# Enhancing Fake News Detection in Social Media via Label Propagation on
Cross-modal Tweet Graph

Wanqing Zhao  Northwest UniversityChina<EMAIL_ADDRESS>,  Yuta Nakashima
Osaka UniversityJapan<EMAIL_ADDRESS>,  Haiyuan Chen  Northwest
UniversityChina<EMAIL_ADDRESS>and  Noboru Babaguchi  Osaka
UniversityJapan<EMAIL_ADDRESS>
(2023)

###### Abstract.

Fake news detection in social media has become increasingly important due to
the rapid proliferation of personal media channels and the consequential
dissemination of misleading information. Existing methods, which primarily
rely on multimodal features and graph-based techniques, have shown promising
performance in detecting fake news. However, they still face a limitation,
i.e., sparsity in graph connections, which hinders capturing possible
interactions among tweets. This challenge has motivated us to explore a novel
method that densifies the graph’s connectivity to capture denser interaction
better. Our method constructs a cross-modal tweet graph using CLIP, which
encodes images and text into a unified space, allowing us to extract potential
connections based on similarities in text and images. We then design a Feature
Contextualization Network with Label Propagation (FCN-LP) to model the
interaction among tweets as well as positive or negative correlations between
predicted labels of connected tweets. The propagated labels from the graph are
weighted and aggregated for the final detection. To enhance the model’s
generalization ability to unseen events, we introduce a domain generalization
loss that ensures consistent features between tweets on seen and unseen
events. We use three publicly available fake news datasets, Twitter, PHEME,
and Weibo, for evaluation. Our method consistently improves the performance
over the state-of-the-art methods on all benchmark datasets and effectively
demonstrates its aptitude for generalizing fake news detection in social
media.

Fake News Detection, Cross-modal Graphs, Label Propagation

††journalyear: 2023††copyright: acmlicensed††conference: Proceedings of the
31st ACM International Conference on Multimedia; October 29-November 3, 2023;
Ottawa, ON, Canada††booktitle: Proceedings of the 31st ACM International
Conference on Multimedia (MM ’23), October 29-November 3, 2023, Ottawa, ON,
Canada††price: 15.00††doi: 10.1145/3581783.3612086††isbn:
979-8-4007-0108-5/23/10††ccs: Computing methodologies Classification and
regression trees††ccs: Theory of computation Social networks

##  1\. Introduction

The proliferation of social media has given rise to an abundance of non-
official personal media channels that allow for the dissemination of news and
opinions. Due to the absence of rigorous editorial oversight, such brand-new
media has engendered a concomitant dissemination of fake news, which poses the
potential to mislead readers and carry deleterious social consequences. Fake
news may manipulate images and texts from various sources; generative AI (Cao
et al., 2023) even allows the generation of fake news from scratch by
fabricating high-quality and specious images and text (Brown et al., 2020;
Ramesh et al., 2021), making it hard for readers to distinguish them from
genuine ones.

Figure 1.  By utilizing cross-modal search, we can construct a more
comprehensive connection to relevant tweets that may contain potential
positive and negative correlations. These connections can be exploited to
enable more credible detection of fake news.

Recent widespread access to new technologies makes automated detection of fake
news imperative. To effectively identify fake news and mitigate its adverse
effects, it is crucial to incorporate multimodal information, including both
text and images. This allows for a more comprehensive analysis of social media
tweets to improve the reliability of fake news detection. Several multimodal
fake news detection approaches have been proposed, which fuse features from
different modalities to achieve more accurate detection than text-only
approaches (Singhal et al., 2019; Qian et al., 2021; Zhou et al., 2020b). Even
with the fusion of images and text for detection, relying on a single tweet to
determine the veracity of the original event can be one-sided and unreliable.

Some works have attempted to leverage explicit social context to build links
between tweets, e.g., building a graph based on retweets and hashtags on
tweets and using Graph Neural Networks (GNNs) (Zhou et al., 2020a) to model
the relationships among them (Yang et al., 2021; Wei et al., 2021; Zheng et
al., 2022). With the help of GNNs, neighboring tweets can be aggregated to
obtain a more comprehensive representation.

Graph construction solely based on social context may still be sparse. For
example, multiple users can author an original tweet message with their own
images on a single event, so their tweets cannot be in the tweet-retweet
relationship. Also, the users do not necessarily use the same hashtags.
Consequently, tweets on a single event may not always be connected to each
other based on the social context. GNNs typically model interactions only
between connecting tweets, and such sparse connections hinder capturing
possible interactions among tweets. This sparsity can be problematic because
the veracity of a certain tweet may stand only when it is compared with
others. This observation inspires us to densify the connection between tweets
based on the content of tweets.

An impactful vision-and-language model, CLIP (Radford et al., 2021), has
recently been proposed to encode images and text into a unified space. CLIP’s
strong zero-shot capabilities for several tasks inspire us to leverage it for
connecting related tweets in social media based on similarities in their text
and images to densify the graph. As depicted in Figure 1, a tweet can be
utilized to retrieve relevant tweets using features obtained from CLIP through
image-to-image or image-to-text search. Similarly, text-to-text or text-to-
image search can be performed. We can then add an edge between a pair of
relevant tweets. Such a densified graph capture interactions among more tweets
by contextualizing features with connecting tweets.

One interesting extension of this idea is to use the same graph for label
propagation. Predictions for a pair of connecting tweets can have a certain
correlation as their content is similar. This correlation, however, can be
both positive and negative. As shown in Figure 1, a pair of semantically
similar tweets can have opposite labels as real and fake news on the same
event can share most of their content (e.g., tweets may come with very similar
images of the same scene but their text messages can be different). Therefore,
label propagation over our densified graph should account for this.

Based on the above motivations, we propose a method for detecting fake news on
social media by leveraging a cross-modal tweet graph to model deeper
interaction behind different tweets. We construct a cross-modal tweet graph by
utilizing CLIP to extract potential connections based on similarities in text
and images. We then design a Feature Contextualization Network with Label
Propagation (FCN-LP). Feature Contextualization Network (FCN) learns the
interaction between tweets over the graph, while Label Propagation (LP)
incorporate the contextualized features to model both positive and negative
correlations of predicted labels by assigning signed attention to edges. The
propagated labels from the graph are then weighted and aggregated for the
final detection. To improve the model’s generalization performance, we
introduce a domain generalization loss that ensures consistent features
between seen and unseen tweets.

Contributions. Our experiments show consistent performance improvement with
FCN-LP over the state-of-the-art methods over three different fake news
detection datasets. This means that CLIP-based graph construction successfully
captures underlying structures of real and fake news tweets of unseen events.
FCN further enhances the separation between real and fake news tweets. This
implies that the FCN features encode the difference among them. We
qualitatively confirm that LP can model positive and negative correlations
from the FCN features. Our domain generalization loss is designed to match the
distribution of tweets on unseen tweets (i.e., ones not used for training FCN)
to the distribution of seen tweets. Given the performance boost, we would say
that this loss consequently leads to features invariant to events.

Figure 2. An overview of FCN-LP for fake news detection.

##  2\. Related Works

Fake news detection is often treated as a binary classification problem.
Earlier approaches used features extracted from text content to train a fake
news classifier (Castillo et al., 2011; Popat, 2017). These methods require
expensive pre-processing and tricky feature engineering. Deep neural networks
(DNNs) can build an end-to-end model from the original text to the classifier
(Yu et al., 2017; Shu et al., 2017). Social network posts often consist of
multimodal data, such as images and text, to make the post more informative.
This abundance of data inspires researchers to propose multimodal approaches
(Singhal et al., 2019; Qian et al., 2021; Zhou et al., 2020b), providing more
comprehensive classification results. Moreover, some graph-based fake news
detection methods explore social relationships to further enhance the
representation of tweets (Yang et al., 2021; Wei et al., 2021; Zheng et al.,
2022). This section provides an overview of these methods.

Multimodal Fake News Detection. In social media, tweets comprise not only text
but are often associated with images and videos. Such multimodality provides
complementary perspectives on the content of the tweet. As such, combining
text and visual information can lead to a more complete understanding of the
content of the tweet. This has motivated researchers to develop multimodal
fake news detection. The most common formulation is to extract textual and
visual features separately using DNNs and fuse the extracted features to
classify the tweet into fake or real.

Spotfake (Singhal et al., 2019) employs VGG19 (Simonyan and Zisserman, 2015)
and BERT (Kenton and Toutanova, 2019) to extract visual and textual features,
respectively. These features are concatenated and fed into a classifier to
leverage both modalities. HMCAN (Qian et al., 2021) feeds image and text
features into a multimodal contextual attention network. This approach
captures both inter- and intra-modality relationships for a better fusion of
the two modalities. Some studies compared the visual and textual content of
news articles. SAFE (Zhou et al., 2020b) leverages an image-to-text model to
convert visual information into textual descriptions, which enables a direct
comparison between the two modalities. The representations of textual and
visual information, along with their similarity, are jointly learned and used
to predict fake news.

All these methods treat tweets individually, i.e., they take a single tweet to
make a prediction; they ignore the social context that can help fake news
detection. For example, how the tweet spread (who retweeted or replied) can be
a cue to identify fake news.

Graph-based Fake News Detection. Graph-based approaches have been proposed to
explore the social context of a tweet as a graph structure to render the
social context of fake news (Wei et al., 2021; Zheng et al., 2022; Nguyen et
al., 2020). These methods evaluate the tweet from a holistic perspective,
taking into account the relationships and interactions among different users
(tweet authors) and sources. These approaches effectively capture the complex
network of social relationships that contribute to the dissemination and
credibility of a tweet. Bi-GCN (Wei et al., 2021) employs a Graph
Convolutional Network (GCN) to organize the spread of fake news in social
media. The method utilizes a top-down GCN to calculate the propagation of fake
news and a bottom-up GCN to aggregate dispersed fake news. More relevantly,
MFAN (Zheng et al., 2022) sought to integrate textual, visual, and social
graph features within a unified framework.

One interesting extension of such graph-based approaches is to further
incorporate the relationships and interactions among tweets on the same event.
Social media typically have many tweets about a specific event, some of which
can be fake. Without the knowledge of the event, fake news may only be spotted
in the context of others on the same event; therefore, contextualizing a tweet
may allow for identifying fake news tweets even if they do not stand by
themselves.

Inspired by this assumption, we construct a cross-modal tweet graph using the
cross-modality of CLIP (Radford et al., 2021), capturing potential connections
between tweets with similar text or images. Based on this cross-modal tweet
graph, a GCN with Label Propagation considers both positive and negative
correlations between neighboring tweets, effectively integrating their
corresponding prediction labels for a more accurate final decision.

##  3\. Method

We primarily focus on utilizing a multimodal tweet (i.e., text and an image)
and the corresponding label on the veracity of its content for training a
model for fake news detection. We initially extract potential connections
among tweets by leveraging their similarity to construct a cross-modal tweet
graph. LPN then learns the signed weight of edges based on node features. This
weight encodes positive or negative correlations between a pair of tweets to
facilitate propagating their labels. For training, we designed a domain
generalization loss, asides from the cross-entropy loss, to improve the
model’s ability to detect unseen events. Figure 2 shows the overall
architecture of our model.

###  3.1. Cross-modal Tweet Graph Construction

As aforementioned, contextualizing a tweet with relevant ones (i.e., tweets on
the same event) may facilitate the detection of fake news. A tweet is often
associated with some tags that identify the corresponding event, but they are
not always available and noisy. Instead of such unreliable sources, we use
inter- and intra-modal similarities to construct a graph that potentially
captures the relevance among tweets.

Our method uses CLIP (Radford et al., 2021), which encodes images and text
into a unified space, for similarity computation. Formally, let $\mathcal{D}$
be a set of $|\mathcal{D}|$ tweets, where each tweet
$d_{i}=(v_{i},t_{i})\in\mathcal{D}$ contains an image $v_{i}$ and text
$t_{i}$. We also denote an undirected graph to represent the relevance among
tweets by $\mathcal{G}=(\mathcal{D},\mathcal{E})$. $\mathcal{E}$ contains edge
$e_{ij}=(d_{i},d_{j})$ if the similarity between $d_{i}$ and $d_{j}$ is high.
Specifically, for pair $(d_{i},d_{j})$, we extract CLIP features from both the
visual and textual modalities and compute the cosine similarities for all
possible combinations of modalities, denoted by $s_{ij}^{c}$, where $c$ is in
$\mathcal{C}=\\{\text{image-to-image},\text{text-to-text},\text{image-to-
text},\text{text-to-image}\\}$. We define a similarity threshold, denoted as
$\tau$, for all possible combinations within the set $\mathcal{C}$. A pair
$e_{ij}$ is included in $\mathcal{E}$ if its corresponding similarity
$s_{ij}^{c}$ surpasses $\tau$, i.e., $e_{ij}\in\mathcal{E}$ when
$s_{ij}^{c}\geq\tau$ for at least one combination $c$.

Connected tweets in this graph have potential relevance; however, it is
important to emphasize that their claims may differ. For example, two tweets
may have the same image and are related to the same event but possibly say the
opposite. It is also possible that the text of one tweet highly matches the
image of another tweet, but their texts exhibit significant differences. These
connections can detect fake news, but a well-designed graph-based model is
needed to discern such differences.

###  3.2. Feature Contextualization

Feature contextualization network (FCN) aggregates relevant (connected) tweets
for a certain tweet to contrast it with the others. Specifically, we employ
Graph Convolutional Network (GCN)-based feature update to tweet feature
$\mathbf{x}_{i}^{l}\in\mathbb{R}^{d}$ in the $l$-th layer ($l=1,\dots,L-1$),
which can be written as:

(1)
$\mathbf{x}_{i}^{l+1}=\sigma\left(W\mathbf{x}_{i}^{l}+\sum_{j\in\mathcal{N}_{i}}\frac{W^{l}\mathbf{x}_{j}^{l}}{\sqrt{|\mathcal{N}_{i}|\;|\mathcal{N}_{j}|}}\right),$

where $\sigma(\cdot)$ is an activation function (e.g., ReLU), $W^{l}$ is a
learnable weight matrix, and $\mathcal{N}_{i}$ is the set of connected tweets
(nodes) to $d_{i}$, given by
$\mathcal{N}_{i}=\\{d_{j}|(d_{i},d_{j})\in\mathcal{E}\\}$. We normalize the
aggregated neighbor tweets’ features by the square root of the product of the
degrees $|\mathcal{N}_{i}|$ and $|\mathcal{N}_{j}|$. The input tweet features
for our FCN can be derived from previously learned fake news detection models
or by concatenating extracted image and text features from CLIP.

###  3.3. Label Propagation

The straightforward approach for making a prediction is to use a Multi-layer
Perceptron (MLP) to compute a two-dimensional vector of logits. Since the
training and test sets for fake news detection may belong to different events,
this can easily lead to overfitting. Integrating the predictions of relevant
tweets to make a final decision facilitates coherent predictions across the
entire set of tweets and mitigates overfitting. Simultaneously, edges in
$\mathcal{G}$ can potentially represent positive and negative correlations. We
thus design a label propagation network (LPN). This network smooths the labels
considering both positive and negative correlations. Specifically, LPN
comprises stacked bi-directional label attention layers.

Let $\mathbf{x}^{L}_{i}$ denote the contextualized features from the final
layer FPN. This feature vector is fed into an MLP to generate a two-
dimensional vector of logits. Subsequently, a softmax function is applied to
convert the logits into class probabilities
$\mathbf{\tilde{y}}_{i}=(\tilde{y}^{\text{r}}_{i},\tilde{y}^{\text{f}}_{i})$,
where $\tilde{y}^{\text{r}}_{i}$ and $\tilde{y}^{\text{f}}_{i}$ represent the
probabilities of tweet $d_{i}$ being real or fake, respectively.

We use $L^{\prime}$ label attention layers in our network. Given label
$\hat{\mathbf{y}}_{i}^{l}\in[0,1]^{2}$ from the $l$-th label attention layer
($l=1,\dots,L^{\prime}-1$) and
$\hat{\mathbf{y}}_{i}^{1}=\tilde{\mathbf{y}}_{i}$. The update for the
$(l+1)$-th label $\hat{\mathbf{y}}_{i}^{l+1}\in[0,1]^{2}$ is given by:

(2)
$\hat{\mathbf{y}}_{i}^{l+1}=\mbox{softmax}\left(\hat{\mathbf{y}}_{i}^{l}+\sum_{j\in\mathcal{N}_{i}}\bm{\alpha}_{ij}\odot\hat{\mathbf{y}}_{j}^{l}\right),$

where $\odot$ is element-wise multiplication and
$\bm{\alpha}_{ij}=(\alpha_{ij}^{\text{r}},\alpha_{ij}^{\text{f}})\in[-1,1]^{2}$
is the bi-directional label attention weights between tweets $i$ and $j$.
These weights are based on the contextualized features:

(3)
$\bm{\alpha}_{ij}=\text{tanh}(A[V\mathbf{x}_{i}^{L},V\mathbf{x}_{j}^{L}]),$

where $[\cdot,\cdot]$ is concatenation and tanh is hyperbolic tangent.
$A\in\mathbb{R}^{2\times 2d}$ and $V\in\mathbb{R}^{d\times d}$ are learnable
weight matrices.

This bi-directional attention effectively captures both positive and negative
correlations between label probabilities of neighboring tweets with
${\bm{\alpha}}_{ij}$. For example, if $\alpha_{ij}^{\text{r}}$ is negative,
then tweet $d_{j}$’s probability of being real in a certain layer decreases
the $d_{i}$’s probability of being real in the next layer. This flexibility
allows for capturing various relationships between tweets to improve
reliability and accuracy.

Table 1. The performance of the state-of-the-art baselines is compared with
the performance after integrating our FCN-LP with them, evaluated on the
Twitter, PHEME, and Weibo datasets.

Method | Twitter | PHEME | Weibo
---|---|---|---
Accuracy | Precision | Recall | F1 | Accuracy | Precision | Recall | F1 | Accuracy | Precision | Recall | F1
EANN |  71.53$\pm$0.91 |  71.38$\pm$1.23 |  63.82$\pm$2.11 |  68.91$\pm$1.58 |  70.17$\pm$0.79 |  71.28$\pm$1.32 |  67.36$\pm$2.17 |  69.10$\pm$1.83 |  79.18$\pm$0.76 |  80.31$\pm$1.23 |  78.52$\pm$0.32 |  79.44$\pm$2.13
\+ FCN-LP |  74.21$\pm$2.12 |  73.52$\pm$0.51 |  67.11$\pm$0.65 |  71.33$\pm$2.14 |  74.22$\pm$1.16 |  75.11$\pm$2.41 |  70.41$\pm$1.52 |  72.33$\pm$1.17 |  82.23$\pm$1.53 |  83.56$\pm$0.77 |  80.98$\pm$1.56 |  81.63$\pm$1.77
SpotFake |  77.16$\pm$1.57 |  75.32$\pm$1.14 |  87.83$\pm$0.63 |  85.14$\pm$0.07 |  81.37$\pm$2.38 |  79.53$\pm$2.27 |  81.22$\pm$2.43 |  79.43$\pm$0.75 |  86.39$\pm$2.51 |  86.12$\pm$0.53 |  87.17$\pm$2.63 |  83.22$\pm$1.41
\+ FCN-LP |  79.46$\pm$2.61 |  78.21$\pm$1.51 |  88.41$\pm$1.02 |  86.53$\pm$2.15 |  83.62$\pm$0.87 |  81.41$\pm$0.24 |  83.17$\pm$1.08 |  81.76$\pm$0.47 |  86.88$\pm$0.56 |  87.65$\pm$1.78 |  87.44$\pm$0.21 |  85.53$\pm$1.04
MVAE |  74.56$\pm$1.58 |  80.15$\pm$2.69 |  76.34$\pm$0.83 |  81.57$\pm$1.98 |  77.83$\pm$1.27 |  73.82$\pm$2.05 |  73.45$\pm$2.62 |  72.21$\pm$0.54 |  71.86$\pm$0.25 |  70.32$\pm$0.69 |  70.32$\pm$2.84 |  70.53$\pm$1.60
\+ FCN-LP |  76.37$\pm$2.57 |  81.18$\pm$0.97 |  79.16$\pm$1.51 |  82.91$\pm$2.23 |  79.06$\pm$2.46 |  76.31$\pm$1.56 |  75.93$\pm$3.00 |  73.88$\pm$1.26 |  73.54$\pm$1.96 |  72.45$\pm$2.37 |  74.65$\pm$0.97 |  74.01$\pm$2.01
SAFE |  76.66$\pm$3.00 |  76.32$\pm$1.94 |  75.41$\pm$2.12 |  76.37$\pm$2.85 |  81.25$\pm$1.34 |  79.92$\pm$2.76 |  79.11$\pm$1.45 |  79.69$\pm$2.67 |  84.91$\pm$2.10 |  83.81$\pm$1.58 |  82.19$\pm$1.16 |  83.01$\pm$1.70
\+ FCN-LP |  77.94$\pm$0.99 |  79.09$\pm$0.45 |  76.73$\pm$1.63 |  77.55$\pm$2.32 |  83.00$\pm$2.60 |  82.22$\pm$1.62 |  81.44$\pm$0.46 |  82.38$\pm$0.91 |  87.14$\pm$2.89 |  85.30$\pm$2.59 |  85.05$\pm$0.92 |  84.53$\pm$0.64
MCAN |  80.91$\pm$2.33 |  82.68$\pm$2.48 |  76.67$\pm$0.94 |  82.26$\pm$1.32 |  80.74$\pm$1.89 |  79.21$\pm$2.23 |  79.64$\pm$1.53 |  80.15$\pm$0.86 |  89.41$\pm$2.91 |  90.35$\pm$2.17 |  88.51$\pm$1.78 |  90.01$\pm$1.57
\+ FCN-LP |  81.98$\pm$1.90 |  83.51$\pm$1.11 |  77.24$\pm$2.21 |  83.19$\pm$0.98 |  81.30$\pm$1.20 |  80.49$\pm$1.91 |  80.50$\pm$1.72 |  81.03$\pm$2.13 | 91.52$\pm$2.67 |  91.56$\pm$2.43 |  89.20$\pm$1.11 | 91.40$\pm$1.91
HMCAN |  83.91$\pm$1.49 |  81.68$\pm$2.08 |  84.67$\pm$1.21 |  82.57$\pm$1.62 |  86.36$\pm$1.83 |  83.18$\pm$1.41 |  83.81$\pm$2.51 |  83.45$\pm$1.07 |  88.51$\pm$2.90 |  92.04$\pm$2.97 |  84.57$\pm$1.77 |  88.11$\pm$1.14
\+ FCN-LP |  84.57$\pm$1.62 |  83.58$\pm$1.66 |  85.22$\pm$2.06 |  84.04$\pm$0.82 | 87.25$\pm$1.18 |  84.77$\pm$1.91 |  84.78$\pm$1.95 |  84.50$\pm$0.85 |  91.15$\pm$2.82 | 94.01$\pm$2.56 |  86.55$\pm$1.54 |  89.13$\pm$1.77
CLIP |  80.93$\pm$1.27 |  75.71$\pm$1.68 |  92.07$\pm$0.85 |  85.45$\pm$0.47 |  80.36$\pm$1.93 |  84.43$\pm$1.27 |  89.12$\pm$0.12 |  86.71$\pm$1.88 |  82.92$\pm$0.54 |  83.17$\pm$1.00 |  88.45$\pm$2.13 |  86.74$\pm$0.41
\+ FCN-LP | 89.00$\pm$1.90 | 83.85$\pm$1.29 | 97.63$\pm$1.67 | 91.22$\pm$1.11 |  84.65$\pm$0.49 | 88.79$\pm$0.92 | 89.81$\pm$1.26 | 89.30$\pm$0.53 |  84.47$\pm$1.66 |  88.41$\pm$0.26 | 91.18$\pm$0.69 |  89.78$\pm$0.84

###  3.4. Classifcation with Domain Generalization

Our approach consists of FCN and LPN. One of the main challenges in training a
model for fake news detection is overfitting to the training data, resulting
in poor performance on unseen tweets, particularly about new events. For
generalization, it is crucial to ensure a consistent distribution of features
of both training and unseen data. We thus divided the training dataset into
two disjoint subsets $\mathcal{D}_{\text{s}}$ and $\mathcal{D}_{\text{u}}$
with seen and unseen tweets, respectively. The seen subset is used to train
both FCN and LPN, whereas the unseen subset is only for updating FCN, so that
the distribution of unseen tweets’ features is consistent with seen ones.

Specifically, we train FCN and LPN by minimizing the cross-entropy loss of
predicted labels over $\mathcal{D}_{\text{s}}$:

(4)
$\mathcal{L}_{\text{FCN}}=-\sum_{d_{i}\in\mathcal{D}_{\text{s}}}\sum_{k\in\\{\text{r},\text{f}\\}}y_{i}^{k}\log\tilde{y}_{i}^{k}(\mathbf{x}_{i}^{L}),$

where $y_{i}^{k}$ is the ground-truth label for tweet $d_{i}$ and
$\tilde{y}_{i}^{k}(\mathbf{x}_{i}^{L})$ is the corresponding prediction.111We
explicify that $\hat{y}_{i}^{k}$ is a function of $\mathbf{x}_{i}^{L}$. This
loss function will maximize the probability that each seen node in the
training set is correctly labeled, thus increasing the inter-class variation
in node feature $\mathbf{x}_{i}^{L}$.

We also apply the cross-entropy loss to train LPN:

(5)
$\mathcal{L}_{\text{LPN}}=-\sum_{d_{i}\in\mathcal{D}_{\text{s}}}\sum_{k\in\\{\text{r},\text{f}\\}}y_{i}^{k}\log\hat{y}_{i}^{L^{\prime}k}(\mathbf{x}_{i}^{L},\tilde{\mathbf{y}}_{i}),$

where $\hat{y}_{i}^{L^{\prime}k}$ is the output of the last ($L^{\prime}$)-th
layer of LPN. Based on this training, FCN learns initial classification
prediction labels from features of tweets in $\mathcal{D}_{\text{s}}$, and LPN
learns to efficiently aggregate neighboring tweets’ labels to take their
correlations into account.

We next consider generalizing the model to unseen tweets. A reasonable
generalization is that guarantees accurate classification on the seen tweets
while regularizing the model to maintain a consistent distribution on the
unseen tweets. Assuming we have two sets $\mathcal{P}$ and $\mathcal{Q}$ of
features, we can use Maximum Mean Discrepancy (MMD) to compare the differences
between the two distributions in the feature space as:

(6)
$\text{MMD}(\mathcal{P},\mathcal{Q})=\left\|\frac{1}{|\mathcal{P}|}\sum_{\mathbf{p}\in\mathcal{P}}\mathbf{p}-\frac{1}{|\mathcal{Q}|}\sum_{\mathbf{q}\in\mathcal{Q}}\mathbf{q}\right\|_{2}^{2}.$

To ensure intra-class consistency and inter-class distinguishability of
features, we compute the MMD separately for the real and fake features of
tweets between seen and unseen sets and use a sum of these losses as the
overall loss. Specifically, given the seen feature sets with real and fake
labels $\mathcal{X}_{\text{s}}^{\text{r}}$ and
$\mathcal{X}_{\text{s}}^{\text{f}}$, as well as the unseen feature sets with
real and fake labels $\mathcal{X}_{\text{u}}^{\text{r}}$ and
$\mathcal{X}_{\text{u}}^{\text{f}}$, we can define the overall loss as
follows:

(7)
$\mathcal{L}_{\text{MMD}}=\text{MMD}(\mathcal{X}_{\text{s}}^{\text{r}},\mathcal{X}_{\text{u}}^{\text{r}})+\text{MMD}(\mathcal{X}_{\text{s}}^{\text{f}},\mathcal{X}_{\text{u}}^{\text{f}}).$

By minimizing the $\mathcal{L}_{\text{MMD}}$ loss, we can ensure the
consistency of features between seen and unseen tweets in the feature space,
thereby improving the model’s generalization ability. Finally, we combine the
three losses ($\mathcal{L}_{\text{FCN}}$, $\mathcal{L}_{\text{LPN}}$, and
$\mathcal{L}_{\text{MMD}}$) and train the entire model end-to-end with the
following loss function:

(8)
$\mathcal{L}_{all}=\mathcal{L}_{\text{FCN}}+\lambda\mathcal{L}_{\text{LPN}}+\mu\mathcal{L}_{\text{MMD}}$

where $\lambda$ and $\mu$ are the balancing hyper-parameters.

Table 2. Performance comparison of FCN-LP with its variants using CLIP
features on the Twitter, PHEME, and Weibo datasets.

| FCN | LPN | LPN-$\bm{\alpha}$ | $\mathcal{L}_{\text{MMD}}$ | Twitter | PHEME | Weibo
---|---|---|---|---|---|---|---
| Acc. | Prec. | Recall | F1 | Acc. | Prec. | Recall | F1 | Acc. | Prec. | Recall | F1
(i) |  |  |  |  | 80.93 | 75.71 | 92.07 | 85.45 | 80.36 | 84.43 | 89.12 | 86.71 | 82.92 | 83.17 | 88.45 | 86.74
(ii) | $\checkmark$ |  |  |  | 82.17 | 76.54 | 92.82 | 86.63 | 80.47 | 85.92 | 89.35 | 86.85 | 83.15 | 84.26 | 88.94 | 86.92
(iii) | $\checkmark$ | $\checkmark$ |  |  | 87.62 | 81.58 | 96.17 | 89.82 | 83.27 | 87.28 | 89.76 | 88.75 | 84.22 | 87.52 | 90.21 | 88.77
(iv) | $\checkmark$ |  | $\checkmark$ | $\checkmark$ | 86.82 | 80.87 | 96.01 | 87.93 | 82.15 | 86.34 | 89.71 | 88.12 | 83.91 | 86.25 | 89.77 | 88.21
(v) | $\checkmark$ | $\checkmark$ |  | $\checkmark$ | 89.00 | 83.85 | 97.63 | 91.22 | 84.65 | 88.79 | 89.81 | 89.30 | 84.47 | 88.41 | 91.18 | 89.78

##  4\. Experiment

###  4.1. Datesets

We evaluated our model on three real social media datasets from Twitter,
PHEME, and Weibo.

Twitter (Boididou et al., 2015): The Twitter dataset, part of the MediaEval
Verifying Multimedia Use benchmark (Boididou et al., 2015), is used for
detecting fake content on social media and contains approximately 17,000
unique tweets related to various events. Each tweet includes textual content,
an attached image or video, and additional social context, with labels on
fake/real and the event. We removed tweets with videos. After removal, the
dataset is divided into a training set (9,000 fake and 6,000 real news tweets)
and a test set (2,000 tweets), ensuring no overlapping events. There are 15
events in the training set, and we randomly select tweets related to 11 events
as seen tweets and the remaining as unseen tweets.

PHEME (Zubiaga et al., 2017): The PHEME dataset comprises tweets from Twitter,
focusing on five breaking news events. Each event contains a set of posts,
including a large number of texts and images, each pair of which also comes
with labels on fake/real and the event. We randomly select three events and
extract related tweets for training, where the tweets on randomly selected two
events are used as seen tweets while the ones on the other event are used as
unseen tweets. The tweets on events other than these three are used for
evaluation.

Weibo (Jin et al., 2017): The Weibo dataset is collected from a Chinese
microblogging service, Sina Weibo. The real news is from authoritative Chinese
news sources, such as Xinhua News Agency, while the fake news is collected
from Weibo between May 2012 and January 2016, verified by Weibo’s official
rumor debunking system. We remove tweets without any text or image. This
dataset does not provide event labels, so we randomly split the dataset into
seen, unseen, and testing sets at 6:2:2. Each sample in the dataset contains
tweet ID, text, image, and real/fake label.

###  4.2. Baselines

To verify the validity of our model, we selected several state-of-the-art
multimodal methods for comparison.

EANN (Wang et al., 2018) first extracts textual and visual features separately
using pre-trained models and combines these features to feed into a fake news
detector. To make the features invariant to events, they simultaneously train
an event classifier and the detector in an adversarial manner so that the
classifier fails.

SpotFake (Singhal et al., 2019) detects fake news by using a simple
concatenation of features extracted from pre-trained text and image models.

MVAE (Khattar et al., 2019) utilizes a bimodal variational autoencoder to
learn a shared representation of tweets, coupled with a binary classifier to
detect fake news.

SAFE (Zhou et al., 2020b) jointly exploits multimodal features and cross-modal
similarity for learning tweet features.

MCAN (Wu et al., 2021) leverages co-attention layers to learn the inter-modal
relationships between textual and visual features. The enhanced features are
fused for fake news detection.

HMCAN (Qian et al., 2021) utilizes a multi-modal contextual attention network
to fuse inter-modality and intra-modality relationships. A hierarchical
encoding network captures rich hierarchical semantics, improving fake news
detection.

CLIP (Radford et al., 2021) exhibits strong zero-shot capabilities for several
vision-and-language tasks. We train a fake news detector with a 2-layer MLP
that takes as input a concatenation of image and text features extracted by
CLIP.

###  4.3. Implementation Details

We implement our FCN-LP with Pytorch and PyG (PyTorch Geometric).222The code
is available at https://github.com/zhaowanqing/FCN-LP. For all the
experiments, we used NVIDIA GeForce GTX 3090 GPU with 24GB memory. For
constructing the cross-modal tweet graph, we choose a similarity threshold
$\tau=0.95$ on the Twitter dataset and $\tau=0.91$ on the PHEME and Weibo
datasets. The balancing hyper-parameters $\lambda$ and $\mu$ are both set to
1. These choices are based on our parameter sensitivity analysis. FCN-LP has
four graph convolutional layers and two label attention layers, which are
trained using AdamW with the learning rate of $10^{-4}$ for 500 epochs. We
train the model 5 times and report the average and standard deviation of
accuracy, precision, recall, and F1 Score.

###  4.4. Performance Comparison

One advantage of FCN-LP is that it can be cascaded to any fake news detector
to improve its performance. For this, we first train a detector over a fake
news dataset. Then, we extract tweet features from the layer preceding the
prediction head, which will serve as node features of FCN-LP. For the CLIP
baseline, we directly concatenate the image and text features by CLIP as node
features. These features are fixed while training FCN-LP.

The results are shown in Table 1. We observe that FCN-LP can boost the
performance of the existing multimodal fake news detectors in all metrics,
ranging from 0.57% to 8.07%. This may mean that interactions among features
and modeling positive and negative correlations via a graph based on
multimodal similarities facilitate the detection. In particular, the detection
based on CLIP features shows superior performance on Twitter and PHEME
datasets. This can be attributed to CLIP being pre-trained on a larger
multimodal dataset, resulting in features with better generalization when
applied to the test set. After training on a fake news dataset, tweet features
extracted from other methods may suffer from potential overfitting. This leads
to limited performance improvement by FCN-LP compared to the CLIP-based
baseline. On the other hand, since CLIP’s pre-training dataset is mainly from
an English corpus, it does not show strong performance in the Weibo dataset,
which is dominated by Chinese text. MCAN and HMCAN employ Chinese BERT to
extract text features. The enhancement in their performance on the Weibo
dataset can probably be attributed to this language model.

###  4.5. Ablation Study

Several design choices are ablated within FCN-LP to comprehend their
importance better. All ablation experiments uses CLIP’s textual and visual
features. We evaluated five settings: (i) CLIP baseline (as in Section 4.2).
(ii) FCN-only that solely utilizes FCN to contextualize the features and feeds
them into a 2-layer MLP for fake new detection. (iii) FCN-LPN without MMD that
integrates both FCN and LPN but omits the Maximum Mean Discrepancy (MMD) loss.
All tweets in the training set are used as seen data for training. (iv)
LPN-$\alpha$: To evaluate the impact of bi-directional label attention, we
modified $\bm{\alpha}_{ij}$ in LPN to a 1-dimensional attention weight,
ranging from $[0,1]$. This variant highlights the impact of using positive and
negative correlations in label propagation. (v) FCN-LP. Due to MMD, these five
variants are exposed to the training set differently. (i)–(iv) use the entire
training set for supervised training, whereas (v) uses the seen subset to
train FCN in a supervised manner and both the seen and unseen subsets for MMD.
This means that FCN-LP uses fewer samples for supervised training. We consider
that this difference advantages (i)–(iv) but not our full model.

_1) Effects of feature contextualization:_ From the comparison between (i) and
(ii) in Table 2, we can observe that feature contextualization by neighboring
tweets is helpful. Compared to detectors solely based on individual tweets,
incorporating additional context can enhance the understanding of a certain
tweet’s content and allow for comparison.

_2) Effects of label propagation:_ As illustrated in Table 2, the average
accuracy of (iii) exhibited an improvement compared to (ii) by 5.45%, 2.8%,
and 1.07% on the Twitter, PHEME, and Weibo datasets, respectively. Since the
training set and test set of Twitter and PHEME are related to different
events, the risk of overfitting is more obvious. The label propagation can
make a final decision by combining the prediction of relevant tweets,
achieving a more cohesive and accurate analysis across the entire tweet set,
and effectively reducing the possibility of overfitting. LPN offers more
significant improvements in the Twitter and PHEME datasets, potentially
indicating that our method robustifies detection for new events.

_3) Effects of bi-directional label attention:_ Variant (iv) shows the
effectiveness of bi-directional label attention. Compared to (v) FCN-LP, (iv)
shows a significant performance deterioration. The possible reason is that
there is a potentially positive and negative correlation between relevant
tweets and single-valued attention experiences difficulty in discerning these
relationships, leading to wrong predictions. Bi-directional label attention,
in contrast, effectively captures such various relationships and assigns
appropriate weights to predictions.

_4) Effects of domain generalization:_ We can verify the effect of the domain
generalization loss $\mathcal{L}_{\text{MMD}}$ by comparing (iii) and (v). As
seen in Table 2, the domain generalization loss gives an obvious boost in all
datasets. This implies that constraining the features to distribute a certain
(the seen subset’s) volume via $\mathcal{L}_{\text{MMD}}$ would be beneficial
in classifying new tweets that may be related to unseen events. This may mean
that $\mathcal{L}_{\text{MMD}}$ consequently tries to make the features
invariant to events.

Figure 3.  Parameter sensitivity analysis for similarity threshold $\tau$: (a)
accuracy curves with different similarity thresholds; (b) the average number
of connected (relevant) tweets per node (Avg. CT / node).  Figure 4.  Accuracy
values obtained for various loss balancing hyper-parameters $\lambda$ and
$\mu$ on the Twitter dataset. Figure 5.  2D t-SNE plot of the cross-modal
tweet graph constructed on the PHEME dataset. The various markers signify
distinct events within the dataset. A red border indicates the tweet is real,
while a black border denotes a fake tweet. Figure 6.  Visualization of the
learned FCN features constructed on the PHEME dataset. The color lines
indicate bi-directional attention values on connected tweets. The yellow line
indicates a positive correlation, and the blue line indicates a negative
correlation. Figure 7. Example of a tweet query: leveraging our method, we can
not only detect fake news but also present associated real and fake tweets
based on correlations. This approach offers interpretable classification
results and assists users in discerning the fact behind the information.

###  4.6. Parameters Sensitivity Analysis

We study the sensitivities of 1) $\tau$, the similarity threshold in
constructing the cross-modal tweet graph, and 2) $\lambda$ and $\mu$, the
balancing hyper-parameters in the overall loss function. All parameter
sensitivity analysis experiments were done using the CLIP model.

The similarity threshold $\tau$ dictates the strength of connections between
tweets based on their content similarity. A higher threshold value ensures
more strict and precise connections. Conversely, a lower threshold value
allows for a greater number of connections but may introduce noise in the form
of irrelevant or loosely related tweets. We vary the value of $\tau$ from
$0.85$ to $0.99$. We report the accuracy with different $\tau$ in Figure 3(a)
and the average number of connections per node in Figure 3(b). Since a large
number of tweets correspond to the same image in the Twitter dataset, more
tweets are connected to each node than in the PHEME and Weibo datasets. Too
many connections led to out-of-memory on the Twitter dataset, so we excluded
the cases where $\tau<0.91$. We finally chose $\tau=0.95$ on the Twitter
dataset and 0.91 on the PHEME and Weibo datasets, which better balances
between the accuracy and complexity.

To analyze the balancing hyper-parameters in the loss function, we change the
values of $\lambda$ and $\mu$ between 0.01 and 100. We performed a grid search
over this range and chose the best hyper-parameters. A bar plot of the
validation accuracy on the Twitter dataset for various combinations of
$\lambda$ and $\mu$ is given in Figure 4. It can be noticed that similar
weights among the three loss functions show better performance. Therefore the
balancing hyper-parameters in our approach can be set as $\lambda=\mu=1$. A
similar behavior was noticed on the PHEME and Weibo datasets as well.

###  4.7. Visualization

We visualize the cross-modal tweet graph constructed on the PHEME dataset in
Figure 5. We use t-SNE (Van der Maaten and Hinton, 2008) to reduce the
original tweet features to 2D and sample 10% for visualization. As seen in
Figure 5, CLIP features separate events at divisible distances, and neighbors
usually have a connection. This is consistent with our estimation and
indicates that cross-modal tweet graphs are meaningfully used for aggregating
features and label propagation.

In order to further explore the learned FCN feature and label propagation
between tweets, we visualize the learned FCN features in Figure 6 using the
same way as visualizing the cross-modal tweet graph, with the addition of
color lines to indicate attention values on labels. We can see that the
learned FCN features show a clear distinction between real and fake tweets.
There are some connections in the graph between real and fake tweets, but our
bi-directional attention gives them a negative correlation and, therefore
indirectly contributes to a performance boost.

This cross-modal tweet graph with bi-directional attention can potentially
provide interpretability for classification results and even reveal the fact
about the query tweet. For example, as shown in Figure 7, for a tweet
predicted as fake, we can simultaneously provide associated real and fake
tweets along with their correlation, serving as fact-checked information to
improve users’ consciousness of fake news. As seen in the example, the
relevant matching real event is a shooting incident in Ottawa. However, it
does not explicitly mention the soldier being shot in real tweets, and the
fake tweet could potentially lead to misinterpretation.

##  5\. Conclusions

This paper presents a method for detecting fake news on social media by
leveraging cross-modal tweet content. Our method constructs a cross-modal
tweet graph using CLIP, which allows us to extract potential connections and
reveal the events behind them to improve detection performance. We also
designed FCN-LP to learn the signed attention of the edges, which generates
positive or negative correlations applied to the related tweet labels. Our
method can generalize even to tweets on unseen events. Our label propagation
based on a constructed cross-modal tweet graph is portable. We can put it on
top of existing methods and use their own predictions to improve the detection
performance. Our domain generalization loss also improves the model’s ability
to detect unseen events, making it more robust in real-world scenarios. One
potential avenue for future research is exploring the use of pre-trained large
language models, such as GPT-3 or even GPT-4, for fake news detection. These
models have shown impressive performance on a range of natural language
processing tasks. They may be able to reveal biases or distortions in the text
that are indicative of fake news.

## Acknowledgments

This research was supported by the National Natural Science Foundation of
China (Grant No. 62273275), the China Scholarship Council and the JST CREST
Grant No. JPMJCR20D3.

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