Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection (2024)

Abstract: The swift detection of multimedia fake news has emerged as a crucial task in combating malicious propaganda and safeguarding the security of the online environment. While existing methods have achieved commendable results in modeling entity-level inconsistency, addressing event-level inconsistency following the inherent subject-predicate logic of news and robustly learning news representations from poorquality news samples remain two challenges. In this paper, we propose an Event-dRiven fAke news Detection frAmewoRk (Event-Radar) based on multi-view learning, which integrates visual manipulation, textual emotion and multimodal inconsistency at event-level for fake news detection. Specifically, leveraging the capability of graph structures to capture interactions between events and parameters, EventRadar captures event-level multimodal inconsistency by constructing an event graph that includes multimodal entity subject-predicate logic. Additionally, to mitigate the interference of poor-quality news, Event-Radar introduces a multi-view fusion mechanism, learning comprehensive and robust representations by computing the credibility of each view as a clue, thereby detecting fake news. Extensive experiments demonstrate that Event-Radar achieves outstanding performance on three large-scale fake news detection benchmarks. Our studies also confirm that Event-Radar exhibits strong robustness, providing a paradigm for detecting fake news from noisy news samples.

heading: 1 INTRODUCTION
text: Against the backdrop of the rapid expansion of social media, online platforms like Twitter have emerged as the primary channels for people to obtain information. Unfortunately, they have also
*Corresponding Author †Part of the work was assisted by SGIT AI Lab, State Grid
Corporation of China during the author’s internship.
become breeding grounds for the proliferation and dissemination of fake news. Fake news publishers exploit these platforms by spreading erroneous information, fueling societal divisions, fostering conspiracy theories, and posing threats to societal safety (Zhao et al., 2015; Lao et al., 2021). The “viral spread of information” during the 2016 US presidential elections (Fisher et al., 2016) and the COVID-19 pandemic (Naeem and Bhatti, 2020) vividly depict how fake news disrupts societal order. Typically, visual media like photos often trigger strong emotional reactions in readers, leading to higher engagement on social media, thereby serving as an ideal vehicle for fake news (Qi et al., 2019).
Some researchers argue that the inconsistency between posts and images is a key feature in judging the authenticity of news, and they have proposed methods to model this text-visual inconsis-
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tency (Chen et al., 2022; Zhou et al., 2023). In addition, news on social media is diverse, and inconsistency is not an absolute criterion for determining news authenticity (Ying et al., 2023). Detecting manipulated images (Cao et al., 2020) or provocative emotion in post (Zhang et al., 2021b) is also an effective view for detecting fake news. As a result, integrating as many available multimodal clues as possible becomes crucial for fake news detection, called multi-view learning (Ying et al., 2023; Wu et al., 2021; Zeng et al., 2023; Wan et al., 2024). Although methods based on inconsistency or multi-view learning have achieved many promising results, the lack of inconsistency checks at the event level still affects the accuracy of detection methods. Meanwhile, most existing methods overlook the impact of inherent noise in multimodal news data. Therefore, we summarize two main shortcomings of current methods: • Event-level multimodal inconsistency: In the context of news being regarded as a collection of events, 89% of news images encompass events characterized by subjects, objects, and predicates (Li et al., 2022). As illustrated in Fig.1 (a) and (b), both images contain entities such as ’police’ and ’protesters.’ However, due to differing subjectverb relationships, they convey significantly different meanings. Although existing methods have achieved excellent results in modeling inconsistency at element-level, aligning subjects and objects in images, merely achieving alignment at the element level may not effectively measure the relationship between news posts and images. This limitation leads the model to learn features biased towards check the authenticity of the news. • Noise of multimodal samples: With the rise of we-media, the casual composition of news has led to the proliferation of poor-quality news on social media. Some images undergo compression processing, making it almost impossible to recognize entities within them, while some news posts contain very few words. Additionally, certain multiview methods incorporate pattern features to detect image manipulation in fake news. Some news publishers use image editing techniques to highlight key elements in news images, as shown in Figure 1(c), leading to biases in models relying on image manipulation for detection. On social media, certain platforms use symbols like "#" in the post for tagging or mentioning, as illustrated in Fig. 1(d), leading to misjudgments by models analyzing post content and emotion. These noise of multimodal
news characterized by poor-quality and capable of causing cognitive bias in models, usually significantly impacts the generalization performance.
To tackle these challenges, we propose the Event-dRiven fAke news Detection frAmewoRk (Event-Radar) based on multi-view learning. The framework leverages statistical distributions to learn more robust news representations at the eventlevel. Specifically, we model individual news as a multimodal graph and extract subgraphs representing events present in both images and posts. Additionally, we utilize textual emotion and image pattern features as additional clues for multi-view learning, leveraging features from different views to enhance classification accuracy. However, this assumes that the quality or importance of these views is relatively stable across all samples. When feature from certain view is severely compromised, it can significantly impact the accuracy of classification (Wu et al., 2022). To address this issue, the beta distribution is utilized to estimate the credibility for each view, biasing the model towards trusting views with higher credibility. The contributions of this paper are three-folded:
• We propose a novel event-driven fake news detection framework that elucidates the inherent subjectverb logic in multimodal news.
• We attempt to address the issue of varying sample quality in news by estimating the credibility of each viewpoint using a Beta distribution. We determine the weight of feature fusion based on the magnitude of credibility, aiming to integrate features more heavily from views with higher credibility.
• Event-Radar not only outperforms all existing multi-view multimodal fake news detection frameworks but also provides a robust approach to resist the disturbance of noise samples, addressing the issue of model bias introduced by the complex data distribution in the real world.

heading: 2 RELATED WORK

heading: 2.1 Multimodal Fake News Detection
text: Traditional multimodal fake news detection extensively leverages latent information from both images and posts to obtain comprehensive multimodal news representations (Liu et al., 2023; Chen et al., 2023; Khattar et al., 2019). Approaches like Safe (Zhou et al., 2020) and BTIC (Zhang et al., 2021a) enhance multimodal representations by setting appropriate loss functions. Detecting fake news through modal inconsistency, measuring
the authenticity of news through entity alignment, is a prevailing method in current fake news research. CAFE (Chen et al., 2022) calculates the ambiguity between different modal elements using KL divergence, while FND-CLIP (Zhou et al., 2023) achieves excellent results through element-level semantic detection. However, entity-level inconsistency checks are relatively coarse and do not model the subject-predicate relationships between entities at the event level. Additionally, the challenge arises when fake news publishers employ image editing or deepfake techniques (Chen et al., 2022), rendering these methods ineffective on certain samples, requiring the integration of pattern features or provocative text emotion for multimodal learning.

heading: 2.2 Multi-view Learning
text: Leveraging multiple views to learn from data has proven to be effective in various tasks (Dang et al., 2023, 2024; Wu et al., 2024). Multi-view models based on CCA (Wang et al., 2016) are widely used for multi-view learning. MOE (Shazeer et al., 2017) based on the principle of divide and conquer introduces the mixed expert method by partitioning input samples into multiple subtasks and training an expert for each subtask. TMC (Han et al., 2021) uses the Dirichlet distribution to check class probabilities, parameterizing evidence from different views. In fake news detection, models like MVNN (Cao et al., 2020) and MCAN (Wu et al., 2021) incorporate pattern features as clues for multi-view learning, and BMR (Ying et al., 2023) introduces an enhanced multi-gate mixture expert network,
demonstrating the advantages of multi-view learning in fake news detection.
However, multi-view methods suffer significant performance degradation when features from individual views are lost or contain a substantial amount of noise, leading to erroneous judgments. Hence, we propose a methodology that harnesses credibility to integrate multi-view features.

heading: 3 METHODOLOGY
text: Fig. 2 illustrates an overview of the Event-Radar framework, comprising a multi-view modeling layer and a credibility estimation layer. Specifically, we initially encode the events, emotions, and pattern information of the news to comprehensively assess the news representation from various views. To model multimodal news events, we introduce an event inconsistency measurement module based on event subgraphs. To obtain credible representations from each view, we employ Beta distribution to compute the credibility of each view and fuse modality features guided by this credibility. Subsequently, for information interaction, we employ a self-attention mechanism to fuse modal information from various views. Finally, we employ a classifier to perform fake news detection.

heading: 3.1 Event Inconsistency Encoder
text: Considering the intricate subject-predicate logic among entities in multimodal news events, our model is designed to capture the complex relationships within and between modalities. Motivated by multimodal learning (Li et al., 2022), we es-
tablish a cross-modal graph Gk as a representation for multimodal news. For the k-th post-image pair (Pk, Ik) in the dataset, we initially tokenize Pk into m tokens and extract n objects from the image Ik using Faster R-CNN (Chen et al., 2019). To obtain features in the same d-dim embedding space, we employ frozen CLIP (Radford et al., 2021) model to extract multimodal features Tk and Vk, i.e,
Tk = CLIP (Pk)
= [tCLSk , t 1 k, t 2 k, · · · , tmk ] ∈ R(m+1)×d,
Vk = CLIP (Ik)
= [vCLSk , v 1 k, v 2 k, · · · , vnk ] ∈ R(n+1)×d,
where tCLSk denotes the encoded representation of the [CLS] token, while vCLSk represents the encoding of the entire image.
We construct a multimodal graph Gk for (Pk, Ik) to leverage the initial representation of the multimodal graph using relationships between post tokens and image objects. Specifically, we consider the embeddings of the post tokens in Tk and the embeddings of objects in Vk as nodes in the graph Gk. The node matrix is the concatenation of Tk and Vk, denoted as: Hk = [Tk, Vk] ∈ R(m+n+2)×d. The edge weight coefficients are initialized by computing the similarity between nodes and then scaled to range between [0, 1], i.e, Ai,jk = h i k · h j k/2∥hik∥∥h j k∥, where hik and h j k are node features, hik, h j k ∈ Hk. To extract eventspecific subgraphs GPk and GIk corresponding to posts and images within the news graph Gk, we employing the approach depicted in Fig.3. Specifically, we utilize both the Stanford NLP (Manning et al., 2014) and TextSmart NLP tools (Zhang et al.; Liu et al.) to perform NER on English and Chinese posts, obtaining the identification of subject tsk, object entity tok, and location adverbial t loc k from Tk. These identified entities are then linked to tCLSk to form the post-event subgraph GPk , with nodes represented as HPk = [t s k, t o k, t loc k ]. To establish the mapping between textual entities and their corresponding image nodes within the GIk subgraph, we select the image object with the highest similarity as the representation of the textual entity nodes, denoted as HIk = [v s k, v o k, v loc k ].
In order to emphasize the events within the news, we apply weighting to the predicate entity and tCLSk , resulting in an enhanced representation of the post denoted as t′CLSk = (t CLS k + t p k)/2, where tpk represents the predicate entity within the post.
For any missing entities, we substitute them with zero-vectors of matching dimensions to denote the absence of critical entities within the news content. The initial weights of edges Akp and A k I within the subgraphs are set to 1. Subsequently, we employ an L-layer Graph Convolutional Network (GCN) (Kipf and Welling, 2016) for learning the multimodal graph. The features at the l-th layer are computed by:
H lk = ReLU(Ã l kH l−1 k W l), (1)
where Ãlk = D − 1 2 k AkD − 1 2
k and Dk represent the degree matrix of the initial weightsAk. W l denotes the learnable parameters. Subsequently, separate convolutions are applied to the event subgraphs of posts and images to obtain the node representations for the i-th layer of the event graph, i.e,
H lPk = ReLU(Ã l Pk H l−1Pk W l p), (2)
H lIk = ReLU(Ã l Ik H l−1Ik W l i ), (3)
where ÃlPk and Ã l Ik represent the normalized adjacency matrices for the event graphs of posts and images, respectively. W lp and W l i denote the learnable parameters for the event graphs associated with posts and images. Inspired by(Sheng et al., 2021), the edge weights among all entities are dynamically adjusted based on the latest representations, which aims to better reflect the relevance of entities within the events, i.e,
∆Alk = σ ( H lkW l aH l k T ) ,
Alk = αA l−1 k + (1− α)∆Alk,
where Wla denotes the learnable parameters, σ represents the sigmoid function, and α stands for the hyperparameter determining the update rate. Finally, we utilize a comparative function (Shen et al.,
2018) to perform graph-to-graph comparison between post events and image events, capturing the inconsistency of the event across modalities, denoted as xck, i.e,
xck =Wc[H L Pk , HLIk , H L PK −HLIk , H L Pk ⊙HLIk ] ∈ R d,
where Wc represents the learnable parameters, ⊙ denotes the Hadamard product.

heading: 3.2 Emotion and Pattern Encoder
text: Emotion encoder. We follow (Zhang et al., 2021b) and extract the emotion feature of the news publisher from the original post Pk, i.e,
xek = femo(Pk). (4)
Pattern encoder. The general distribution of the image and the minute traces left by manipulating or compression are defined as image patterns.We employ Multi-Head Self-Attention (MHSA) network to encode image features transformed by Discrete Cosine Transform (Liu and Li, 2003), i.e,
xfk = 1
lI
lI∑
j=0
MHSA(DCT (fj)) ∈ Rd, (5)
where MHSA(·)(Vaswani et al., 2017) represents the Multi-Head Self-Attention network;lI represents the number of image patches; DCT (·) signifies the Discrete Cosine Transform (Liu and Li, 2003); fj stands for the j-th patch of the image Ik.

heading: 3.3 Single view credibility calculation
text: The confidence levels of the three mentioned features vary for different multimodal fake news detection scenarios. Intuitively, calculating credibility and integrating them can enhance the detection performance. TMC (Han et al., 2021) has demonstrated that utilizing the Dirichlet distribution can effectively estimate the credibility of a single view. As the Beta distribution serves as a dimensionality reduction of the Dirichlet distribution and shares the same mathematical significance in binary classification scenarios, we interpret the output before the softmax operation of the classifier for the v-th view as the "evidence" ev for inferring fake news. This "evidence" quantifies the support for the classification result gathered from the input and is employed to derive the parameters βv of the Beta distribution, i.e,
evr = Softplus(o v r) (6) βvr = 1 + e v r (7)
for r ∈ {0, 1}, where ovr represents the output of the final layer of the classifier model for the v-th view regarding the r-th classification result. Consequently, we infer the credible quality bvk of classifying the news into the k-th class, i.e, bvr = e v r/S
v, where Sv = βv0 + β v 1 represents the strength of the beta distribution. Beta distribution parameterizes the "evidence" as credible quality and serves as the conjugate prior for the classification distribution. We connect the parameters of the beta distribution to the uncertainty of the model’s classification uv using the Subjective Logic Theory framework (Jsang, 2018). Specifically, the sum of credible quality and uncertainty for the classification of real and fake news under a certain view is constrained to be 1, i.e, uv + bv0 + b v 1 = 1. q
v characterizes the credibility of a particular view clue, which will be directly used in the fusion process of multi-view features. Certainly, it’s straightforward to understand that the credibility qv of the v-th view can be inferred by subtracting the uncertainty from 1, i.e, qv = 1− uv = bv0 + bv1.
We concatenate the credibility of three views, i.e, modality inconsistency qck, post emotion q e k, and image pattern qfk , to form the credibility vector, i.e,
Qk = [q c k, q e k, q f k ]. (8)

heading: 3.4 Multi-view fusion layer
text: After obtaining the credibility estimates from individual views, the fusion of representations of inconsistency, emotions, and patterns with the corresponding credibility is achieved using a MultiHead Self-Attention Network. This process enables modality interaction by multiplying the representations with their respective credibility, i.e,
xk = MHSA([xck, x e k, x f k ] ·QTk ). (9)
Simultaneously, we evaluated the structural differences among representations from different views (Lei et al., 2022) to enhance the model’s generalization performance, i.e,
x̃k = flatten(sample(M)), (10)
where M denotes the attention matrix, which undergoes downsampling after being flattened. The fused features derived from multiple views enable robust detection of intricate fake news samples within social networks.

heading: 3.5 Training and Inference
text: After combining the features from multiple views, we connect these fused features with the structural difference features. Then, applying a linear transformation, we obtain the predicted results, i.e,
yk =Wo · [xk, x̃k] + bo, (11)
where Wo and bo represent the learnable parameters. During the training process of Event-Radar, we refer to (Kiela et al., 2018) and utilized the the credible loss Lu for each view to ensure the model’s judgments are more confident for each sample, i.e,
Lu(xvk) = ∑
k∈Y ŷk · (ψ (Sv)− ψ (βvk)), (12)
where Y is the annotated label set, ŷk represents the ground truth label and ψ(·) denotes the digamma function. We also incorporate a contrastive learning loss Lc to encourage features to be as distant as possible from distributions with low credibility in the embedding space, i.e,
Lc(sk, qtmin) = ∑(c,e,f) i ̸=t s i k(1− qtmin) + stkqtmin∑(c,e,f)
i s i k
,
where qtmin signifies the value of the lowest credibility among the views, t denotes the view with the lowest credibility and sk = { sck, s e k, s f k } represents the set of similarities between the single-view representations and the fused representation. The overall loss function can be presented as:
L = ∑
k∈Y ŷk log yk + λ1
∑
k∈Y
{c,e,f}∑
v
Lu(xvk)
+ λ2 ∑
k∈Y Lc(sk, uvmin),
(13)
where λ1 and λ2 are hyperparameters used to balance these components.

heading: 4 Experiment

heading: 4.1 Experiment Settings
text: We evaluated the Event-Radar on three widely used benchmarks for fake news detection: Twitter (Boididou et al., 2015), Weibo (wei), and Pheme (Zubiaga et al., 2017). Twitter was released in 2015 at MediaEval, comprising 17673 news. Weibo is the most extensively used Chinese dataset with 9528 news exposing fake news. Pheme is designed for detecting fake news spread on social media and consists of five breaking news stories, encompassing a total of 3670 news. In all of our experiments, we used the division of the original dataset into training and test sets. We selected classic models EANN, SAFE, MVAE, CAFE, MCAN, FND-CLIP, and BMR as strong baselines.
Figure 4: The results of inconsistency studies.
To ensure fairness, we replace the backbones of the latest strong baselines CAFE and MCAN, with CLIP having identical parameters. We also use CLIP+MLP as a comparative baseline. BMR proposed the use of MAE as a more suitable backbone for fake news detection; therefore, we did not alter its backbone. Meanwhile, BMR improved by removing poor-quality samples during data preparation, which, however, cannot address the complex data distribution in the real world. Therefore, during testing, we used the most original data distribution. More details of the implementation and baselines can be found in the appendix.

heading: 4.2 Main Result
text: We evaluated Event-Radar and eight representative baselines on three fake news detection benchmarks. Table.1 presents the results, indicating:
• The performance of Event-Radar consistently outperforms all baseline methods across the three datasets. On average, it achieves an 1.4% increase in accuracy and 1.43% increase in F1 scores compared to baslines on the three datasets.
• It is evident that leveraging the powerful multimodal representation capabilities, the CLIP+MLP method has achieved remarkably good detection performance. While methods like CAFE and MCAN show limited improvement on a CLIPbased backbone, Event-Radar demonstrates higher enhancement due to its ability to model at the event level and encode credibility across multiple views, compared to CLIP+MLP.
• Through multi-view feature modeling, MCAN and BMR have achieved excellent results in inferring fake news. However, subsequent experiments have shown that the ability of multi-view learning is highly sensitive to the quality of news samples. Event-Radar, through the adoption of more effective event modeling and credibility calculation, has
Figure 5: Heatmap visualization. Each cell in the heat maps represents the paired cosine similarity.
demonstrated more promising inferential capabilities and accuracy. Its specific noise resistance will be further validated in robustness experiments.

heading: 4.3 Ablation Studies
text: We conduct further analysis to examine the roles of each module in our proposed model. The corresponding results are shown in Table. 2:
• To validate the reliability of our fusion approach, apart from simple concatenation of features and excluding the enhanced representation loss Lc, we also employed the MOE used in BMR for multimodal fusion as a comparison to validate the effectiveness of our fusion strategy. We observe that while using the MOE fusion method yielded decent results, it fell 0.9% and 1% lower in accuracy and F1 Score respectively compared to Event-Radar. This also shows that our credibility-based fusion approach is effective.
• To assess the effectiveness of utilizing information from each modality, we removed event inconsistency, emotion, and pattern features, comparing these ablated versions with the complete model. The view ablation experiments in the table.2 show that removing any view leads to performance degradation, which emphasizes the advantage of learning fused representations from multiple views to better judge news veracity.

heading: 4.4 Inconsistency Studies
text: To demonstrate our superior ability in measuring multimodal inconsistency, we focused on the multimodal inconsistency view for fake news inference. For a fair comparison, we compared this view with CLIP and CAFE retaining only the inconsistency component. It is evident that our inconsistency modeling capability surpasses the current popular methods in Fig.4. Additionally, we constructed models that do not use event subgraphs and only perform convolution on the multimodal graph G (denoted as "NS"), and models that only construct event subgraphs without convolution (denoted as
"NC"). We tested their inconsistency inference performance to validate the rationality of our inconsistency module design. As observed, our model achieved better accuracy, even outperforming most baselines in the main experiment. In Fig.5, we employed a heatmap visualization to measure the representational ability of the inconsistency module. We selected 60 true news and 60 fake news, calculating the paired similarity between incoming news. It is clear that our inconsistency modeling method exhibits significant intra-class similarity and inter-class differences, demonstrating strong discriminative capabilities.

heading: 4.5 Robustness Study
text: To further validate the ability of Event-Radar to learn from news samples with varying quality, we added Gaussian noise of different intensities to the features of each view in the test data. This was done to simulate information degradation or poor quality in a certain view, assessing the robustness of Event-Radar in integrating multimodal features. As shown in Fig.6, we tested the accuracy after adding noise to each modality and averaged the results to evaluate the performance of each model. Clearly, the performance of all models decreased to some extent after adding noise, while our model’s accuracy dropped less and remained relatively stable. To further explore the underlying mechanisms, we plotted the change in the number of test samples in each credibility interval after adding Gaussian
noise with an intensity of 102 in a certain view in Fig.7. It can be observed that after adding Gaussian noise, the number of samples in the credibility interval of 0 to 0.1 increased by 40%, while the number of high-credibility samples sharply decreased. This indicates that the model adjusts the fusion strategy by reducing the credibility in that view, thereby alleviating the performance decline.

heading: 4.6 Case Study
text: We present the probability and credibility associated with each view while classifying both real and fake news, thereby illustrating the classification process employed by Event-Radar. As shown in Fig. 8, it displays the contribution of specific views and the model’s credibility in each view. We denote the inconsistency between post and image as "C", post emotion as "E", and pattern features as "P". In the first news, although the model classified it as fake news based on emotion and pattern fea-
tures, the credibility for these modalities were low. The model chose to believe the judgment based on event inconsistency, resulting in the correct classification. Similarly, in the second example, the model provided correct credible judgments, leading to the correct classification result.

heading: 5 CONCLUSION
text: Event-Radar is a novel fake news detection framework that demonstrates exceptional event modeling capabilities and significant robustness, aimed at addressing the issue of maliciously crafted fake news. Extensive experiments validate that EventRadar’s classification performance surpasses all listed strong benchmarks. Further research confirms the effectiveness of our initial technical contributions, emphasizing Event-Radar’s ability to resist interference from poor-quality news.

heading: 6 ACKNOWLEDGEMENT
text: This work was supported by the National Key Research and Development Program of China (No. 2022YFB3102600), the National Nature Science Foundation of China (No. 62192781, No. 62272374), the Natural Science Foundation of Shaanxi Province (2024JC-JCQN-62), the National Nature Science Foundation of China (No. 62202367, No. 62250009, No. 62137002), Project of China Knowledge Center for Engineering Science and Technology, and Project of Chinese academy of engineering “The Online and Offline Mixed Educational Service System for ‘The Belt and Road’ Training in MOOC China”. We would like to express our gratitude for the support of K. C. Wong Education Foundation.

heading: 7 LIMITATIONS
text: Our work has two limitations that may impact the generalization ability of our proposed framework. While introducing event graphs has yielded promising results in fake news detection, we have yet to explore event representation learning from a causal relationship perspective. Additionally, the performance of the NER tools and object detection tools used can impact the structure of the event subgraph, thereby influencing the accuracy of event representation. Moreover, although the confidence-based fusion layer used in our work is effective in resisting interference from low-quality samples, extremely small confidence scores may result in an abundance of zero values in the classifier input, posing a risk
of overfitting or gradient vanishing. We plan to address these limitations in future research.

heading: 8 ETHICS STATEMENT
text: This paper adheres to the ACM Code of Ethics and Professional Conduct. Firstly, the dataset utilized does not contain sensitive private information and poses no harm to society. Secondly, proper attribution is given to relevant papers and the sources of pre-trained models, along with detailed references to the toolkits used. Furthermore, our code will be released under the license of any artifacts used. Lastly, the proposed fake news detection method is designed to contribute to the safety and stability of the internet environment and public opinion.

heading: B Baselines
text: To validate the effectiveness of our method, we applied the Event-Radar framework to the following seven strong baselines:
EANN (Wang et al., 2018) trained a fake news classifier based on extracting post events.
SAFE (Zhou et al., 2020) employs consistency as a loss function to optimize the task.
MVAE (Khattar et al., 2019) employs a variational autoencoder to model representations between text and images.
CLIP (Radford et al., 2021) exhibits strong multimodal representation capabilities. We concatenated CLIP with a two-layer MLP for our task.
CAFE (Chen et al., 2022) adaptively aggregates features based on the inherent cross-modal ambiguity, addressing misclassification issues arising from differences between different modalities.
MCAN (Wu et al., 2021) integrates pattern features into the co-attention network. It conducts detection by incorporating multiple views that fuse text, image semantics, and image pattern features.
FND-CLIP (Zhou et al., 2023) leverages the multimodal cognitive capabilities of clip by generating self-directed attentional weights to fuse features through modal similarity computed by CLIP.
BMR (Ying et al., 2023) models news features from multiple views through bootstrap multi-view representations. It utilizes the Mixture of Experts network for the fusion of multi-view features.

heading: C Ablation Studies
text: The detailed abaltion study results in the ablation study are shown in Table4.

heading: D Classification Result
text: The detailed classification results in the main experiment are shown in Table.5.

heading: E Representation Study
text: In Fig.9, we present t-SNE visualizations of different model features learned by Event-Radar, CAFE, MCAN, and BMR on the Twitter test set. In EventRadar, there is a relatively clear boundary between true and false news, and the clustering effect is good, with fewer outliers. This indicates that the features extracted by Event-Radar are more distinctive.

heading: Future Work
text: We plan to address these limitations in future research