckER-Prototypical | 61.5 | 85.4 | 71.5
\+ TransMS-All | 82.9 | 85.4 | 84.1
\+ TransMS-Prototypical | 82.4 | 86.4 | 84.3
Table 3: Evaluation results for misinformation detection experiments performed
on the test collection from the CoVaxLies dataset described in Section 3.3.

As shown in Table 3, the TMD-BC-LSTM and TMD-BC-BERT systems had higher
Precision, Recall, F1 scores than the BM25-BC and BERTScore(DA)-BC systems,
demonstrating the importance of fine-tuning neural misinformation detection
systems. The TMD-BC-BERT system significantly outperformed the TMD-BC-LSTM
system, yielding a Micro F1 score of $76.8$, which is superior to the Micro F1
score of the TMD-BC-LSTM system of $54.9$, but the F1 results of the TMD-BC-
BERT system were lower than 7 out of 9 configurations of the TMD-GLP system.
The only configurations of the TMD-GLP system under-performing the TMD-BC-BERT
system used TuckER as the knowledge embedding model. The TMD-GLP with the KNN
baseline configuration outperformed TMD-BC-BERT, with a Micro F1 score of
$81.2$, which also performed better than 5 out of the 8 TMD-GLP configurations
which considered MisTs as edge types in the _Misinformation Knowledge Graph_.
From the evaluation results obtained for the TMD-GLP system we can see that
when selecting the prototypical tweet of the FCG of a MisT, the results were
inferior to those obtained when selecting any tweet from the FCG, with the
exception of the system configuration using the TransMS knowledge embedding
model. In fact, the best overall results for misinformation detection were
obtained when graph link prediction was used, informed by the scoring function
of the TransMS knowledge embedding system, and when selecting the prototypical
tweet of each MisT-specific FCG. For this configuration, the TMD-GLP system
produced a Micro F1 score of $84.3$. This can be explained because the TransMS
knowledge embedding model accounts for the most interactions between nodes and
edges in its scoring function, seen in Equation 4, while also best modeling
symmetric relationships, which we often see in our _Misinformation Knowledge
Graph_ (MKG). This may also explain why the TransMS-All configuration of the
TMD-GLP system generated the best overall Precision score.

Additionally, two baselines for Detecting Misinformation about COVID-19
Vaccines were considered, both assuming that relevance retrieval is
sufficient. These systems follow prior work [3] and cast misinformation
detection as misinformation retrieval, by considering each tweet as a query
and returning the most relevant MisTs. We acknowledge that this retrieval
framework is atypical because the collection of MisTs is several orders of
magnitude smaller than the number of tweets from the test collection of
CoVaxLies, whereas retrieval systems typically work on an index which is much
larger than the number of queries. But since this is the framework for
retrieval that was considered in [3], we adopted the same framework for these
baselines. In our experiments, the retrieval system using the BM25-BC model
returned for each tweet the most relevant MisTs, and we considered that MisTs
with a relevance score above a pre-defined threshold $T$ are predicted as
linked to the tweet. The BERTScore(DA)-BC baseline compares the text of each
tweet against the text of each MisT using the BERTScore (DA) relevance model.
BERTScore (DA) assigns a relevance score to each MisT for each tweet, and
MisTs with a relevance score above a pre-defined threshold $T$ are predicted
as relevant. The threshold $T$ for both systems is selected by maximizing F1
score on the development set.

For this purpose, we have evaluated on the test collection of CoVaxLies the
retrieval systems using the (1) BM25 scoring function, used for Binary
Classification (BM25-BC) or; (2) the BERTScore(DA) scoring function, used for
Binary Classification (BERTScore(DA)-BC). The retrieval system using the BM25
score produced a Micro F1 score of $51.2$, setting a baseline expectation of
performance. This performance can be attributed to the large amount of shared
terminology between MisTs and _Relevant_ tweets, which benefits the BM25
scoring function. The retrieval system using BERTScore (DA) produced a Micro
F1 score of $29.0$, which was much lower than expected, when comparing with
results published in prior work [3].

The results of the evaluations listed in Table 3 are interesting, as they
generally indicate that casting misinformation detection as graph-link
prediction, informed by knowledge embedding models such as TransE, TransD, and
TransMS, can generate promising results, superior to the results obtained when
considering misinformation detection as a multi-label binary classification
problem, as most current systems do. We can also notice that TransE, a much
simpler knowledge embedding model, outperformed TransD and TuckER, and was
competitive with TransMS in their impact on misinformation detection. This
also indicates that some advancements in knowledge embedding models do not
necessarily result in improved performance on certain tasks such as
misinformation detection.

System hyper-parameters were selected by maximizing the F1 score of each
system on the development collection. The TMD-BC-BERT and TMD-GLP systems
share many of the same training hyper-parameters: a linearly decayed learning
rate of $5e-4$ which was warmed up over the first $10\%$ of the $40$ total
epochs, an attention drop-out rate of $10\%$, a batch size of $6$, and a
gradient norm clipping of $1.0$. The TMD-BC-LSTM system has $2$ layers of
bidirectional LSTM cells with a size of $1024$, a linearly decayed learning
rate of $5e-2$ which was warmed up over the first $10\%$ of the $40$ total
epochs, a batch size of $6$, and a gradient norm clipping of $1.0$. The TMD-
GLP system has different knowledge embedding hyper-parameters depending on the
knowledge embedding model selected. The tweet knowledge embedding size $z$ was
set to $8$ for all knowledge embedding models except for TransD, which used
$16$ such that both the split knowledge embeddings have size $8$. The MisT
knowledge embedding size $v$ was set to $8$ for all knowledge embedding models
except for TransD, which used a knowledge embedding size of $16$ for the same
reason as above, and TransMS, which used a knowledge embedding size of $9$ to
ensure the MisT knowledge embedding vectors were size $8$ when considering the
extra $\alpha_{j}$ parameter. Initial experiments on the development
collection of CoVaxLies produced the best results for embedding size of 8. We
experimented with embedding sizes of 4, 8, 16, 32, 64, and 128, and found that
performance was, on average, 2-4 F1 points lower on the development set as the
size increased above 8 when using the TransMS-Prototypical configuration. We
observed the same decrease in results for the TransE-All configuration.
Embeddings of size 4 produced a 6-point F1 score drop on the development set
for the TransMS-Prototypical configuration. Therefore we decided that we would
use embedding sizes of 8 for all test collection experiments. Our intuition is
that the relatively simple FCGs of the misinformation knowledge graph likely
do not need larger embeddings to encode the necessary graph link prediction,
but cannot encode that information in embeddings of size 4.

The TMD-GLP system utilizes the training collection for learning to perform
graph-link prediction by optimizing the margin loss, described in Equation 7.
The $\gamma$ hyper-parameter is set to $1.0$ for all knowledge graph embedding
models, and we sample $1$ negative corrupted link for each labeled link.
Threshold values $N_{x}$ and $T_{x}$ were also automatically selected by
maximizing the F1 score of the TMD-GLP system on each MisTx on the development
collection.

###  5.3 Discussion of the Identification of Tweets Potentially Containing
Misinformation about COVID-19 Vaccines

Collecting tweets for CoVaxLies and judging their relevance against each MisT
revealed a large discrepancy between retrieval performance of the retrieval
systems using the BM25 scoring function or BERTScore. Prior work [3] on the
COVIDLies dataset found that retrieval using BERTScore performed better than
retrieval using the BM25 scoring function for misinformation detection, but
the entire COVIDLies dataset was collected using only BERTScore. Their
collection methodology resulted in a judged _Relevant_ tweet percentage of
$14.98\%$, meaning only $14.98\%$ of their discovered tweets were judged
_Relevant_ by annotators. We found a similar _Relevant_ tweet percentage of
$31.5\%$ on our collection when only retrieving tweets using the BERTScore
(DA), but simultaneously identified that the retrieval system using the BM25
scoring function generated a relevant percentage of $47.7\%$, presented in
Table 2. This large difference indicates that there are many instances of
tweets containing misinformation which are not discovered by the retrieval
system using BERTScore, and that the retrieval system using the BM25 scoring
function is actually better at discovering more _Relevant_ tweets for each
MisT. We also analyzed the differences in total tweets: the retrieval system
using the BM25 scoring function returned a total of $4,153$ tweets, while the
retrieval system using BERTScore (DA) returned $4,689$ tweets. This difference
in the number of retrieved tweets arises from the sensitivity of each system
to changes to the query: We found that the retrieval system using BERTScore to
be more sensitive to replacing “COVID-19” with “coronavirus” when querying the
CoVaxLies dataset for each MisT. The retrieval system using BERTScore produced
more disjoint retrieved lists of tweets for the original and the modified
query, while when the retrieval system using the BM25 scoring function was
used, there was much more overlap. But, when using the retrieval system
informed by BERTScore, we discovered that most of these additional non-
overlapping tweets were judged as _Not_Relevant_.

Advantages remain to using BERTScore, as the _Relevant_ tweets it finds are
characterized by less term overlap with the language used to describe the
MisTs, and, thus it emphasizes more semantic relevancy. However, ignoring
classical scoring functions such as BM25 leads to a heavily biased dataset of
tweets that potentially contain misinformation, which may very well lead to
misinformation topic shift, such that the tweets are deemed relevant, when in
fact they share more semantics with other topics than the one we try to
collect, namely misinformation about COVID-19 vaccines. As provided in Table
3, performance of the misinformation detection systems using the BM25 scoring
function and BERTScore (DA) on misinformation detection on our CoVaxLies
dataset was significantly different. The BM25-BC system scored much higher on
all metrics than BERTScore(DA)-BC, which was the opposite conclusion drawn
from prior work [3] on the COVIDLies dataset. We hypothesized that this
difference was due to the data collection methodology utilized to create the
COVIDLies dataset: The only retrieval system used to find tweets for each MisT
was BERTScore, therefore the _Relevant_ annotations would be biased towards a
BERTScore-based model. This would naturally lead the misinformation detection
system using the BM25 scoring function to perform worse than a BERTScore-based
model in the evaluation of misinformation detection. To test this hypothesis,
we modified our data collection to only include annotated tweets which were
discovered by BERTScore (DA) during the retrieval of relevant tweets for each
MisT. We re-ran both the BERTScore(DA)-BC and BM25-BC misinformation detection
systems on this modified collection of CoVaxLies and report the results in
Table 4. BM25-BC produces a F1 score of $32.0$, while BERTScore(DA)-BC
produces an F1 score of $39.2$. This experiment demonstrates the flaw of
collecting data using only BERTScore (DA): The collection will be heavily
biased towards _Relevant_ tweets which BERTScore (DA) alone would discover.
Simultaneously, _Relevant_ tweets would be missed by BERTScore but would have
been discovered by other systems such as BM25. This phenomenon can lead to
misleading conclusions during system evaluation.

##  6 Discussion

Misinformation | Micro |  |
---|---|---|---
Detection System | Precision | Recall |  F1-Score
BM25-BC | 19.7 | 85.5 | 32.0
BERTScore(DA)-BC | 29.9 | 57.1 | 39.2
Table 4: Evaluation results of misinformation detection on tweets from the
CoVaxLies dataset discovered by BERTScore(DA).

###  6.1 Assessing the Quality of Misinformation Detection

The experimental results, provided in Table 3, indicate that detecting
misinformation using the TMD-GLP system, that benefits from graph link
prediction, improves upon baseline systems using only retrieval scoring or
neural classification approaches. The TransE knowledge embedding model had a
better impact than the TransD or TuckER knowledge embedding models on the TMD-
GLP system, although it is a much older and simpler model. We believe this is
due to the fact that knowledge embedding models have been rarely tested in
extrinsic evaluations, in which they are used for other applications than
knowledge representation per se. Our goal was not to model a massive, multi-
relational knowledge graph with complicated hierarchies and directed edges.
Our Misinformation Knowledge Graph (MKG) is relatively simple, containing a
Fully Connected Graph for each MisT, and therefore benefiting from a simpler
knowledge embedding model, such as TransE. We also see this pattern hold for
the KNN baseline configuration, where we entirely ignore the MisT edge
embeddings in our MKG. But, TransMS, an improved knowledge graph embedding
model, did improve upon TransE and KNN in our misinformation detection
framework. We believe TransMS performed best for two reasons:

  * •

The scoring function for TransMS, provided in Equation 4, is insensitive to
the direction of the relationship. Symmetric relationships, which are the only
kind of relationships in our MKG, benefit from this property.

  * •

Non-linear interactions in the scoring function for TransMS, in the form of
the $tanh$ operations, provide more rich knowledge embeddings than the linear,
multiplicative, and dynamic projection approaches of TransE, TuckER, and
TransD respectively.

Misinformation | Micro |  |
---|---|---|---
Detection System | P | R |  F1
BM25-BC | 15.8 | 24.1 | 19.1
BERTScore(DA)-BC | 13.3 | 22.2 | 16.6
TMD-BC-BERT | 21.8 | 54.1 | 31.1
TMD-GLP |  |  |
\+ TransMS-Prototypical | 29.4 | 79.7 | 40.7
Table 5: Results from 5-fold cross validation on the COVIDLies dataset for misinformation detection. ID | Precision | Recall |  F1-Score |  nx
---|---|---|---|---
1 | 87.9 | 76.3 | 81.7 | 13
2 | 92.9 | 100.0 | 96.3 | 20
3 | 76.9 | 87.0 | 81.6 | 26
4 | 94.5 | 100.0 | 97.2 | 24
5 | 79.3 | 95.8 | 86.8 | 6
6 | 100.0 | 100.0 | 100.0 | 24
7 | 97.1 | 84.6 | 90.4 | 20
8 | 85.3 | 55.8 | 67.4 | 19
9 | 63.4 | 88.1 | 73.8 | 27
10 | 71.8 | 80.0 | 75.7 | 15
11 | 80.6 | 86.2 | 83.3 | 14
12 | 81.8 | 75.0 | 78.3 | 12
13 | 0.0 | 0.0 | 0.0 | 2
14 | 43.5 | 90.9 | 58.8 | 4
15 | 91.4 | 97.0 | 94.1 | 7
16 | 44.4 | 100.0 | 61.5 | 1
17 | 66.7 | 76.9 | 71.4 | 11
Table 6: Misinformation detection performance of TMD-GLP with the TransMS-Prototypical configuration for each MisT in the test collection. nx is the number of nodes in the FCG for MisTx Judged MisT |  Tweet | BM25-BC | TMD-BC-BERT | TMD-GLP
---|---|---|---|---
1: RNA alters a person’s DNA when taking the COVID-19 vaccine. |  The Covid vaccine is not a vaccine. It’s gene therapy making genetically modified humans | ✗ | ✓ | ✓
17: Vaccines contain unsafe toxins such as formaldehyde, mercury or aluminum. |  Covid vaccine: think 5x over currently. You cannot detox from this. You will be inserted with nano technology. Nano lipids. You’ll become a human antenna with the aluminum encased in the nano lipids. They are basically impossible to remove once they are in. | ✗ | ✗ | ✓
13: The COVID-19 vaccine can increase risk for other illnesses. |  The covid vaccine is a one way ticket to cancer and dementia. Remember that. Don’t vaccinate. | ✗ | ✗ | ✗
Table 7: Tweets in which MisTs are not correctly detected by BM25-BC, TMD-BC-
BERT, and TMD-GLP with the TransMS-Prototypical configuration.

Additionally, we observed that TransMS was the only knowledge embedding model
which performed better when selecting a prototypical tweet from a MisT-
informed FCG (as opposed to considering all the tweets of the FCG) to predict
a link to an unconnected tweet. This can be explained by the observation that
a prototypical tweet is represented as the average knowledge embedding of all
tweets from the same MisT-informed FCG, which can create a more “coherent”
representation of the MisT, but may damage some of the specific structure of
the knowledge embeddings for each tweet in the MisT-informed FCG needed for
link prediction. In contrast, when all the tweets of the MisT-informed FCG are
considered, some of their knowledge embeddings may be less of a “coherent”
representation of the MisT, but the stronger specific knowledge embedding
structure remains for link prediction. Thus, the Prototypical approach likely
reduces performance when the gains in “coherence” are outweighed by losses to
knowledge embedding structure, as we see when using the TransE, TransD, or
TuckER knowledge embedding models.

###  6.2 Portability of Misinformation Detection as Graph Link Prediction to
Other Datasets

In order to assess the portability of our approach to detecting misinformation
as graph link detection to other datasets, we have considered the COVIDLies
dataset [3]. The COVIDLies collection consists of 5,748 tweets annotated with
stance towards COVID-19 pandemic misinformation targets, with stance values of
_“Agree”_ , _“Disagree”_ , and _“No Stance”_. However, the COVIDLies
annotators made no distinction between tweets which contained a neutral _“No
Stance”_ towards a MisT and tweets which were not relevant to a MisT, which
were also annotated as _“No Stance”_. To resolve this problem, we categorized
tweets labeled as _“Agree”_ or _“Disagree”_ with a MisT as _“Relevant”_ to the
MisT, while all tweets with _“No Stance”_ against a Mist were considered _“Not
Relevant”_ to that MisT. This re-annotation of COVIDLies in terms of relevance
of tweets towards various MisTs led to having only 17% of the unique tweets
from COVIDLies as relevant to one or more MisTs. In comparison, in the
CoVaxLies collection, 57% of the unique tweets are relevant to one or more
MisTs.

The portability of the system for Twitter Misinformation Detection as Graph
Link Prediction (TMD-GLP) on the COVIDLies collection also entails the
availability of training, development and test sets on this collection.
However, COVIDLies has no official training or testing collections. Therefore,
we split COVIDLies into 5 evenly distributed folds, assigning three folds for
training, one fold for development and the fifth fold for testing. This
process was performed five times, enabling the evaluation of the results of
TMD-GLP on COVIDLies. Results of the evaluations, when considering the
configuration of the TMD-GLP that produced the best results on the CoVaxLies
dataset, namely when using the TransMS knowledge embedding model with a
prototypical representation for the FCG of each MisT, i.e. TMD-GLP+TransMS-
Prototypical, are listed in Table 5. We also ported on COVIDLies the system
for Twitter Misinformation Detection as Binary Classification with BERT (TMD-
BC-BERT), evaluating it on the same 5-fold cross validation as we did with the
TMD-GLP+TransMS-Prototypical system.

Table 5 shows that even on COVIDLies, the TMD-GLP+TransMS-Prototypical system
performs best, obtaining an F1 score of $40.7$. However, a major performance
drop is observed between the performance of this system from its operation on
the CoVaxLies dataset to its operation on the COVIDLies dataset, namely a drop
from an F1 score of $84.3$ to an F1 score of $40.7$. This drop can be
explained when the dataset statistics are considered. Because the COVIDLies
collection has a smaller percentage of relevant tweets than the CoVaxLies
collection, it will lead to the discovery of a _Misinformation Knowledge
Graph_ containing smaller FCGs for each MisT than those discovered when using
the CoVaxLies dataset. This entails that it becomes harder to predict a
correct graph link, when the FCGs are smaller. We can notice that both TMD-BC-
BERT and TMD-GLP systems suffer from this major reduction in relevant tweets
in the CoVaxLies collection, as the results listed in Table 5 show that both
systems generate much better Recall results than Precision results, which are
quite low. This low Precision is likely due to such a small number of relevant
tweets for each MisT in COVIDLies, with many MisTs having only one or two
relevant tweets from which to learn to predict true positive links, leading to
many more false positive links. But, the evaluation results listed in Table 5
showcase the portability of the TM-GLP method on a second dataset, while also
highlighting the limitations of the COVIDLies dataset.

###  6.3 Discussion of Detailed Results and Error Analysis

Detailed performance of the TMD-GLP system using the TransMS-Prototypical
configuration is provided for each MisT in Table 6. We also include the size
$n_{x}$ of the FCG for each MisT. The TMD-GLP+TransMS-Prototypical
misinformation detection system performed best on MisT2: “The COVID-19 vaccine
causes infertility or miscarriages in women.”; MisT4: “The COVID-19 vaccine
causes Bell’s palsy.”, and MisT6: “The COVID-19 vaccine contains tissue from
aborted fetuses.”. These MisTs made easily identifiable claims, such as
“causing infertility”, “causing Bell’s palsy”, and “containing aborted fetus
tissue”. Statements supporting, refuting, or reporting on these claims were
very easy to detect. For example, the following is a tweet referring to MisT2:
“@JoPatWar @allen40_allen @Telegraph The Pfizer CEO said that the coronavirus
vaccine can make women infertile. The COVID-19 vaccine causes infertility or
miscarriages in women.” , where the claim articulated in MisT2 is underlined.
MisT2, MisT4, and MisT6 also all inform relatively large FCGs, with $n_{x}\geq
20$, providing a significant sample of tweets to compute prototypical
embeddings. Additionally, Figure 5 demonstrates that MisT2, MisT4, and MisT6
are among the upper half of MisTs which had the most _Relevant_ tweets judged,
meaning they had more relevant data during training than other MisTs.

The TMD-GLP+TransMS-Prototypical misinformation detection system performed
worst on MisT13: “The COVID-19 vaccine can increase risk for other
illnesses.”; MisT14: “Many people already have died from the COVID-19 vaccine
trials.”; and MisT16: “The COVID-19 vaccine should not be taken by people who
are allergic to eggs.”. Misinformation claims made by these MisTs were much
more difficult to identify because the articulation of the claims was either
too vague or too specific. MisT13 contains the vague statement “can increase
the risk for other illnesses”, which was much more vague than the claims made
in other MisTs, such as MisT4: “The COVID-19 vaccine causes Bell’s palsy.” In
contrast, MisT16 focuses on specifically “egg allergies”, which was difficult
to find on Twitter, as most discussions surrounded other specific allergies.
MisT13, MisT14, and MisT16 also all inform relatively small FCGs, with
$n_{x}\leq 4$, providing too small a sample of tweets to compute prototypical
embeddings. Additionally, Figure 5 demonstrates that MisT13, MisT14, and
MisT16 are the three MisTs with the fewest _Relevant_ tweets judged, meaning
they had less relevant data during training than other MisTs.

Table 7 lists tweets which were judged to refer to some MisT of interest, but
the misinformation detection systems that we evaluated failed to identify. The
first tweet listed in Table 7 was judged to refer to MisT1 (also listed in the
Table). The TMD-BC-BERT system as well as the TMD-GLP system with the TransMS-
Prototypical configuration were able to detect the connection to this MisT,
while the baseline using the BM25 scoring function failed to accomplish this
task. A lack of exact term overlap between the tweet text and the MisT text
explains why a term-based Lucene index, utilized by the BM25 system, along
with a BM25 scoring function would be unlikely to discover this
misinformation. The second tweet from Table 7 was judged to refer to MisT17.
The TMD-GLP system with the TransMS-Prototypical configuration was the only
system able to identify the reference to this MisT. The TMD-GLP system
compared the knowledge graph embedding of the tweet with the knowledge
embedding obtained for other tweets connected to the same MisT-informed FCG,
such as “@FayCortez @annaedney @LauermanJohn @business Because the people who
created the deadly, depopulating Covid vaccine are part of the same contingent
of Planners who have been spraying you with aluminum, barium, and strontium
via chemtrails to intentionally increase your chances of getting Alzheimer’s.”
The TMD-GLP system was able to identify that the second tweet listed in Table
7 likely refers to the same MisT as this tweet, and therefore correctly
detected the misinformation. The third tweet listed in Table 7 was judged to
refer to MisT13. None of the systems that we evaluated were able to identify
that this tweet referred to MisT13. There is little term overlap or contextual
clues to indicate that this tweet would be related to MisT13, which explains
why the TMD-BC-BERT systems as well as the system using the BM25 scoring
function failed. To determine why the TMD-GLP system failed, we can look at
the FCG informed by MisT13 and recognize the following: Table 6 states that
there were only two tweets in the FGC for MisT13, and upon further inspection
we see that these two tweets only mention the COVID-19 vaccine increasing the
risk of “cancer”, “heart disease”, and “HIV”. The third tweet discusses
“cancer”, but it also proposes the COVID-19 vaccine is “a one way ticket to”
the illness “dementia”. The fact that “cancer” or “dementia” were not
recognized as instances of “illness”, because no clinical language processing
was applied on the content of the tweets or of the description of the MisT13
explains why the third tweet was not linked to MisT13 by any of the systems
that we have evaluated.

###  6.4 Limitations

There are several important limitations to our study. The first limitation
originates in the fact that we aim to discover tweets that discuss or refer
only to known misinformation targets. When additional misinformation targets
become known, new relevant tweets must be retrieved, judged if they discuss
information that is relevant to the new targeted misinformation, or not, and
then enable the creation of new training, development and testing data for
identifying additional tweets that discuss the new targeted misinformation.
The recognition of new, yet unknown misinformation is not withing the scope of
this study. Even if this may seem a major limitation, it is a significant
departure from most previous methods, discussed in Section 2, that detect only
if there is some misinformation (or rumor) in a tweet, but fail to recognize
what kind of misinformation is discussed or referred.

An additional limitation can be found in the use of specific search keywords,
such as “covid” or “coronavirus”. More search terms, such as “corona”, “vax”,
or “jab” may be used more often by the general public, and should be
considered in future studies. These terms might reveal new misinformation, or
may be used more often by users which tweet about known misinformation.

Another limitation of the study is determined by the fact that we decided not
to consider the stance of the tweets against the misinformation targets. We
only recognize if the information shared by a tweet is relevant to a MisT, but
do not recognize if it agrees or disagrees with the predication of the MisT,
or if it has no stance at all. We believe that stance detection is a separate
task, which can be performed only on the tweets that are known to be relevant
to a MisT. Previous work [3], [24] showed that identifying the tweet stance
towards a MisT benefits from knowing that the tweet is discussing information
relevant to the MisT. In future work we plan to address the problem of
recognizing tweets that are relevant to new misinformation targets, casting
the problem as a zero-shot learning problem.

##  7 Conclusion

It has been estimated that the COVID-19 vaccines will need to be accepted by
at least 55% of the population to provide herd immunity, with estimates
reaching as high as 85% depending on country and infection rate [52]. Reaching
these required vaccination levels is hindered by vaccine hesitancy across the
world [53], which is often fuelled by misinformation spreading on social
media. Therefore, it is important to know which misinformation targets are
used, and in which tweets and by which authors, such that people can be
inoculated against COVID-19 vaccine misinformation before they are exposed to
it. Moreover, because Twitter is the social media platform where most of the
conversations about COVID-19 vaccines take place, it is essential to discover
automatically tweets that spread misinformation, such that vaccine hesitancy
interventions can be delivered to those participating in misinformed
conversations. In this paper we present CoVaxLies, a corpus of 7,246 tweets
judged by language experts to refer to 17 different targets of misinformation
about COVID-19 vaccines. The CoVaxLies dataset was created using a methodology
similar to the one used in the generation of the COVIDLies [3] dataset of
tweets, which annotated misinformation about COVID-19. Therefore, they can be
used together for learning to identify misinformation about COVID-19 and
COVID-19 vaccines. Both COVIDLies and CoVaxLies are evolving, as additional
targets of misinformation are added and tweets relevant to them are retrieved
and judged by experts. This paper has also explored the need to retrieve
tweets relevant to misinformation targets using a combination of retrieval
systems, concluding that in this way a larger set of truly relevant tweets are
discovered, and can be included in these datasets.

In this paper, CoVaxLies was used to train and evaluate a novel, simple and
elegant method for discovering misinformation on Twitter, relying on graph
link prediction. This method is enabled by (a) the organization of a
Misinformation Knowledge Graph and (b) the availability of link scoring
functions from several knowledge embedding models. Our experiments have shown
that superior results can be obtained when discovering misinformation using
graph link prediction, as compared with neural classification-based methods,
yielding an increase of up to 10% F1-score. The method presented in this paper
does not consider the conversation threads, as many recent misinformation
detection methods do, e.g. [16], [14]. But CoVaxLies will be extended to
account for entire conversation threads on Twitter, allowing us to extend the
methodology of misinformation detection presented in this paper, and to
evaluate the impact conversations have on the quality of misinformation
detection, when misinformation targets are also considered, an important
aspect that is currently ignored.

Our future work will also consider the discovery of adoption or rejection of
misinformation about COVID-19 vaccines. This will be achieved by relying on
the automatic inference of the stance of tweets relative to the misinformation
targets. This will allow us to expand our work reported in [24], where we
developed a neural architecture that combined the role of semantic, lexical
and affect characteristics of language with the taxonomy of concerns raised by
misinformation targets. Along with automatically detecting misinformation
about COVID-19 vaccines, the recognition of the adoption or rejection of that
misinformation will be a stepping stone in the direction of developing
misinformation inoculation interventions on social media platforms in the era
of COVID-19.

## Conflicts of interest

There are no conflicts of interest.

## Acknowledgments

## References

  * [1] S. Loomba, A. de Figueiredo, S. J. Piatek, K. de Graaf, H. J. Larson, Measuring the impact of covid-19 vaccine misinformation on vaccination intent in the uk and usa, Nature Human Behaviour 5 (3) (2021) 337–348.
  * [2] T. Saraykar, S. Sekhar, What social media is saying about the covid-19 vaccine rollout, https://www.iqvia.com/blogs/2021/04/what-social-media-is-saying-about-the-covid-19-vaccine-rollout (apr 2021).
  * [3] T. Hossain, R. L. Logan IV, A. Ugarte, Y. Matsubara, S. Young, S. Singh, COVIDLies: Detecting COVID-19 misinformation on social media, in: Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Association for Computational Linguistics, 2020.
  * [4] C. Castillo, M. Mendoza, B. Poblete, Information credibility on twitter, in: Proceedings of the 20th International Conference on World Wide Web, WWW ’11, Association for Computing Machinery, New York, NY, USA, 2011, p. 675–684.
  * [5] X. Liu, A. Nourbakhsh, Q. Li, R. Fang, S. Shah, Real-time rumor debunking on twitter, in: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM ’15, Association for Computing Machinery, New York, NY, USA, 2015, p. 1867–1870.
  * [6] Z. Zhao, P. Resnick, Q. Mei, Enquiring minds: Early detection of rumors in social media from enquiry posts, WWW ’15, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 2015, p. 1395–1405.
  * [7] F. Yang, Y. Liu, X. Yu, M. Yang, Automatic detection of rumor on sina weibo, in: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, MDS ’12, Association for Computing Machinery, New York, NY, USA, 2012.
  * [8] S. Kwon, M. Cha, K. Jung, W. Chen, Y. Wang, Prominent features of rumor propagation in online social media, in: 2013 IEEE 13th International Conference on Data Mining, 2013, pp. 1103–1108.
  * [9] L. Wu, R. Yuan, H. Yu, Y. Wang, N. Ambreen, A multi-semantics classification method based on deep learning for incredible messages on social media, Chinese Journal of Electronics 28 (2019) 754–763.
  * [10] J. Ma, W. Gao, P. Mitra, S. Kwon, B. J. Jansen, K.-F. Wong, M. Cha, Detecting rumors from microblogs with recurrent neural networks, in: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI’16, AAAI Press, 2016, p. 3818–3824.
  * [11] Y. Liu, Y. B. Wu, Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks, in: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, 2018, AAAI Press, pp. 354–361.
  * [12] J. Ma, W. Gao, K.-F. Wong, Detect rumors in microblog posts using propagation structure via kernel learning, in: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 708–717.
  * [13] J. Ma, W. Gao, K.-F. Wong, Rumor detection on Twitter with tree-structured recursive neural networks, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Melbourne, Australia, 2018, pp. 1980–1989.
  * [14] J. Ma, W. Gao, K.-F. Wong, Detect rumors on twitter by promoting information campaigns with generative adversarial learning, in: The World Wide Web Conference, WWW ’19, Association for Computing Machinery, New York, NY, USA, 2019, p. 3049–3055.
  * [15] X. Liu, A. Nourbakhsh, Q. Li, R. Fang, S. Shah, Real-time rumor debunking on twitter, in: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM ’15, Association for Computing Machinery, New York, NY, USA, 2015, p. 1867–1870.
  * [16] Y.-J. Lu, C.-T. Li, GCAN: Graph-aware co-attention networks for explainable fake news detection on social media, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, 2020, pp. 505–514.
  * [17] K. Shu, A. Sliva, S. Wang, J. Tang, H. Liu, Fake news detection on social media: A data mining perspective, SIGKDD Explor. Newsl. 19 (1) (2017) 22–36.
  * [18] A. Zubiaga, G. W. S. Hoi, M. Liakata, R. Procter, P. Tolmie, Analysing how people orient to and spread rumours in social media by looking at conversational threads, PLoS ONE 11.
  * [19] L. Derczynski, K. Bontcheva, M. Liakata, R. Procter, G. Wong Sak Hoi, A. Zubiaga, SemEval-2017 task 8: RumourEval: Determining rumour veracity and support for rumours, in: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 69–76.
  * [20] L. Wu, Y. Rao, H. Jin, A. Nazir, L. Sun, Different absorption from the same sharing: Sifted multi-task learning for fake news detection, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 4644–4653.
  * [21] Q. Zhang, A. Lipani, S. Liang, E. Yilmaz, Reply-aided detection of misinformation via bayesian deep learning, in: The World Wide Web Conference, WWW ’19, Association for Computing Machinery, New York, NY, USA, 2019, p. 2333–2343.
  * [22] D. M. Nguyen, T. H. Do, R. Calderbank, N. Deligiannis, Fake news detection using deep Markov random fields, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics, Minneapolis, Minnesota, 2019, pp. 1391–1400.
  * [23] E. Chen, K. Lerman, E. Ferrara, #covid-19: A public coronavirus twitter dataset tracking social media discourse about the pandemic (preprint), JMIR Public Health and Surveillance 6.
  * [24] M. Weinzierl, S. Hopfer, S. Harabagiu, Misinformation adoption or rejection in the era of covid-19, in: Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), AAAI Press, 2021.
  * [25] M. M. Beaulieu, M. Gatford, X. Huang, S. Robertson, S. Walker, P. Williams, Okapi at trec-5, in: The Fifth Text REtrieval Conference (TREC-5), the fifth text retrieval conference (trec–5) Edition, 1997, pp. 143–165.
  * [26] Covid-19 misinformation, https://en.wikipedia.org/wiki/Misinformation_related_to_the_COVID-19_pandemic#Vaccine_misinformation, accessed: 2021-02-01.
  * [27] The real facts about common covid-19 vaccine myths, https://health.ucdavis.edu/health-news/newsroom/the-real-facts-about-common-covid-19-vaccine-myths/2020/12, accessed: 2021-02-01.
  * [28] Covid-19 vaccine myths debunked, https://www.mayoclinichealthsystem.org/hometown-health/featured-topic/covid-19-vaccine-myths-debunked, accessed: 2021-02-01.
  * [29] The covid-19 vaccine: Myths vs. facts, https://www.muhealth.org/our-stories/covid-19-vaccine-myths-vs-facts, accessed: 2021-02-01.
  * [30] Debunking the myths about the covid-19 vaccine, https://www.uab.edu/news/youcanuse/item/11771-debunking-the-myths-about-the-covid-19-vaccine, accessed: 2021-02-01.
  * [31] Covid-19 vaccine myths debunked, https://newsnetwork.mayoclinic.org/discussion/covid-19-vaccine-myths-debunked/, accessed: 2021-02-01.
  * [32] Vaccine myths debunked, https://www.publichealth.org/public-awareness/understanding-vaccines/vaccine-myths-debunked/, accessed: 2021-02-01.
  * [33] F. C. Goodman, Jack, Vaccine rumours debunked: Microchips, ’altered dna’ and more (Dec 2020).
URL https://www.bbc.com/news/54893437

  * [34] A. S. Das, M. Datar, A. Garg, S. Rajaram, Google news personalization: Scalable online collaborative filtering, in: Proceedings of the 16th International Conference on World Wide Web, WWW ’07, Association for Computing Machinery, New York, NY, USA, 2007, p. 271–280.
  * [35] L. Ou-Yang, Newspaper3k: Article scraping & curation, https://github.com/codelucas/newspaper (2014).
  * [36] T. Zhang, V. Kishore, F. Wu, K. Q. Weinberger, Y. Artzi, Bertscore: Evaluating text generation with bert, in: International Conference on Learning Representations, 2020.
  * [37] A. S. Foundation, Apache lucene, https://github.com/apache/lucene (1999).
  * [38] M. L. McHugh, Interrater reliability: the kappa statistic, Biochemia medica 22 (3) (2012) 276–282.
  * [39] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, O. Yakhnenko, Translating embeddings for modeling multi-relational data, in: Advances in neural information processing systems, 2013, pp. 2787–2795.
  * [40] G. Ji, S. He, L. Xu, K. Liu, J. Zhao, Knowledge graph embedding via dynamic mapping matrix, in: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Vol. 1, 2015, pp. 687–696.
  * [41] S. Yang, J. Tian, H. Zhang, J. Yan, H. He, Y. Jin, Transms: Knowledge graph embedding for complex relations by multidirectional semantics, in: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, International Joint Conferences on Artificial Intelligence Organization, 2019, pp. 1935–1942.
  * [42] I. Balazevic, C. Allen, T. Hospedales, TuckER: Tensor factorization for knowledge graph completion, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China, 2019.
  * [43] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics, 2019, pp. 4171–4186.
  * [44] M. Müller, M. Salathé, P. E. Kummervold, Covid-twitter-bert: A natural language processing model to analyse covid-19 content on twitter, https://arxiv.org/abs/2005.07503.
  * [45] I. Beltagy, K. Lo, A. Cohan, SciBERT: A pretrained language model for scientific text, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 3615–3620.
  * [46] J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, J. Kang, Biobert: a pre-trained biomedical language representation model for biomedical text mining, Bioinformatics.
  * [47] D. Q. Nguyen, T. Vu, A. Tuan Nguyen, BERTweet: A pre-trained language model for English tweets, in: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Association for Computational Linguistics, Online, 2020, pp. 9–14.
  * [48] D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980.
  * [49] R. Nogueira, K. Cho, Passage re-ranking with bert (2020).  arXiv:1901.04085.
  * [50] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural computation 9 (8) (1997) 1735–1780.
  * [51] J. P. Chiu, E. Nichols, Named entity recognition with bidirectional LSTM-CNNs, Transactions of the Association for Computational Linguistics 4 (2016) 357–370.  doi:10.1162/tacl_a_00104.
URL https://aclanthology.org/Q16-1026

  * [52] K. O. Kwok, F. Lai, I. W. Wei, S. Y. S. Wong, J. W. Tang, Herd immunity - estimating the level required to halt the covid-19 epidemics in affected countries, Journal of Infection 6 (2020) e32–e33.
  * [53] A. Figueiredo, C. Simas, E. Karafillakis, P. Paterson, H. Larson, Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study, Lancet (London, England) 396.