# Like Article, Like Audience: Enforcing Multimodal Correlations for
Disinformation Detection

Liesbeth Allein  European Commission, Joint Research Centre
(JRC)IspraLombardyItaly ,  Marie-Francine Moens  KU LeuvenLeuvenBelgium and
Domenico Perrotta  European Commission, Joint Research Centre
(JRC)IspraLombardyItaly

###### Abstract.

User-generated content (e.g., tweets and profile descriptions) and shared
content between users (e.g., news articles) reflect a user’s online identity.
This paper investigates whether correlations between user-generated and user-
shared content can be leveraged for detecting disinformation in online news
articles. We develop a multimodal learning algorithm for disinformation
detection. The latent representations of news articles and user-generated
content allow that during training the model is guided by the profile of users
who prefer content similar to the news article that is evaluated, and this
effect is reinforced if that content is shared among different users. By only
leveraging user information during model optimization, the model does not rely
on user profiling when predicting an article’s veracity. The algorithm is
successfully applied to three widely used neural classifiers, and results are
obtained on different datasets. Visualization techniques show that the
proposed model learns feature representations of unseen news articles that
better discriminate between fake and real news texts.

Disinformation, Fake News, Constrained Representation Learning, Multimodal
Data Fusion, Natural Language Processing

††ccs: Computer systems organization Neural networks††ccs: Theory of
computation Semi-supervised learning††ccs: Information systems Document
representation††ccs: Information systems Clustering and classification††ccs:
Computing methodologies Natural language processing††ccs: Computing
methodologies Learning latent representations

##  1\. Introduction

Disinformation - or the more politically-loaded term ‘fake news’ - is not new.
Throughout history, it has been a popular means of deception and persuasion.
Although the motivations for disinformation have remained fairly unchanged,
the Internet and social media nowadays provide low-resource platforms to
disseminate deceitful and inaccurate information more rapidly and broadly than
ever before. The authors who create articles containing disinformation and the
social media users who consequently spread them are key components of
disinformation dissemination. This paper focuses on the (cor)relations between
online news articles and social media users, and examines whether
computational disinformation detection models benefit from leveraging such
correlations.

Figure 1. Overview of the multimodal disinformation detection algorithm. Given
an article, a classifier encodes the article and predicts whether it is ‘true’
or ‘fake’ (in grey). A subset of users who shared the article is encoded
independently (in blue). All parameters are optimized on three training
objectives (in red): discriminate between true and fake news (A), minimize the
cosine distance between the article latent representation and the user latent
representations (B), and minimize the cosine distance between the user latent
representations (C).

The authors’ and users’ motivations for spreading information are twofold:
audience-oriented and self-oriented. Firstly, they both target an ideal
audience when presenting information and aim to elicit certain reactions from
them (audience-oriented motivations). In case of disinformation, authors aim
to deceive and/or persuade their readers. Such audience-oriented motivations
are often reflected in the content and style of disinformation articles:
controversial topics, emotion-provoking words and persuasion rhetorics (Shu et
al., 2017; Przybyla, 2020). Users sharing those articles are not necessarily
driven by the same audience-oriented motivations as the authors. They might
want to simply inform their friends, entertain their followers or even debunk
the information presented in the disinformation article (Giachanou et al.,
2020). Despite their possibly contradicting audience-oriented motivations,
both authors and users aim to construct an online social identity through the
information they create and share (self-oriented motivations). The information
a person generates or shares is said to reflect the meta-image of the self
(Marwick and Boyd, 2011). Considering Twitter, a user’s online identity is
arguably contained in both their tweets (user-generated content) and the
external content they share (user-shared content). Assuming that a user does
not display a plethora of online identities within a single user profile, a
user’s online identity portrayed in their user-generated information (e.g.,
tweets and profile description) should somehow correlate with the identity
reflected in the user-shared article.

In this paper, we leverage correlations between and within article and user
modalities for detecting disinformation. We construct a learning algorithm in
which model parameters are optimized on three training objectives: (1)
discriminate between true and false news articles, (2) minimize the distance
between article and user latent representations, and (3) minimize the distance
between latent representations of users who share the same article (Figure 1).
We apply this learning algorithm to three popular neural text classifiers used
for disinformation detection: CNN (Kim, 2014), HAN (Yang et al., 2016) and
DistilBERT (Sanh et al., 2019). We train and evaluate their performance using
the FakeNewsNet (Shu et al., 2020) and ReCOVery (Zhou et al., 2020) dataset.
Our main contributions can be summarized as follows:

  1. (1)

We design a multimodal learning algorithm for detecting online disinformation
based on observations of human online behaviour and identity made in social
sciences.

  2. (2)

In the algorithm, we propose to explicitly model unimodal and multimodal
correlations by enforcing distance constraints between article and user latent
representations during model training.

  3. (3)

Coordinating the article latent space using a loss function that incorporates
learned user representations is - to our knowledge - new in multimodal
disinformation detection.

  4. (4)

In contrast to existing multimodal disinformation detection models, our
algorithm only leverages user information during model optimization. The
prediction model does not rely on user profiling when predicting an article’s
veracity. This way, we respect the European Commission’s ethics guidelines for
trustworthy AI (on Artificial Intelligence (AI HLEG), 2019).

  5. (5)

Statistical visualization techniques prove that our multimodal learning
algorithm forces the models to learn feature representations that are better
for discriminating between fake and true news.

##  2\. Related Work

In this section, we discuss how previous studies have represented and
leveraged user information for online disinformation detection.

A few disinformation detection tasks purely focus on the users. Those tasks
aim to predict whether a given user is a fake news spreader (Rangel et al.,
2020), or try to distinguish between humans and bots (Ferrara et al., 2016).
In these tasks, users are mainly represented by predefined personality traits
and linguistic properties that are extracted from tweets, often in combination
with supporting metadata (e.g., number of statuses and followers) (Giachanou
et al., 2021; Balestrucci and De Nicola, 2020).

Disinformation detection models predicting the veracity of a given text (e.g.,
news article or tweet) sometimes take users as additional input. Here, users
are represented in terms of their interaction with the text and/or their
connection with other users in the dataset. Kim et al. (2018), for example,
represent each user by a designated numerical identifier (ID) and construct
(user, timestamp, article)-tuples. This way, a model knows not only who
interacted with the articles, but also when and in which sequence the
interactions occurred. Ruchansky et al. (2017) construct similar tuples, but
instead of merely representing users by their ID, they compute a binary
incidence matrix of articles with which the users have engaged before. This
way, users who engaged with the same articles will have similar
representations. However, such approaches lack rich user representations and
merely model users by their interaction with an article. Moreover,
interactions between users remain rather implicit.

Some works include rich user information and describe the type of user-article
interaction. Users are represented by user-generated texts such as their
comment/reply to a news article or tweet (Shu et al., 2019; Qian et al., 2018;
Zhang et al., 2019) and the retweet in which they comment on the original
tweet (Song et al., 2019). Although such user representations contain a user’s
stance and opinion towards an article, users are now merely represented by a
single short text that is related to the article in question. This contrast to
our approach as we represent a user by a larger collection of user-generated
texts (i.e., profile description and tweets) that are not exclusively linked
to a single article.

Finally, other works explicitly model users, user-article interactions and
user-user interactions. They often do this by constructing a heterogenous
graph in which user nodes are linked to both article nodes and other user
nodes in various manners. Nguyen et al. (2020), for example, represent users
by a semantic vector representation of their Twitter profile description. They
then compute a social context graph in which user nodes are connected with
articles nodes using stance edges and other user nodes denoting followership.
Chandra et al. (2020) represent users by the BOW vector over all the articles
they have shared. The user-article edges simply denote that the user has
shared the article while user-user edges show that the users have a follower-
following relation. Although social groups can be arguably deducted from a
user’s explicit social connections, we refrain from modeling such user-user
relations as like-minded people are not necessarily connected to each other on
social media. Moreover, explicit social network connections do not always mean
that the two connected users share common interests: they can merely denote
some social relation such as kinship (Fani et al., 2017). Unlike other popular
social media platforms, the reciprocity level on Twitter is fairly low: less
than one out of four social relations are mutual and about one out of three
users do not have a reciprocal relation with any of the Twitter accounts they
follow (Kwak et al., 2010). This suggests that people more likely use Twitter
as a source of information than a social networking site (Kwak et al., 2010).
We therefore opt to model user-user relations in terms of the common articles
they have shared online, and look for correlations in their generated content.

##  3\. Methodology

We approach disinformation detection as a binary classification task. Given an
online news article $a_{i}\in A$, a disinformation detection model transforms
$a_{i}$ to its latent representation $h(a_{i})$ and predicts whether $a_{i}$
is ‘fake/unreliable’ ($y_{i}=0$) or ‘true/reliable’ ($y_{i}=1$). In this
paper, we leverage user information from Twitter users in the optimization
step. Given a subset of users $U_{i}\subseteq U$ who shared article $a_{i}$ on
Twitter, a separate encoder transforms each user $u_{j}\in U_{i}$ to their
latent representation $h(u_{j})$. During model training, the model is
optimized on three objectives:

  1. (1)

Minimize the cross-entropy between the predicted and ground-truth label
probabilities for $y_{i}$ (= model output).

  2. (2)

Minimize the mean cosine distance between the article representation
$h(a_{i})$ and all user latent representations $h(u_{j})\in U_{i}$.

  3. (3)

Minimize the mean cosine distance between all user latent representations
$h(u_{j})\in U_{i}$.

The three training objectives are combined in a single loss function as a
weighted sum. Both the disinformation detection model and the user encoder are
optimized using the combined loss. As user information is only leveraged in
the loss function during model training, the disinformation detection model
only relies on $a_{i}$ for predicting $y_{i}$ (see Figure 1).

In this section, we first discuss the three neural text classifiers to which
we will apply our multimodal learning algorithm. We then elaborate on the
various training objectives and explain how they are integrated in the
learning algorithm.

###  3.1. Model Architecture

The learning algorithm takes the following features as input:

  1. (1)

$A=\\{(a_{i},y_{i})|0<i\leq N\\}$ is the set of $N$ news articles, where each
article $a_{i}$ is represented by its title $t_{i}$ and body text $b_{i}$, and
labeled with a ground-truth veracity label $y_{i}\in\\{0,1\\}$ (0 =
‘fake/unreliable’, 1 = ‘true/reliable’). Each token in title $t_{i}$ and body
text $b_{i}$ is represented by a one-hot vector that refers to a unique entry
in either the GloVe vocabulary (Pennington et al., 2014) (CNN, HAN) or
DistilBERT vocabulary (Devlin et al., 2019) (DistilBERT)111CNN/HAN: We use the
standard tokenizer from the NLTK toolkit (Bird et al., 2009) and the GloVe 6B
vocabulary of 400k unique tokens with 300-dimensional word embedding
pretrained on Wikipedia 2014 and Gigaword 5 (uncased, 6 billion tokens).
DistilBERT: We adopt the pretrained DistilBERT model and tokenizer
(‘distilbert-base-uncased’) from the Huggingface Transformers library (Wolf et
al., 2020), with a vocabulary of 30,522 unique tokens and 768-dimensional
pretrained word embeddings.. Ultimately, $a_{i}$ is a concatenation of $t_{i}$
and $b_{i}$: $a_{i}=[t_{i};b_{i}]$.

  2. (2)

$U=\\{u_{j}|0<j\leq K\\}$ is the set of $K$ Twitter users that shared at least
one $a_{i}\in A$. Each $u_{j}$ is represented by their profile description
$d_{j}$ and user timeline ${tw}_{j}$. Each token in description $d_{j}$ is
represented by a one-hot vector that refers to a unique entry in either the
GloVe vocabulary (CNN, HAN) or DistilBERT vocabulary (DistilBERT). Analogous
to $d_{j}$, the tokens in a tweet in a user’s timeline $tw_{j}$ are
represented by one-hot vectors. User timeline ${tw}_{j}$ is a concatenation of
the tweet vectors. Ultimately, $d_{j}$ and ${tw}_{j}$ are concatenated to get
user representation $u_{j}$: $u_{j}=[d_{j};{tw}_{j}]$.

  3. (3)

$U_{i}\subseteq U$ is the subset of users who shared the same article $a_{i}$.
User subset $U_{i}$ is represented as a $S{\times}V$ matrix, with $S$ the
number of users who have shared $a_{i}$ and $V$ the number of vector values in
$u_{j}$. For sake of memory usage, we set $S=10$.222We found that increasing
$S$ has little effect on model performance. The $S$ users for each $U_{i}$ are
automatically obtained by taking the user IDs linked to the $S$ lowest tweet
IDs in an article’s tweet ID list. If a user has shared multiple articles in
the dataset, $u_{j}$ can belong to multiple user subsets.

News articles $A$ and Twitter users $U$ are encoded independently but
projected onto the same latent space, i.e., their latent representations have
the same vector dimensions. In the multimodal learning algorithm, $A$ is used
as input to the disinformation detection model while $U$ is leveraged in the
loss function for model optimization.

####  3.1.1. Disinformation Detection Model: Article Encoding and
Classification

The disinformation detection model consists of an article encoder and a single
linear classification layer. The article encoding layer takes as input article
$a_{i}$ and transforms it to a single latent vector representation $h(a_{i})$:

(1) $h(a_{i})=encoding(a_{i})$

We experiment with several text encoding methods that transform a given text
to a single document representation:

  * •

CNN (Kim, 2014): The Convolutional Neural Network (CNN) performs one-level
encoding: word-level encoding using three convolutional layers with a max-
pooling operation. For this model, the length of $a_{i}$ is limited to 500
tokens. The word embedding layer, initialized with pretrained GloVe
embeddings, first transforms each token in $a_{i}$ to its 300-dimensional word
embedding. These are then fed to three separate convolutional layers with
different filter windows (3, 4, 5) with 100 feature maps each and ReLU
activation. Each layer yields a 100-dimensional hidden article representation
which are ultimately concatenated into a single 300-dimensional latent article
representation $h(a_{i})$, as done in (Kim, 2014).

  * •

HAN (Yang et al., 2016): The Hierarchical Attention Network (HAN) performs
two-level encoding: word-level and sentence-level encoding using bidirectional
GRU networks with attention mechanism. To perform two-level encoding, $a_{i}$
is transformed from a one-dimensional vector to a two-dimensional $Z{\times}W$
matrix with $Z$ the maximum number of sentences333We consider title $t_{i}$ as
a single sentence, while body text $b_{i}$ is split into sentences using the
sentence tokenizer from the NLTK toolkit (Bird et al., 2009). For user
encoding (Subsection 3.1.2), $d_{j}$ and each tweet in $tw_{j}$ are considered
as single sentences. (50) and $W$ the maximum number of tokens per sentence
(50). The word embedding layer, initialized with pretrained GloVe embeddings,
first transforms each token in $a_{i}$ to its 300-dimensional word embedding.
A bidirectional GRU with word-level attention and hidden size (50) then
computes $Z$ latent sentence representations, where each sentence
representation is the concatenated 100-dimensional last hidden state of the
bidirectional GRU. Ultimately, a bidirectional GRU with sentence-level
attention and hidden size (50) yields a single 100-dimensional latent article
representation $h(a_{i})$ (as done in (Yang et al., 2016)), which is the
concatenated last hidden state of the bidirectional GRU.

  * •

DistilBERT (Sanh et al., 2019): DistilBERT is a light Transformer model based
on the BERT architecture (Devlin et al., 2019). For this model, the length of
$a_{i}$ is limited to 512 (= maximum input dimension of DistilBERT). The
embedding layer transforms each token in $a_{i}$ to its 768-dimensional word
embedding. These are then sent through five Transformer blocks with multihead
self-attention. The last hidden states are max-pooled, resulting in a
768-dimensional latent article representation $h(a_{i})$, as done in (Sanh et
al., 2019).

The latent article representation $h(a_{i})$ is ultimately sent through a
single linear layer with softmax activation:

(2) $p_{i}=softmax(linear(h(a_{i})))$

with $p_{i}$ the probability distribution over all classification labels.

####  3.1.2. User Encoding

The user encoder takes as input user subset $U_{i}$ and transforms each
$u_{j}\in U_{i}$ to its latent user representation $h(u_{j})$:

(3) $h(u_{j})=encoding(u_{j})$

We use the same preprocessing steps and text encoding methods for $u_{j}$ as
for $a_{i}$. During training, we adopt the same encoding architecture for both
the article and user encoder: CNN article encoder + CNN user encoder, HAN
article encoder + HAN user encoder, DistilBERT article encoder + DistilBERT
user encoder. The resulting latent user representations $h(u_{j})$ are
combined in a $S{\times}L$ matrix:

(4) $h(U_{i})=[h(u_{1});...;h(u_{j})]$

with $S$ the number of users in $U_{i}$ and $L$ the dimension of $h(u_{j})$:
$L$ = 300 (CNN), $L$ = 100 (HAN), $L$ = 768 (DistilBERT). The latent user
representation matrix $h(U_{i})$ is eventually used as input to the loss
function.

###  3.2. Training Objectives

Figure 2. Overview of the training objectives. Given a set of articles (top
left), a disinformation detection model should be able to discriminate between
true and fake articles (Objective A, bottom left); this is a common
classification objective. In our learning algorithm, the model is additionally
forced to project not only the article and the users who shared it (Objective
B), but also the users who shared the same article (Objective C) close
together in the latent space (top right$\xrightarrow{}$bottom right).

In the multimodal learning algorithm, both the disinformation detection model
and user encoder parameters are optimized on the following objectives (Figure
2):

  * (A)

The model should be able to discriminate between true and fake news articles.

  * (B)

The model should project articles and the users who shared them closely to
each other in the latent space.

  * (C)

The model should project the users who shared the same article closely to each
other in the latent space.

We assign a dedicated loss function for each training objective and combine
the losses in a single loss function as a weighted sum.

##### Objective A: Discriminate between true and fake articles

A discriminative loss on the ground-truth and predicted labels is commonly
used to optimize a classifier’s parameters. We opt for the cross-entropy loss
on the veracity prediction output, which is the probability distribution over
the two labels yielded by a softmax function (= model output):

(5) $\mathcal{L}_{pred}=-\sum\limits_{i}y_{i}\log\hat{y}_{i}$

where $y_{i}\in\\{0,1\\}$ is the ground-truth label for article $a_{i}$, and
$\hat{y}_{i}$ is the predicted probability for $y_{i}$. By minimizing this
prediction loss, the model is encouraged to look for patterns that
discriminate between true and false articles, while it simultaneously learns
patterns within each class. We provide baselines that are solely optimized on
this prediction loss (base).

##### Objective B: Find correlations between an article and the users who
shared it

We minimize the distance between an article’s latent representation $h(a_{i})$
and the latent representations of the users who shared that article
$h(U_{i})$. We use the cosine distance, as computed in Kocher and Savoy
(2017), as distance measure. The article-user distance loss computes the
arithmetic mean over the cosine distance between $h(a_{i})$ and each
$h(u_{j})$ element of $h(U_{i})$. If $a_{i}$ does not have a user subset
$U_{i}$, or $U_{i}=\emptyset$, the article-user distance loss is set to $0$.

(6)
$\mathcal{L}_{dist(a,U)}=\begin{cases}\frac{1}{|U_{i}|}\sum\limits_{j=1}^{|U_{i}|}dist_{cosine}(h(a_{i}),h(u_{j}))&\text{if
}U_{i}\neq\emptyset\\\ 0&\text{otherwise}\end{cases}$

where $L$ is number of vector values for $h(a_{i})$ and $h(u_{j})$. By
optimizing the model on the article-user distance loss, the model learns
correlations between the article and its audience.

##### Objective C: Find correlations between users who shared the same article

We minimize the distance between the latent representations of all users
$h(u_{j})$ within the same user subset $h(U_{i})$. Given user representation
matrix $h(U_{i})$, the user-user distance loss first computes a distance
matrix $\mathbf{D}$ where $d_{ij}$ is the cosine distance between $h(u_{i})$
and $h(u_{j})$.

(7) $\mathbf{D}=\begin{bmatrix}d_{11}&d_{12}&\cdots&d_{1S}\\\
d_{21}&d_{22}&\cdots&d_{2S}\\\ \vdots&\vdots&\ddots&\vdots\\\
d_{S1}&d_{S2}&\cdots&d_{SS}\\\ \end{bmatrix}=(d_{ij})\in\mathbb{R}^{S\times
S}$

with $S$ the length of user subset $U_{i}$. Then, the average of each matrix
column is calculated - leaving out a user’s distance to itself ($=d_{ii}$).
This results in distance vector $d=[d_{1},d_{2},...,d_{S}]$, in which $d_{i}$
denotes the average cosine distance between $h(u_{i})$ and all other
$h(u_{j})$ element of $h(U_{i})$. Finally, the user-user distance loss is
obtained by computing the arithmetic mean over $d_{i}$. If $a_{i}$ does not
have a user subset $U_{i}$, or $U_{i}=\emptyset$, the user-user distance loss
is set to $0$.

(8)
$\mathcal{L}_{dist(U)}=\begin{cases}\frac{1}{|U_{i}|}\sum\limits_{j=1}^{|U_{i}|}dist(h(u_{j}),h(U_{i}))&\text{if
}U\neq\emptyset\\\ 0&\text{otherwise}\end{cases}$ (9)
$dist(h(u_{j}),h(U_{i}))=\frac{1}{|U_{i}|-1}\sum\limits_{k=1}^{|U_{i}|}dist_{cosine}(h(u_{j}),h(u_{k}))$

By optimizing the model on the user-user distance loss, the model learns
correlations between the users who shared the same news article.

##### Combined loss

The combined loss is a weighted sum over the prediction loss
$\mathcal{L}_{pred}$, the article-user distance loss $\mathcal{L}_{dist(a,U)}$
and the user-user distance loss $\mathcal{L}_{dist(U)}$:

(10)
$\mathcal{L}=\lambda_{1}\mathcal{L}_{pred}+\lambda_{2}\mathcal{L}_{dist(a,U)}+\lambda_{3}\mathcal{L}_{dist(U)}$

where $\lambda_{1}$, $\lambda_{2}$ and $\lambda_{3}$ sum to 1. During
training, the data is fed to the model in batches, and the disinformation
detection model and user encoder parameters are optimized using the mean batch
loss after each forward pass.

##  4\. Experiments

###  4.1. Data

##### FakeNewsNet

(Shu et al., 2020). The FakeNewsNet dataset comprises news articles from two
fact-check domains: Politifact and Gossipcop. The articles are labeled as
‘fake’ (0) or ‘true’ (1). For each article, the dataset provides a URL to the
original article and a list of tweet IDs that shared that specific article. We
use the download script provided by the authors to crawl the articles (title
and body text), tweet IDs (metadata) and user timelines (content and metadata
of max. 200 tweets). In total, 568 Politifact and 16,963 GossipCop complete
articles could be automatically crawled. To obtain the user subsets $U_{i}$
for each article $a_{i}$, we extract the user IDs from the tweets’ metadata in
the dedicated tweet ID list, link each user ID with its user timeline, extract
the timeline’s tweet content ($tw_{j}$) and take the user profile description
from the metadata of the most recent tweet in the user timeline ($d_{j}$). As
we can extract for each article the complete user information of at least one
Twitter user, there are no empty user subsets $U_{i}$ for $Politifact$ and
$GossipCop$ articles during training. We regard Politifact and Gossipcop as
two distinct datasets, as they greatly differ in scope: Politifact focuses on
political news while GossipCop mainly verifies gossip news.

##### ReCOVery

(Zhou et al., 2020). The ReCOVery dataset contains 2,029 news articles about
COVID-19 that are labeled as ‘unreliable’ (0) or ‘reliable’ (1). As these
labels are similar to the ‘fake’ (0) and ‘true’ (1) labels in the other two
datasets, we consider them synonymous. The dataset provides the full articles
(title and body text) and a list of tweet IDs for each article. Given the
tweet ID lists, we use the Twitter API to crawl the tweets’ metadata, the user
IDs and the user timelines (content and metadata). We obtain the user subset
$U_{i}$ for each article $a_{i}$ in the same way as done for Politifact and
GossipCop. However, only 133 articles have non-empty user subsets.

Domain | True/Reliable | Fake/Unreliable | Total
---|---|---|---
Politifact | 248 | 320 | 568
Gossipcop | 12,904 | 4,059 | 16,963
ReCOVery | 1,364 | 665 | 2,029
Total | 14,516 | 5,044 | 19,560
Table 1. Overview of the three datasets.

###  4.2. Experimental Setup

As the Politifact and ReCOVery dataset do not contain a high number of data
samples, we combine the three datasets in one dataset. We split the combined
dataset in a train (80%), validation (10%) and test (10%) set in a label-
stratified manner (random seed = 42). Although we train and validate on the
combined dataset, we report performance results for each dataset individually.
The results are obtained in terms of recall, precision and F1. During model
training, the data is fed in batches of 32 (CNN/HAN) or 8 (DistilBERT). We
investigate four experimental setups:

  1. (1)

base: User information is not leveraged. The model parameters are optimized
using only the prediction loss $\mathcal{L}_{pred}$.

  2. (2)

+u/d: Users are represented by their profile description ($d_{j}$). Both the
model and user encoder parameters are optimized using $\mathcal{L}$, which is
the weighted sum of $\mathcal{L}_{pred}$, $\mathcal{L}_{dist(a,U)}$ and
$\mathcal{L}_{dist(U)}$.

  3. (3)

+u/t: Users are represented by their tweets ($tw_{j}$). Both model and user
encoder parameters are optimized using $\mathcal{L}$.

  4. (4)

+u/d+t: Users are represented by both their profile description ($d_{j}$) and
tweets ($tw_{j}$). Both the model and user encoder parameters are optimized
using $\mathcal{L}$.

The Adam optimization algorithm (learning rate = 1e-4) optimizes the
parameters after every forward pass. We perform early stopping on the
validation loss with patience (7).

To decide the $\lambda_{1}$, $\lambda_{2}$ and $\lambda_{3}$-values in
$\mathcal{L}$ for each model, we experiment with different value combinations
for CNN+u/d, HAN+u/d and DistilBERT+u/d. We additionally tested whether or not
including both $\mathcal{L}_{dist(a,U)}$ and $\mathcal{L}_{dist(U)}$ yields
higher model performance than simply including one of the two losses by
setting either $\lambda_{2}$ or $\lambda_{3}$ to zero. Based on the validation
set, we found the following optimal [$\lambda_{1}$, $\lambda_{2}$,
$\lambda_{3}$]-values: [0.8, 0.1, 0.1] (CNN), [0.5, 0.25, 0.25] (HAN), [0.33,
0.33, 0.33] (DistilBERT). This shows that both correlation losses contribute
to

###  4.3. Results

Table 2 and Table 3 show the performance results in terms of precision (P),
recall (R) and F1-score (F1) for the fake/unreliable and true/reliable label,
respectively. We opt for these performance metrics as the two classes are
rather unbalanced. The results show that leveraging user information in a
multimodal learning algorithm positively influences the model performance for
all three neural classifiers and datasets.

For the fake class (Table 2), the user setup with only user profile
descriptions ($+u/d$) has the highest positive impact when predicting
Politifact articles: +3.78/+9.09/+9.19% (P/R/F1) for the CNN model, and
+6.02/(-3.03)/+0.35% (P/R/F1) for the DistilBERT model. When classifying fake
GossipCop and ReCOVery articles, the models benefit from the multimodal
learning algorithm the most when users are (partly) represented by their
tweets. For example, the DistilBERT model returns the highest GossipCop
results with the $+u/d+t$ setup ( -1.21/+4.68/+2.87%; P/R/F1), while the CNN
model achieves its highest ReCOVery prediction performance with the $+u/t$
setup (+3.19/+3.01/+3.12%; P/R/F1). The multimodal learning algorithm has a
rather limited positive impact on the HAN prediction performance across all
three datasets.

For the true class (Table 3), we observe similar user setup preferences as for
the fake class. For the true Politifact articles, the models mainly benefit
from the user setup with only profile descriptions ($+u/d$):
+3.66/(+0.00)/+2.90% (P/R/F1) for the CNN model, and +0.19/+8.00/+3.14%
(P/R/F1) for the DistilBERT model. When predicting true GossipCop and ReCOVery
articles, the user setups that represent each user by their tweets (and
profile description) yield the highest performance results. For example,
DistilBERT’s performance increases by +1.10/(-0.78)/+0.24% (P/R/F1) for
GossipCop articles with the $+u/d+t$ setup. For ReCOVery articles, CNN
performance rises by +3.19/3.01/3.12% (P/R/F1) with the $+u/t$ setup while HAN
performance modestly increases by +0.15/1.50/0.85% (P/R/F1) with the $+u/d+t$
setup.

|  | Politifact | GossipCop | ReCOVery
---|---|---|---|---
|  | P | R | F1 | P | R | F1 | P | R | F1
CNN | base | .7857 | .3333 | .4681 | .7951 | .5542 | .6531 | .7872 | .5606 | .6549
+u/d | .8235 | .4242 | .5600 | .7882 | .5591 | .6542 | .7959 | .5909 | .6783
+u/t | .7778 | .4242 | .5490 | .7633 | .5640 | .6487 | .8750 | .6364 | .7368
+u/d+t | .7647 | .3939 | .5200 | .7813 | .5542 | .6484 | .8478 | .5909 | .6964
HAN | base | .8214 | .6970 | .7541 | .6684 | .6502 | .6592 | .7500 | .8182 | .7826
+u/d | .7778 | .6364 | .7000 | .6855 | .6281 | .6555 | .7571 | .8030 | .7794
+u/t | .7778 | .6364 | .7000 | .6855 | .6281 | .6555 | .7571 | .8030 | .7794
+u/d+t | .7857 | .6667 | .7213 | .6569 | .6650 | .6610 | .7714 | .8182 | .7941
DistilBERT | base | .8148 | .6667 | .7333 | .7845 | .5468 | .6444 | .7463 | .7576 | .7519
+u/d | .8750 | .6364 | .7368 | .8045 | .5271 | .6369 | .7347 | .5455 | .6261
+u/t | .7619 | .4848 | .5926 | .7786 | .5025 | .6108 | .7000 | .6364 | .6667
+u/d+t | .8000 | .6061 | .6897 | .7724 | .5936 | .6731 | .8421 | .7273 | .7805
Table 2. Performance results for the fake class: precision (P), recall (R) and F1-score (F1). The underlined results indicate that the user-constrained model outperforms the corresponding $base$ model on that performance metric. The highest results for each model architecture (CNN, HAN, DistilBERT) are marked in bold. |  | Politifact | GossipCop | ReCOVery
---|---|---|---|---
|  | P | R | F1 | P | R | F1 | P | R | F1
CNN | base | .5000 | .8800 | .6377 | .8722 | .9552 | .9118 | .8092 | .9248 | .8632
+u/d | .5366 | .8800 | .6667 | .8732 | .9529 | .9113 | .8200 | .9248 | .8693
+u/t | .5250 | .8400 | .6462 | .8736 | .9451 | .9079 | .8411 | .9549 | .8944
+u/d+t | .5122 | .8400 | .6364 | .8718 | .9513 | .9098 | .8235 | .9474 | .8811
HAN | base | .6667 | .8000 | .7273 | .8912 | .8988 | .8950 | .9055 | .8647 | .8846
+u/d | .6129 | .7600 | .6786 | .8863 | .9096 | .8978 | .8992 | .8722 | .8855
+u/t | .6129 | .7600 | .6786 | .8863 | .9096 | .8978 | .8992 | .8722 | .8855
+u/d+t | .6333 | .7600 | .6909 | .8945 | .8910 | .8928 | .9070 | .8797 | .8931
DistilBERT | base | .6452 | .8000 | .7143 | .8701 | .9529 | .9096 | .8788 | .8722 | .8755
+u/d | .6471 | .8800 | .7457 | .8661 | .9598 | .9106 | .8000 | .9023 | .8481
+u/t | .5405 | .8000 | .6452 | .8595 | .9552 | .9048 | .8273 | .8647 | .8456
+u/d+t | .6061 | .8000 | .6897 | .8811 | .9451 | .9120 | .8732 | .9323 | .9018
Table 3. Performance results for the true class: precision (P), recall (R) and
F1-score (F1). The underlined results indicate that the user-constrained model
outperforms the corresponding $base$ model on that performance metric. The
highest performance results for each model architecture (CNN, HAN, DistilBERT)
are marked in bold.

##  5\. Discussion

We start with a general discussion of the performance results and continue to
investigate the following research questions:

  1. (1)

Does leveraging user information lead to better latent article
representations?

  2. (2)

To which extent does user and tweet selection influence model performance?

  3. (3)

Does the model indeed find and leverage user-article and user-user
correlations?

###  5.1. General

In terms of the performance metrics, the impact of the multimodal learning
algorithm appears to be greater for the smaller datasets (Politifact and
ReCOVery) than for the largest dataset (GossipCop). As the models are trained
on all three datasets at the same time, the base model might have learned to
discriminate between true and fake gossip news better than between more
political or COVID-related news. This seems to suggest that leveraging
additional user information adds little value to the prediction performance
for GossipCop articles. Nonetheless, the multimodal learning algorithm might
have influenced the models’ confidence about their predictions. We therefore
evaluate whether the models yield significantly higher prediction
probabilities for GossipCop articles when leveraging user information. For
each GossipCop article in the test set, we take the computed probability for
the predicted label yielded by the model’s softmax layer. This probability
measure lies between 0.5 and 1, as the model outputs the label with highest
probability. We then perform a two-sample T-test and Kruskal-Wallis H-test
between the GossipCop prediction probabilities yielded by the $base$ model and
those yielded by the model optimized using one of the three user setups
($+u/d$, $+u/t$, $+u/d+t$). Figure 3 shows the distribution of the prediction
probabilities yielded by each DistilBERT model. Both statistical tests confirm
that the prediction confidence significantly differs between DistilBERTbase
($\mu$ = 0.861697, $\sigma^{2}$ = 0.018571) and DistilBERT+u/t ($\mu$ =
0.847578, $\sigma^{2}$ = 0.014585) [T = 3.196966, $p$ ¡ 0.01; H = 64.073104,
$p$ ¡ 0.01], and DistilBERTbase and DistilBERT+u/d+t ($\mu$ = 0.891369,
$\sigma^{2}$ = 0.015190)[T = -6.658362, $p$ ¡ 0.01; H = 33.668810, $p$ ¡
0.01]. Only the Kruskal-Wallis H-test rejects the null hypothesis for
DistilBERTbase and DistilBERT+u/d ($\mu$ = 0.859106, $\sigma^{2}$ = 0.014207)
[H = 23.783539, $p$ ¡ 0.01]. The distributions depicted in Figure 3 show that
DistilBERT+u/d+t yields the statistically highest probabilities on average.
The statistical tests confirm the same for the HAN model, but are inconclusive
for the CNN model.

Figure 3. Distribution of the predicted probabilities yielded by the
DistilBERT model for GossipCop articles in the test set. The blue horizontal
lines indicate the minimum, mean and maximum probability value in the
distribution.

###  5.2. Influence on Article Latent Space (RQ1)

(a) CNNbase ($\bar{\omega}=0.5592$)

(b) CNN+u/d ($\bar{\omega}=0.3897$)

(c) HANbase ($\bar{\omega}=0.2635$)

(d) HAN+u/d ($\bar{\omega}=0.2081$)

(e) DistilBERTbase ($\bar{\omega}=0.0432$)

(f) DistilBERT+u/d ($\bar{\omega}=0.0807$)

Figure 4. Statistical analysis: multidimensional scaling based on cosine
similarity for GossipCop latent article representations (test set). The
average overlap between the two groups is quantified by $\bar{\omega}$.

The multimodal learning algorithm constrains the classification parameters by
enforcing a minimized cosine distance between latent article and user
representations and a minimized cosine distance between latent user
representations during training. We analyze how and to which extent these
user-related constraints influence the article latent space.

The effect of the latent space choice in our experimental settings can be
appreciated using some established dimension reduction techniques. We
illustrate here two possible approaches: multidimensional scaling (Cox and
Cox, 1994) and principle component analysis (PCA) (Hubert et al., 2005, 2009).
In both cases, we applied a robust variant of the methodologies to avoid that
results are distorted by the presence of outliers and deviations from
canonical distributional assumptions. In the case of multidimensional scaling,
the panels of Figure 4 show a two-dimensional representation of the $n$ rows
of the latent space, where the Euclidean distances between them approximate a
monotonic transformation of the corresponding $n\times n$ cosine similarities
computed in the original space. Robustness is ensured by applying Huber’s
weighting in the estimation, using MATLAB function mdscale with option
statset(’Robust’,’on’, ’RobustWgtFun’,’huber’).

(a) CNN: base ($\bar{\omega}=0.7700$)

(b) CNN: +u/d ($\bar{\omega}=0.3966$)

Figure 5. Statistical analysis: scores of the three main principle components.
Validation set, GossipCop. The average overlap between the two groups is
quantified by $\bar{\omega}$.

The robust PCA is applied using function robpca of the LIBRA toolbox (Verboven
and Hubert, 2010). We also used additional monitoring features of the FSDA
toolbox that allow removing an appropriate percentage of outliers from the
analysis and study the fine-grained structure of the data (number of groups)
(Riani et al., 2012; Torti et al., 2021). 444The MATLAB code used for this
part of the data analysis is available upon request.

Figures 4 and 5 show how the true (in blue) and fake (in red) classes are
projected in the two sub-space representations. It is clear from these
examples that implementing our multimodal learning algorithm increases the
separation between the two prediction classes in the article latent space.
Maitra and Melnykov (2010) provide a possible approach to quantify this
separation, with an overlap measure that expresses the probability of miss-
classification assuming a Gaussian mixture model as data generating process.
We computed this measure using the FSDA overlap function described in Riani et
al. (2015), which is reported in the caption of the figures with
$\bar{\omega}$. Note that the measure is quite reliable in the PC
representations of Figure 5, where the Gaussian mixture is clearly
appropriate, while in some cases the multidimensional scaling (Figure 4)
produces groups that are quite skewed (e.g., panel 4(e)) and this might bias
the proposed separation estimate. In sum, the statistical visualization
techniques and overlap measures show that our multimodal learning algorithm
forces the classification models to learn feature representations that better
discriminate between fake and true news texts.

###  5.3. User and Tweet Selection (RQ2)

We assess to which extent the selection of both users and tweets in the user
subsets influence model performance.

We start with the user selection. In the above experiments (Section 4), the
$S$ (= 10) user IDs linked to the lowest tweet IDs in an article’s tweet ID
list are automatically included in the article’s user subset. This way, each
user subset contains users who were among the firsts to share the article on
Twitter. In other words, the user subset reflects the audience of the
article’s early dissemination process. We investigate to which extent model
performance changes when using users from the articles’ late dissemination
process. For this, we select the user IDs linked to the highest tweet IDs in
an article’s tweet ID list and train the models with the new user subsets.
Model performance is conjectured to be higher with the original, early
dissemination subset than with the new, late dissemination subset. We expect
that the correlation between an article and the early dissemination users is
higher, as it is more likely that the article’s author and news outlet are
among the first to spread their articles on social media. For brevity, we only
show and discuss the results for the CNN model (Figure 6; orange bars).

The late dissemination user subset raises model performance for the Politifact
and ReCOVery dataset when using their description (CNN+u/d) and both
description and tweets (CNN+u/d+t). Model performance remains more or less the
same for the GossipCop dataset with all user setups. The results with the new
user subsets thus rejects our hypothesis that leveraging early dissemination
users leads to higher performance increases.

Figure 6. Impact of altered user (in orange) and tweet (in green) selection
during training on the F1-score for the fake (left figure) and true (right
figure) class in the test set: CNNbase (in grey), CNN+u/d, CNN+u/t and
CNN+u/d+t (in blue).

Not only the user selection, but also the tweet selection might influence
model performance. Currently, the most recent tweets in a user’s timeline are
taken into consideration in the $+u/t$ and $+u/d+t$ setups. Upon investigating
the time span of user timeline of 10,000 randomly selected users from the user
subsets, we found that more than half of the timelines span max. three months,
and less than one in four have a time span of more than a year. By taking the
oldest tweets from a user’s timeline instead of the newest tweets, the user
representation will reflect a slightly older user identity that may correlate
more with the earlier spread articles. Given the articles’ publication date in
the datasets (i.e., Politifact: 2008-2018, GossipCop: 2017-2018, reCOVery:
2020), we expect that the models’ prediction performance for reCOVery articles
will increase as the oldest tweets in the user timelines might cover more
diverse COVID-related topics. For the other two datasets, on the other hand,
performance should not be affected too much. The results for the CNN model
(Figure 6; green bars) confirm our hypotheses: the model performs consistently
better for ReCOVery articles, especially for the fake class, while its
performance for the other two datasets remains rather stable. This suggests
that leveraging user information from the same time period as the article that
is evaluated improves model performance.

###  5.4. Correlations: Experimental Analysis (RQ3)

When implementing the multimodal learning algorithm, does the model learn more
discriminative article features because there are indeed correlations between
the user-generated content and the news article content, and between the user-
generated content of the users who shared the same article, or simply because
there is more data? To investigate this, we first pair each article with
another, randomly chosen user subset (random seed = 42). In this first
experiment (Random Subset), the users contained within the same user subset
remain unchanged, but they are linked to another news article. This way, we
distort possible user-article correlations while maintaining possible
correlations between users in the same user subset. We expect that model
performance will drop below base model performance, as incorrect user-article
correlations will be learned. In a second experiment (Random Subset +
Composition), we take it a step further and also change the composition of the
user subsets by randomly assigning a user to a different user subset (random
seed = 42). We expect that this experimental setup will yield the lowest
performance results as both the article-user and user-user correlations are
distorted.

Figure 7 reports the F1-score for the CNN models for the Random Subset
experiment (orange bars) and the Random Subset + Composition experiment (green
bars). Overall, the Random Subset experiment yields counter-intuitively higher
results than the original user-constrained CNN models (CNN+u/d, CNN+u/t,
CNN+u/d+t). Although the Random Subset + Composition experiment lowers the
performance of those user-constrained models, they still perform better than
the CNNbase model. It can thus be argued that the user-constrained models
benefit from leveraging extra user data by having distance constraints between
and within the two modalities enforced during model optimization without
actually uncovering the assumed real-world correlations constructed by the
user modality.

Figure 7. Impact of distorting the user-article correlations (Random Subset;
in orange), and distorting both user-article and user-user correlations
(Random Subset + Composition; in green) during training on the F1-score for
the fake class (left figure) and true class (right figure) in the test set:
CNNbase (in grey), CNN+u/d, CNN+u/t and CNN+u/d+t (in blue).

###  5.5. Correlations: Qualitative Analysis (RQ3)

In this brief qualitative analysis, we try to check whether we as humans can
detect correlations between the news article text and the user-generated
content (i.e., profile description and tweets) that possibly guide a model
towards the correct prediction label. We randomly select 100 samples from the
training set (random seed = 42) with the predicted label yielded by the HAN
models and focus on cases where the models do not agree on the label. Figure 8
provides such a case. In that example, the HAN+u/d predicts the ground-truth
true label whereas the other models assign similar high probabilities to the
incorrect fake label. It could be argued that the presence of similar topics
in the news article and profile descriptions (i.e., music) guides the model
towards the correct prediction label. Those topics are also found in the tweet
timelines, but both HAN+u/t and HAN+u/d+t predict the opposing label with a
high probability nevertheless. The tweets discuss a wide variety of topics
and, therefore, introduce additional noise in the user representations. That
noise seems to eclipse the valuable information contained in the user
descriptions in HAN+u/d+t. We observe similar patterns in other training
samples. Given that user-generated content is inherently noisy, future work
can look into various filtering techniques to distinguish useful and relevant
information from highly noisy and irrelevant Twitter content before leveraging
it in a multimodal learning algorithm.

Figure 8. Example from the GossipCop dataset (training set) with output from
the HAN models: HANbase, HAN+u/d, HAN+u/t and HAN+u/d+t.

###  5.6. Limitations

Fake news articles and creators tend to quickly disappear from the Internet
(Allcott and Gentzkow, 2017). As a result, not all fake news articles and
profiles can be extracted, leading to a reduced share of fake news articles in
the datasets and possibly incomplete Twitter dissemination processes. The
datasets will thus become obsolete in the course of time. Moreover, our
approach relies on user-generated Twitter profile descriptions and tweets from
user timelines. A user can alter their description and generate new tweets
after sharing the article that is evaluated. As time passes by, a user’s
identity reflected in their current description and latest tweets might no
longer align with their identity at the time of sharing the article.

##  6\. Conclusion

This paper introduced a novel, multimodal learning algorithm that allows a
disinformation detection model to learn features that discriminate better
between true and false news using user-generated content on Twitter. It
provides an elegant way to integrate and correlate multimodal information
without requiring its presence at testing time. Further research can build on
this approach and experiment with other modalities such as images and audio.
Furthermore, the statistically robust approach we proposed to check the effect
of the user-related constraints on the article latent space has a wider
potential: it can be applied to understand the fine-grained structure of the
data used for training or validating models, and to clean them if necessary.
As it can avoid introducing disturbances in the model estimates, this would
arguably lead to better generalization performances.

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