# Identifying the Adoption or Rejection of Misinformation Targeting COVID-19
Vaccines in Twitter Discourse

Maxwell Weinzierl  0000-0002-8049-7453 Human Language Technology Research
Institute, University of Texas at DallasRichardsonTexasUSA
<EMAIL_ADDRESS>and  Sanda Harabagiu  0000-0002-8186-1501 Human
Language Technology Research Institute, University of Texas at
DallasRichardsonTexasUSA<EMAIL_ADDRESS>
###### Abstract.

Although billions of COVID-19 vaccines have been administered, too many people
remain hesitant. Misinformation about the COVID-19 vaccines, propagating on
social media, is believed to drive hesitancy towards vaccination. However,
exposure to misinformation does not necessarily indicate misinformation
adoption. In this paper we describe a novel framework for identifying the
stance towards misinformation, relying on attitude consistency and its
properties. The interactions between attitude consistency, adoption or
rejection of misinformation and the content of microblogs are exploited in a
novel neural architecture, where the stance towards misinformation is
organized in a knowledge graph. This new neural framework is enabling the
identification of stance towards misinformation about COVID-19 vaccines with
state-of-the-art results. The experiments are performed on a new dataset of
misinformation towards COVID-19 vaccines, called CoVaxLies, collected from
recent Twitter discourse. Because CoVaxLies provides a taxonomy of the
misinformation about COVID-19 vaccines, we are able to show which type of
misinformation is mostly adopted and which is mostly rejected.

COVID-19, vaccine, misinformation, twitter, social media, stance

††copyright: none††ccs: Computing methodologies Natural language
processing††ccs: Computing methodologies Artificial intelligence

##  1\. Introduction

Although billions of inoculations against the SARS-CoV-2 virus, the causative
agent of COVID-19, have been administered around the world starting with 2020,
too many remain hesitant about this vaccine. It is believed that hesitancy is
driven by misinformation about the COVID-19 vaccines that is spread on social
media. Recent research by Loomba et al. (2021) has shown that exposure to
online misinformation around COVID-19 vaccines affects intent to vaccinate in
order to protect oneself and others. However, exposure to misinformation about
the COVID-19 vaccines does not mean that those exposed adopt the
misinformation. This is why knowing if misinformation is adopted or rejected
when encountered in social media discourse will enable public health experts
to perform interventions at the right time and in the right place on social
media, addressing vaccine hesitancy successfully.

Misinformation detection on social media platforms, such as Twitter, is
performed in two steps: (1) the recognition whether a social media posting
contains any misconception, reference to conspiracy theories or faulted
reasoning; and (2) the recognition of the stance towards the targeted
misinformation. The stance defines the attitude the author of the micro-blog
manifests towards the misinformation target, as exemplified in Table 1. When
the misinformation is adopted, an Accept stance is observed, whereas when it
is rejected, the Reject stance reflects the attitude towards the targeted
misinformation.

Misinformation Target: The COVID vaccine renders pregnancies risky.
---
Stance: Accept
Tweet: ¡@USER¿ Chances of a healthy young woman dying of COVID if they even
catch it: 0.003% Chances of COVID vaccine causing miscarriage, birth defects,
or future infertility: ¡Data Unavailable¿ Risk management would say DON’T TAKE
THE VACCINE IF YOU’RE PREGNANT.
Stance: Reject
Tweet: Vaccinated women who breastfeed can pass #COVID19 protection to their
babies. COVID-19 #vaccines aren’t considered a risk to infants during
pregnancy or from breastfeeding. During the study, none of the women or
infants experienced serious adverse events. ¡URL¿
Table 1. Examples of tweets with different stance towards misinformation
targeting COVID-19 vaccines.

Although the identification of misinformation about COVID-19 vaccines in the
Twitter discourse is fundamental in understanding its impact on vaccine
hesitancy, we consider that efforts focusing on this first step of
misinformation detection have made important progress recently, generating
high-quality results (Liu and Wu, 2018; Ma et al., 2017, 2018, 2019). In this
paper we focus on the second step of misinformation detection, namely the
identification of the stance towards misinformation, which still needs
improvements.

A significant barrier in the identification of stance towards misinformation
targeting the COVID-19 vaccines stems from the absence of large Twitter
datasets which cover misinformation about these vaccines. To address this
limitation, we present in this paper a new Twitter dataset, called CoVaxLies,
inspired by the recently released COVIDLies dataset (Hossain et al., 2020).
CoVaxLies consists of (1) multiple known Misinformation Targets (MisTs)
towards COVID-19 vaccines; (2) a large set of [tweet, MisT] pairs, indicating
when the tweet has the stance of: (a) Accept towards the MisT; (b) Reject
towards the MisT; or (c) No Stance towards the MisT. In addition, we provide a
taxonomy of the misinformation about COVID-19 vaccines, informed by the MisTs
available in CoVaxLies, enabling the interpretation of the adopted or rejected
misinformation about COVID-19 vaccines.

As it can be noticed from the examples listed in Table 1, identifying the
stance of a tweet with respect to a given MisT is not a trivial language
processing task. The framework for stance identification presented in this
paper makes several contributions that address the Twitter discourse referring
to misinformation. First, it takes into account the attitude consistency (AC)
observed throughout the Twitter discourse between tweet authors that adopt or
reject a MisT. AC is informing the equivalence between stance identification
and the recognition of agree or disagree relations between pairs of tweets.
Second, this stance identification framework captures the interactions between
discourse AC, the stance values of tweets towards a MisT, and the language
used in the articulation of the MisT and the content of the tweets. Third, it
considers that the Twitter discourse about a MisT encapsulates knowledge that
can be represented by learning knowledge embeddings. This knowledge
contributes, along with the neural representation of the content language of
tweets, to the prediction of agreement or disagreement between pairs of tweets
referring to the same MisT. Finally, the system implementing this novel stance
identification framework has produced in our experiments very promising
results on the CoVaxLies dataset.

The remainder of the paper is organized as follows. Section 2 describes the
related work while Section 3 details the CoVaxLies dataset. Section 4
describes stance identification informed by attitude consistency (AC). Section
5 presents the experimental result while Section 6 is providing discussions of
the results. Section 7 summarizes the conclusions.

##  2\. Related Work

In previous work stance identification on Twitter was cast either as (1) a
classification problem, learning to predict the stance value of a tweet
towards a given target claim; or (2) an inference problem, concluding that a
tweet entails, contradicts or does not imply the given target claim.

Stance identification as a classification problem: Several datasets were used
in prior work aiming stance classification on Twitter. The PHEME dataset
(Zubiaga et al., 2016) 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, 1,067 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 (Derczynski et al., 2017). The stance
labels are ‘support’, ‘deny’, ‘comment’ and ‘query’. There are 865 tweets
annotated with the ‘support’ stance label; 325 tweets annotated with the
‘deny’ stance label; 341 tweets annotated with the ‘query’ stance label and
2789 tweets annotated with the ‘comment’ stance label. Several neural
classification architectures for stance identification were designed by
participants in RumourEval (Augenstein et al., 2016; Liu et al., 2016; Wei et
al., 2016). However, Ghosh et al. (2019) have shown that the original pre-
trained BERT (Devlin et al., 2019) without any further fine-tuning outperforms
all these former state-of-the-art models on the RumourEval dataset, including
the model that utilizes both text and user information (Del Tredici et al.,
2019).

More recently, another dataset containing stance annotations was released,
namely the COVIDLies dataset (Hossain et al., 2020). The starting point was
provided by 86 common misconceptions about COVID-19 available from the
Wikipedia page dedicated to COVID-19 misinformation, which became
Misinformation Targets (MisTs). For each known MisT, a set of tweets were
annotated with three possible values for stance towards each misconception:
(1) agree, when the tweet adopts the MisT; (2) disagree, when the tweet
contradicts/rejects the MisT; and (3) no stance when the tweet is either
neutral or is irrelevant to the MisT. Of the 6761 annotated tweets, 5,748
(85.02%) received a label of no stance; 670 (9.91%) received a label of agree
and 343 (5.07%) received a label of disagree. Recently, using this dataset,
Weinzierl et al. (2021) used a neural language processing model that exploits
the pre-trained domain-specific language model COVID-Twitter-BERT-v2
(Muller2020COVIDTwitterBERTAN) and refined it by stacking several layers of
lexico-syntactic, semantic, and emotion Graph Attention Networks (GATs)
(Velickovic et al., 2018) to learn and all the possible interactions between
these different linguistic phenomena, before classifying a tweet as (a)
agreeing; (b) disagreeing or (c) having no stance towards a MisT.

Stance identification as an inference problem: When the COVIDLies dataset of
stance annotations was released in (Hossain et al., 2020), stance
identification was presented as a natural language inference problem which can
benefit from existing textual inference datasets. In fact, Bidirectional LSTM
encoders and Sentence-BERT (SBERT) (Reimers and Gurevych, 2019) were trained
on three common NLI datasets—SNLI (Bowman et al., 2015), MultiNLI (Williams et
al., 2018), and MedNLI (Romanov and Shivade, 2018).

We were intrigued and inspired by the COVIDLies dataset, and believed that 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 identifying the stance
towards misinformation targeting COVID-19 vaccines.

Figure 1. Taxonomy of Misinformation

##  3\. Stance Annotations in CoVaxLies

###  3.1. CoVaxLies: A Twitter Dataset of Misinformation about COVID-19
Vaccines

The CoVaxLies Twitter dataset contains misinformation about COVID-19 vaccines
represented as (1) several known Misinformation Targets (MisTs); (2) a
collection of tweets paired with the MisTs they refer to and annotated with
stance values, indicating whether the tweet agrees, disagrees or has no stance
towards the MisT; and (3) a taxonomy of misinformation about the COVID-19
vaccines, revealing the themes and the concerns addressed by the MisTs from
CoVaxLies. We used two information sources for identifying Misinformation
Targets (Mists) for COVID-19 vaccines. First, we have considered (a) the
Wikipedia page available at
en.wikipedia.org/wiki/COVID-19_misinformation#Vaccines, which collects many
misconception claims referring to the vaccines developed for immunization
against the SARS-CoV-2 virus; and (b) MisTs identified by 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), which have been actively collecting misinformation about
the COVID-19 vaccines and debunking them on public websites. There are 17
MisTs about COVID-19 vaccines identified in this way in CoVaxLies. Appendix A
provides examples of MisTs identified in this way.

Secondly, we have used 19 questions from the Vaccine Confidence Repository
(Rossen et al., 2019) to retrieve answers from an index of 5,865,046 unique
original tweets obtained from the Twitter streaming API as a result of the
query “(covid OR coronavirus) vaccine lang:en”. These tweets were authored in
the time frame from December 18th, 2019, to January 4th, 2021. Many answers
that were retrieved as responding to questions about vaccine confidence
contained misinformation, and became MisTs as well. We identified an
additional set of 37 MisTs, out of which 7 MisTs were already known to us from
the first source of information. Appendix A provides examples of MisTs
retrieved as answers to questions about vaccine confidence. Therefore,
CoVaxLies relies on 47 MisTs about COVID-19 vaccines. 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.

In order to identify $\mathcal{}{T}_{R}$, the collection of tweets which
potentially contain language relevant to the MisTs from CoVaxLies, we relied
on two information retrieval systems: (1) a retrieval system using the BM25
(Beaulieu et al., 1997) scoring function; and (2) a retrieval system using
BERTScore (Zhang et al., 2020) with Domain Adaptation (DA), identical to the
one used by Hossain et al. (2020). Both these retrieval systems operated on an
index of $\mathcal{}{C}_{T}$, retrieving tweets by processing CoVaxLies MisTs
as queries.

Researchers from the Human Language Technology Research Institute (HLTRI) at
the University of Texas at Dallas judged 7,346 tweets to be relevant to the
MisTs from CoVaxLies and organized them in [tweet, MisT] pairs, annotated with
stance information. There are 3,720 tweets which Accept their MisT, 2,194
tweets which Reject their and 1,238 tweets that have No Stance. We note that
CoVaxLies contains an order of magnitude more stance annotations than PHEME
(Zubiaga et al., 2016), the most popular Twitter dataset containing stance
annotations, and therefore it presents clear advantages for neural learning
methods.

To enable the usage of CoVaxLies in neural learning frameworks, we split the
tweets 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 stance identification systems, described in Section 4. The
development collection, which consists of 527 [tweet, MisT] pairs, was used to
select model hyperparameters, such as threshold values. The test collection,
which consists of 1,452 [tweet, MisT] pairs, was used to evaluate the stance
identification approaches, enabling us to report the results in Section 5.

Figure 2. Distribution of Misinformation Themes and Concerns in the tweets
available from CoVaxLies.

###  3.2. The Misinformation Taxonomy from CoVaxLies

Figure 1 illustrates the taxonomy of misinformation available in CoVaxLies.
The themes represent the highest level of abstraction, while the concerns
differentiate the various MisTs from CoVaxLies. The taxonomy emerged from
discussion between public health experts from the University of California,
Irvine School of Medicine and computational linguists from HLTRI. Nine
misinformation themes were revealed, all characterizing aspects that impact
confidence in the COVID-19 vaccine. Confidence, along with convenience and
complacency, are well known universal factors contributing to vaccine
hesitancy, according to the 3C model (MacDonald, 2015). For each
misinformation theme, as shown in Figure 1, a different number of concerns
were revealed: the largest number of concerns pertain to the theme predicating
the fact that the COVID-19 vaccines are unsafe (8 concerns) while the smallest
number of concerns pertain to the themes claiming that the vaccines are not
effective or that information about the vaccines is concealed. Using the
information provided by the taxonomy illustrated in Figure 1, we notice in
Figure 2 that the misinformation themes that dominate the tweets from
CoVaxLies are those about the ingredients of the COVID-19 vaccines, about the
adverse events and the fact that the vaccines are unsafe. Moreover, the
dominant misinformation regarding the vaccine ingredients claims that the
vaccines contain the virus, while the dominant concerns of the lack of safety
of the vaccines indicates risky pregnancies or Bell’s palsy.

Figure 3. Distribution of Stance Values across Misinformation Themes in
CoVaxLies.

When considering the distribution of tweets that adopt the misinformation,
those that reject it and those that are neutral (because of having no stance)
for the tweets across all the misinformation themes, we noticed, as
illustrated in Figure 3, that the misinformation that is most adopted has the
theme of considering alternatives to the COVID-19 vaccines, immediately
followed by misinformation regarding the testing of the vaccines and the
ingredients used in the vaccines. Interestingly, most of the misinformation
that is rejected has to do with the theme indicating that the COVID-19
vaccines are unnecessary, or that they affect the immune system.

##  4\. Stance Identification through Attitude Consistency

###  4.1. Attitude Consistency and Stance

Central to our stance identification framework is the belief that the stance
of any tweet $t_{j}$ towards a particular MisT $m_{i}$ should not be
considered in isolation. Because $t_{j}$ participates in the Twitter discourse
about $m_{i}$, its stance should be consistent with the attitude of the other
tweet authors towards $m_{i}$. We hypothesize that all the authors of tweets
that Accept $m_{i}$ must be agreeing among themselves with regard to $m_{i}$.
Similarly, all the authors of tweets that Reject $m_{i}$ must also be agreeing
among themselves with regard to $m_{i}$. But also, any author of a tweet
$t_{j}$ that has an Accept stance towards $m_{i}$ must disagree with the
author of any tweet $t_{k}$ that has a Reject stance towards $m_{i}$.
Therefore, all these tweet authors have Attitude Consistency (AC) towards
$m_{i}$. AC can be illustrated as in Figure 4, by linking all the tweets that
have the same stance towards a MisT $m_{i}$ through implicit agree relations,
and all tweets that have opposing stances towards $m_{i}$ with implicit
disagree relations. In this way, all the tweets that have an Accept stance
towards $m_{i}$ are organized in a fully connected graph spanned by agree
relations and similarly, all the tweets having a Reject stance towards $m_{i}$
are organized in a fully connected graph spanned also by agree relations. In
addition, disagree relations are established between all pairs of tweets that
have opposing stance towards $m_{i}$. Moreover, all tweets that do not have
either an Accept or Reject stance towards $m_{i}$ are considered to
automatically have No Stance towards $m_{i}$. Hence. the stance values
$SV=\\{Accept,Reject\\}$ are the only ones informing AC.

Figure 4. Stance Misinformation Knowledge Graph Figure 5. Attitude Consistency
Examples

As shown in Figure 4, a Stance Misinformation Knowledge Graph is organized for
each $m_{i}$, referred to as SMKG($m_{i}$). For clarity, the SMKG($m_{i}$)
illustrated in Figure 4 shows only several of the agree and disagree
relations. For each MisT $m_{i}$ available in the CoVaxLies dataset, we
generate an SMKG($m_{i}$) when considering only the tweets annotated with
Accept or Reject stance information, available from the training set of
CoVaxLies. However, there are many other tweets in CoVaxLies with no known
stance towards any of the MisTs available in the dataset. We refer to the
entire set of such tweets as the Tweets with Unknown Stance towards
Misinformation (TUSM).

To identify the stance of tweets from TUSM we assume that AC is preserved.
This entails three possible cases when considering in addition to the
SMKG($m_{i}$), tweets from TUSM, e.g. $t_{1}$, $t_{2}$, $t_{3}$ or $t_{4}$, as
illustrated in Figure 5. All the three cases of AC show that the unknown
stance of any tweet $t_{x}\in$ TUSM can be identified as Accept when knowing
if (a) an agree relation is predicted between $t_{x}$ and $t_{A}$, a tweet
known to have an Accept stance towards $m_{i}$; or (b) a disagree relation is
predicted between $t_{x}$ and $t_{B}$, a tweet known to have a Reject stance
towards $m_{i}$. Similarly, the unknown stance of any tweet $t_{x}\in$ TUSM
can be identified as Reject when knowing if (a) a disagree relation is
predicted between $t_{x}$ and $t_{A}$, a tweet known to have an Accept stance
towards $m_{i}$; or (b) an agree relation is predicted between $t_{x}$ and
$t_{B}$, a tweet known to have a Reject stance towards $m_{i}$. If none of
these relations can be predicted, then the stance of $t_{x}$ is identified as
No Stance towards $m_{i}$. To formalize the interaction between the implicit
relation types and the values of the stance towards a MisT $m_{i}$ identified
for a pair of tweets $t_{x}$ and $t_{y}$ we considered a function that selects
the Relation Type that preserves AC (RTAC), defined as:

(1) $RTAC(s_{x},s_{y})=\left\\{\begin{array}[]{ll}agree&\mbox{if
$s_{x}=s_{y}$}\\\ disagree&\mbox{if $s_{x}\neq s_{y}$}\end{array}\right.$

where the value of the stance of $t_{x}$ towards $m_{i}$ is $s_{x}$ while the
value of the stance of $t_{y}$ is $s_{y}$. Moreover, we believe that AC can be
further extended to account for an entire chain of agree and disagree
relations spanning tweets with unknown stance towards $m_{i}$.

###  4.2. Transitive Attitude Consistency

Transitive Attitude Consistency extends the interaction between the values of
the stance towards a MisT $m_{i}$ and the binary agree and disagree relations
to an entire chain of such implicit relations that may connect a tweet from
TUSM to a tweet from SMKG($m_{i}$), whose stance is known. For example, Figure
6 shows how the identified stance towards $m_{i}$ of tweets $t_{x}$, $t_{y}$,
$t_{z}$ and $t_{w}$ is informed by chains of agree or disagree relations
originating either in $t_{A}$ or $t_{B}$, tweets from SMKG($m_{i}$). It is
important to note that this extension has to take into account that every time
a new stance $s_{x}$ towards a MisT $m_{i}$ is identified for a tweet
$t_{x}\in$ TUSM, the confidence that the AC is preserved is computed by an
Attitude Consistency Score ($ACS$). $ACS$ depends on $l$, the number of
relations in the chain originating at a tweet with known stance, available
from SMKG($m_{i}$) and ending at a tweet $t_{x}\in$ TUSM, with unknown stance:
$ACS^{l}(t_{x},s_{x},m_{i})$. To compute $ACS^{l}(t_{x},s_{x},m_{i})$ we first
need to consider the way in which we can represent the SMKG($m_{i}$).

Figure 6. Stance Identification with Transitive Attitude Consistency and
Attitude Consistency Scores.

The knowledge graph of SMKG($m_{i}$) can be represented in a continuous vector
space called the embedding space by learning knowledge embeddings for its
nodes and edges. When formalizing the SMKG($m_{i}$)$=(V;E)$, each node
$v_{k}\in V$ can be seen as $v_{k}=(t_{k},s_{k})$, where a tweet $t_{k}$ is
paired with its stance $s_{k}$ towards $m_{i}$; and each edge $e_{ij}\in E$ is
either an agree or a disagree relation. Knowledge embedding models learn an
embedding $te_{k}$ for each tweet $t_{k}$ as well as an embedding
$me_{i}^{agree}$ for the agree relation in SMKG($m_{i}$) and an embedding
$me_{i}^{disagree}$ for the disagree relation in SMKG($m_{i}$). But more
importantly, knowledge embedding models use a relation scoring function $f$
for assigning a plausibility score to any potential link between two tweets
$t_{x}$ and $t_{y}$, given their knowledge embeddings $te_{x}$ and $te_{y}$
and the embedding of the relation they share. Because the relation between
$t_{x}$ and $t_{y}$ must preserve AC between the stance $s_{x}$ identified for
$t_{x}$ and the stance $s_{y}$ identified for $t_{y}$, the relation between
these two tweets is provided by the function $RTAC(s_{x},s_{y})$. The
embedding of the relation indicated by $RTAC(s_{x},s_{y})$ is computed as:

(2) $RE(s_{x},s_{y},m_{i})=\left\\{\begin{array}[]{ll}me_{i}^{agree}&\mbox{if
$RTAC(s_{x},s_{y})$ = {\em agree}}\\\ me_{i}^{disagree}&\mbox{if
$RTAC(s_{x},s_{y})$ = {\em disagree}}\end{array}\right.$

Hence, the scoring function of the relation between the pair of tweets $t_{x}$
and $t_{y}$ is defined as $f(te_{x},RE(s_{x},s_{y},m_{i}),te_{y})$, where $f$
is provided by various knowledge embedding models, such as those that we
discuss in Section 4.3,

Given the representation of SMKG($m_{i}$) through knowledge embeddings, we can
define $ACS^{l}(t_{x},s_{x},m_{i})$, starting with the chains of length $l=1$:

(3) $ACS^{1}(t_{x},s_{x},m_{i})=\sum\limits_{(t_{y},s_{y})\in
SMKG(m_{i})}\frac{f(te_{x},RE(s_{x},s_{y},m_{i}),te_{y})}{|SMKG(m_{i})|}$

Then, $ACS^{l}(t_{x},s_{x},m_{i})$ for chains of length $l>1$ is computed by
considering that we have defined already $SV=\\{Accept,Reject\\}$ and that we
shall take into account all tweets from TUSM when generating chains of agree
and/or disagree relations originating in SMKG. We compute
$ACS^{l}(t_{x},s_{x},m_{i})$ as:

(4) $ACS^{l}(t_{x},s_{x},m_{i})=\\\ \sum\limits_{\begin{subarray}{c}t_{z}\in
TUSM\\\ t_{z}\neq t_{x}\end{subarray}}\sum\limits_{s_{z}\in
SV}\frac{ACS^{l-1}(t_{z},s_{z},m_{i})+f(te_{x},RE(s_{x},s_{z},m_{i}),te_{z})}{|TUSM|-1}$

To consider the overall $ACS^{*}$ of any tweet $t_{x}$ with stance $s_{x}$
towards $m_{i}$ we average the $ACS$ across all possible chains of relations,
of varying lengths, up to a maximum length $L$:

(5)
$ACS^{*}(t_{x},s_{x},m_{i})=\frac{1}{L}\sum\limits_{l=1}^{L}ACS^{l}(t_{x},s_{x},m_{i})$

Finally, stance $s_{x}$ towards $m_{i}$ of a tweet $t_{x}\in$ TUSM is assigned
the value corresponding to the maximum $ACS^{*}$:

(6) $s_{x}=\operatorname*{argmax}_{s_{k}\in SV}{ACS^{*}(t_{x},s_{k},m_{i})}$

However, Equation 5 shows how we assign stance of value Accept or Reject to
tweets with previously unknown stance towards a MisT $m_{i}$. To also assign
the stance value No Stance, we relied on the development set from CoVaxLies to
assign a threshold value $T(m_{i})$ for each MisT $m_{i}$, such that when
$ACS^{*}(t_{x},s_{x},m_{i})\leq T(m_{i})$, for stance values Accept and
Reject, we can finalize the stance $s_{x}$ of a tweet $t_{x}$ as having the
value No Stance. With all stance values finalized for tweets from TUSM towards
any MisT $m_{i}$ from CoVaxLies, we update SMKG($m_{i}$) to contain all the
tweets from TUSM that have either an Accept or a Reject stance towards
$m_{i}$.

###  4.3. Learning Knowledge Embeddings for the Stance Misinformation
Knowledge Graph

Knowledge embedding models such as TransE (Bordes et al., 2013) and TransD (Ji
et al., 2015) have had significant success in modeling relations in knowledge
graphs. More recently, new knowledge embeddings models capture more complex
interactions from the knowledge graph, e.g. TransMS (Yang et al., 2019),
TuckER (Balazevic et al., 2019), and RotatE (Sun et al., 2019). Each knowledge
embedding model provides a different method of scoring the likelihood of
relations in the knowledge graph SMKG($m_{i}$), as shown in Table 2. The
scoring of a relation in each knowledge embedding model relies on
$me_{i}^{r}$, the embedding of a relation that maintains AC with the stance
towards a MisT $m_{i}$ of the tweets connected by the relation, and on the
embeddings of these tweets, $te_{x}$ and $te_{y}$.

KE Model |  Scoring Function $f(te_{x},me_{i}^{r},te_{y})$
---|---
TransE (Bordes et al., 2013) | $-||te_{x}+me_{i}^{r}-te_{y}||$
TransD (Ji et al., 2015) | $-\left\lVert(\bm{I}+me_{i}^{r,p}\times({te^{p}_{x}})^{\top})\times te_{x}\right.+me_{i}^{r}$
| $-\left.(\bm{I}+me_{i}^{r,p}\times({te^{p}_{y}})^{\top})\times
te_{y}\right\rVert$
TransMS (Yang et al., 2019) | $-\left\lVert-tanh(te_{y}\odot me_{i}^{r})\odot te_{x}\right.+me_{i}^{r}$
| $+\alpha_{i}^{r}\cdot(te_{x}\odot te_{y})\left.-tanh(te_{x}\odot
me_{i}^{r})\odot te_{y}\right\rVert$
TuckER (Balazevic et al., 2019) | $\mathcal{W}\times_{1}te_{x}\times_{2}me_{i}^{r}\times_{3}te_{y}$
RotatE (Sun et al., 2019) | $-||te_{x}\odot me_{i}^{r}-te_{y}||$
Table 2. Knowledge Embedding Scoring Functions.

In Table 2, we denote $||\cdot||$ as the $L1$ norm, $\bm{I}$ is the identity
matrix, $tanh(x)$ is the non-linear hyperbolic tangent function and
$\alpha^{r}_{i}$ is a real numbered parameter dependent on each MisT. The
operator $\odot$ represents the Hadamard product, and $\times_{n}$ indicates
the tensor product along the n-th mode. Any of the scoring functions listed in
Table 2 measure the likelihood of an agree or disagree relation between a pair
of tweets which preserves the AC with the stance of the tweets. However, the
content of the tweets, communicated in natural language, with the subtleties
and deep connections expressed in language also need to be captured when
scoring these relations.

###  4.4. Interactions between Tweet Language, Stance towards Misinformation
and Attitude Consistency

The AC of various tweet authors is expressed through the language they use in
their tweets. Therefore. it is imperative to also consider the interaction of
the language of tweets with the stance towards misinformation and the attitude
consistency of the tweet author’s discourse. Because the identification of
stance towards misinformation is equivalent to discovering the type of
relation, either agree or disagree, shared by a pair of tweets that preserves
AC, we designed a neural language architecture which considers (1) the
contextual embeddings of each MisT $m_{i}$, as well as each pair of tweets
$t_{x}$ and $t_{y}$ having a stance towards $m_{i}$; and (2) knowledge
embeddings learned for the SMKG($m_{i}$) such that we predict the likelihood
of a relation between $t_{x}$ and $t_{y}$ to be of type agree or to be of type
disagree. This neural architecture for Language-informed Attitude Consistency-
preserving Relation scoring (LACRscore) is illustrated in Figure 7.

Figure 7. Neural Architecture for Language-informed Attitude Consistency-
preserving Relation scoring (LACRscore).

Given a MisT $m_{i}$, the LACRscore system first performs Word-Piece
Tokenization (Devlin et al., 2019) on (a) the textual description of $m_{i}$,
producing tokens $m_{1}^{i},m_{2}^{i},...,m_{d}^{i}$, as well as on the text
of tweets $t_{x}$ and $t_{y}$, which are then passed through the BERT (Devlin
et al., 2019) COVID-19 Language Model COVID-Twitter-BERT-v2 (Müller et al.,
2020) pre-trained on the masked language modeling task (Devlin et al., 2019)
for 97 million COVID-19 tweets. This process of further pre-training has been
shown to improve performance on downstream tasks in various scientific
(Beltagy et al., 2019), biomedical (Lee et al., 2019), and social media
(Nguyen et al., 2020) domains. COVID-Twitter-BERT-v2 produces contextualized
embeddings $mc_{1}^{i},mc_{2}^{i},...,mc_{d+2}^{i}$ for the word-piece tokens
in the MisT $m_{i}$ along with the $[CLS]^{i}$ and $[SEP]^{i}$ tokens. In this
way, we encode the language describing the MisT $m_{i}$ using a contextualized
embedding $mc_{1}^{i}\in\mathbb{R}^{1024}$, where $1024$ is the contextual
embedding size for COVID-Twitter-BERT-V2. Similarly, the language used in the
tweets $t_{x}$ and $t_{y}$ is represented by contextual embeddings
$tc_{1}^{x}$ and $tc_{1}^{y}$ after being processed through COVID-Twitter-
BERT-v2. But, it is important to note, that the scoring function $f$ from any
of the knowledge embedding models provided in Table 2, cannot operate directly
on the contextual embeddings $tc_{1}^{x}$, $mc_{1}^{i}$ or $tc_{1}^{y}$, as
they do not have the same dimensions of the knowledge embeddings these models
learn. Additionally, we need to produce two knowledge embeddings for the MisT
$m_{i}$ to represent both the agree and disagree relation embeddings.
Therefore, in LACRscore 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 two
separate fully-connected layers, to project from $mc_{1}^{i}$ into the
necessary knowledge embeddings $me_{i}^{agree}$ and $me_{i}^{disagree}$ from
any of the knowledge embedding models considered. Similarly, the Tweet
Knowledge Embedding Projection Encoder (T-KEPE) uses a different fully-
connected layer than M-KEPE to project from $tc_{1}^{x}$ and $tc_{1}^{y}$ to
$te_{x}$ and $te_{y}$ respectively. As shown in Figure 7, these encoders
produce the arguments of the scoring function $f$, provided by some knowledge
embedding model. The likelihood of an agree or disagree relation between
tweets $t_{x}$ and $t_{y}$ with respect to the MisT $m_{i}$ is computed by
$f(te_{x},me_{i}^{agree},te_{y})$ and $f(te_{x},me_{i}^{disagree},te_{y})$.

LACRscore was trained on the SMKG($m_{i}$) derived from the training
collection of CoVaxLies, described in Section 3.1. Relations from each
SMKG($m_{i}$) were used as positive examples, and we performed negative
sampling to construct “Attitude Inconsistent” examples. Negative sampling
consists of corrupting a relation $r$ between tweets $t_{x}$ with stance
$s_{x}$ and $t_{y}$ with stance $s_{y}$ towards MisT $m_{j}$, which preserves
AC. This corruption process is performed by randomly sampling either (1): a
different tweet $(t_{z},s_{z})\in SMKG(m_{i})$ with the same relation
$\hat{r}=r$, to replace $t_{y}$ such that $RTAC(s_{z},s_{x})\neq r$, or (2):
flipping $r$ from an agree relation to $\hat{r}=$disagree relation, or vice
versa. The negative sampling will ensure that AC relations will be scored
higher than non-AC relations. Moreover, we optimized the following margin loss
to train LACRscore when scoring relations:

(7) $\mathcal{L}=\\\
\sum\limits_{\begin{subarray}{c}\end{subarray}}{\left[\gamma-f(te_{x},me^{r}_{i},te_{y})+f(te_{x},me_{i}^{\hat{r}},te_{z})\right]_{+}}$

where $\gamma$ is a training score threshold which represents the differences
between the score of AC relations and the non-AC relations. The loss
$\mathcal{L}$ is minimized with the ADAM(Kingma and Ba, 2015) optimizer, a
variant of gradient descent.

System | Accept F1 | Accept P | Accept R | Reject F1 | Reject P | Reject R | Macro F1 | Macro P | Macro R
---|---|---|---|---|---|---|---|---|---
NLI-Tweet-MisT (Hossain et al., 2020) | 45.9 | 72.9 | 33.5 | 54.6 | 38.6 | 93.2 | 50.2 | 55.8 | 63.3
DS-StanceId (Weinzierl et al., 2021) | 86.2 | 88.3 | 84.2 | 79.1 | 82.7 | 75.8 | 82.7 | 85.5 | 80.0
LES-GAT-StanceId (Weinzierl et al., 2021) | 86.7 | 84.6 | 88.9 | 80.7 | 83.2 | 78.3 | 83.7 | 83.9 | 83.6
LACRscore |  |  |  |  |  |  |  |  |
\+ TransE | 69.4 | 65.6 | 73.7 | 47.7 | 52.3 | 43.9 | 58.6 | 59.0 | 58.8
\+ TransE + ACS | 60.1 | 64.0 | 56.7 | 50.5 | 44.7 | 58.1 | 55.3 | 54.4 | 57.4
\+ TransD | 54.9 | 59.4 | 51.0 | 46.6 | 40.3 | 55.2 | 50.7 | 49.9 | 53.1
\+ TransD + ACS | 51.6 | 56.7 | 47.4 | 41.5 | 35.3 | 50.5 | 46.6 | 46.0 | 48.9
\+ TuckER | 87.7 | 86.7 | 88.7 | 82.3 | 79.3 | 85.5 | 85.0 | 82.0 | 87.1
\+ TuckER + ACS | 86.1 | 85.6 | 86.6 | 80.9 | 73.5 | 89.8 | 83.5 | 79.6 | 88.2
\+ RotatE | 86.6 | 83.6 | 89.9 | 80.9 | 73.5 | 89.8 | 83.7 | 78.5 | 89.9
\+ RotatE + ACS | 86.6 | 85.7 | 87.5 | 83.0 | 80.5 | 85.8 | 84.8 | 83.1 | 86.6
\+ TransMS | 85.7 | 81.8 | 90.0 | 78.4 | 69.3 | 90.3 | 82.1 | 75.6 | 90.1
\+ TransMS + ACS | 88.7 | 89.8 | 87.6 | 85.6 | 83.2 | 88.2 | 87.1 | 86.5 | 87.9
Table 3. Results from the stance identification experiments on the CoVaxLies
dataset.

##  5\. Experimental Results

To evaluate the quality of stance identification on the test collection from
CoVaxLies we use the Precision (P), Recall (R), and F1 metrics for detecting
the _Accept_ and _Reject_ values of stance. We also compute a Macro averaged
Precision, Recall, and F1 score. The evaluation results are listed in Table 3.
The bolded numbers represent the best results obtained. When evaluating the
LACRscore system, we have considered (1) five possible knowledge embedding
models (TransE; TransD; TuckER; RotatE; and TransMS), which provide different
relation scoring functions; and (2) two possible options of stance prediction:
(a) using the Attitude Consistency Scoring (ACS) approach described in Section
4.2; and (b) ignoring ACS by and constraining $L=1$ for any chain of
relations, thus ignoring the transitive property of AC.

In addition, we have evaluated several baselines. First, we considered the
system introduced by Hossain et al. (2020), listed as the Natural Language
Inference between Tweet text and MisT text (NLI-Tweet-MisT) system. As a
baseline, we have also considered the Domain-Specific Stance Identification
(DS-StanceId) (Weinzierl et al., 2021) system, which utilizes the ”[CLS]”
embedding from COVID-Twitter-BERT-v2 to directly perform stance
classification. In addition, we considered the Lexical, Emotion, and Semantic
Graph Attention Network for Stance Identification (LES-GAT-StanceId)
(Weinzierl et al., 2021) system which relies on Lexical, Emotion, and Semantic
Graph Attention Networks.

The NLI-Tweet-MisT system produced a Macro F1 score of $50.2$, indicating that
stance identification as inference over language is not sufficient. Far
superior results were obtained by the DS-StanceId system with a Macro F1 score
of $82.7$, showcasing the advantage of fine-tuning stance identification
systems. The LES-GAT-StanceId system produced a Macro F1 score of $83.7$,
which indicates that integrating Lexical, Emotional, and Semantic Graphs
further improves stance identification. The LACRscore system with the TuckER
configuration produced a Macro F1 score of $85.0$, indicating that identifying
the stance towards misinformation through AC presents performance advantages
over previous methods. Unsurprisingly. the LACRscore system with the TransMS +
ACS configuration performed best, producing a Macro F1 score of $87.1$, which
indicates that the transitive nature of AC should not be ignored. The results
also show that detecting misinformation rejection tends to be more difficult
than the identification of misinformation adoption.

System hyperparameters were selected by maximizing the F1 score of each system
on the development set. The LACRscore system was trained with the following
hyperparameters: a linearly decayed learning rate of $1e-4$ which was warmed
up over the first $10\%$ of the $36$ total epochs, an attention drop-out rate
of $10\%$, a batch size of $32$, and the tweet and MisT knowledge embedding
size was set to $8$ for all knowledge embedding models, as we found that to
perform best on the development set. The LACRscore system utilized the
training set for learning to score AC-preserving relations by optimizing the
margin loss, described in Equation 7. The LACRscore system with the ACS
configuration utilized a maximum chain length $L$ of $32$, the length value
performing best on the development set. The $\gamma$ hyperparameter is set to
$4.0$ for all knowledge graph embedding models, and we sampled $1$ negative
corrupted relation for each AC relation in the SMKG($m_{i}$). Threshold values
$T(m_{i})$ were also automatically selected by maximizing the F1 score of the
LACRscore system on each MisT $m_{i}$ on the development set.

##  6\. Discussion

Figure 8. F1-scores of the misinformation adoption vs. rejection discovered by
the LACRscore system with the TransMS and ACS configuration across
misinformation Themes from the CoVaxLies dataset.

Because the LACRscore system produced the best results with the TransMS and
ACS configuration, we performed an analysis of the F1 scores of this system
across each of the themes available in the CoVaxLies Misinformation Hierarchy,
considering both the adoption and rejection of misinformation, as illustrated
in Figure 8. The identification of adopted misinformation has remarkable
performance, across all themes. Moreover, the misinformation rejection is
identified quite well too, except for the theme of concealing information
about vaccines. This is explained by the observation that this theme is
addressed by few tweets in CoVaxLies, as illustrated in Figure 2, and
moreover, it has the smallest percentage of rejection stance values, as
illustrated in Figure 3.

##  7\. Conclusion

In this paper we present a new method for identifying the stance towards
misinformation informed by attitude consistency (AC), which accounts for very
promising results on CoVaxLies, a new Twitter dataset of misinformation
targeting the COVID-19 vaccines. AC proves to be a stronger signal for stance
identification than lexical, emotional and semantic knowledge alone. Moreover,
AC informs the knowledge encapsulated in the misinformation discourse on
Twitter, which explains the promising results produced by this method, both
for the adoption and rejection of misinformation about COVID-19 vaccines.

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##  Appendix A Misinformation Targets in CoVaxLies

Misinformation Targets (MisTs), which represent common misconceptions about
the COVID-19 vaccines or refer to conspiracy theories associated with these
vaccines, have two different sources. In Table 4, all examples marked with
$\diamond$ correspond to some of the MisTs identified as known misinformation
from Wikipedia and other trusted sources, while all examples marked with
$\Box$ correspond to some of the answers to questions about vaccine
confidence, originating from Rossen et al. (2019).

$\diamond$ RNA alters a person’s DNA when taking the COVID-19 vaccine.
---
$\diamond$ The COVID-19 vaccine causes infertility or miscarriages in women.
$\diamond$ The COVID-19 vaccine causes Bell’s palsy.
$\diamond$ The COVID-19 vaccine contains tissue from aborted fetuses.
$\diamond$ The COVID-19 vaccine can cause autism.
$\diamond$ Hydroxychloroquine protects against COVID-19.
$\diamond$The COVID-19 Vaccine is a satanic plan to microchip people
$\Box$ There are severe side effects of the COVID-19 vaccines, worse than
having the virus.
$\Box$ The COVID-19 vaccine is not safe because it was rapidly developed and
tested.
$\Box$ The COVID-19 vaccine can increase risk for other illnesses.
$\Box$ Vaccines contain unsafe toxins such as formaldehyde, mercury or
aluminum.
$\Box$ Governments hide COVID-19 vaccine safety information
$\Box$ The COVID-19 Vaccine will make you gay.
Table 4. Examples of COVID-19 Misinformation Targets

##  Appendix B Code and CoVaxLies Data Availability

The CoVaxLies dataset, comprising the Misinformation Targets (MisTs), the
misinformation taxonomy, and the [${tweet}_{i}$, ${MisT}_{j}$] pairs, which
associate a ${tweet}_{i}$ with its evoked ${MisT}_{j}$ along with stance
annotations. The CoVaxLies dataset is publicly available at the following
GitHub repository:

https://github.com/Supermaxman/vaccine-lies/tree/master/covid19

Code needed to reproduce

the experiments described in this paper is also publicly available at the
following GitHub repository:

https://github.com/Supermaxman/covid19-vaccine-nlp

We note that an early version of CoVaxLies was presented in Weinzierl and
Harabagiu (2021), but in that version of CoVaxLies only 17 Misinformation
Targets (MisTs) were available, namely the MisTs discovered from Wikipedia and
other trusted sources, which are available in this later version as well.
Moreover, the previous version of CoVaxLies did not contain any _stance_
annotations, and it did not contain the misinformation taxonomy which were
made available in the current version.