MVAE: Multimodal Variational Autoencoder for Fake News Detection

By Dhruv Khattar, Jaipal Singh Goud, Manish Gupta, Vasudeva Varma. The Web Conference, 2019

840 citations recorded by the source corpus.

In recent times, fake news and misinformation have had a disruptive and adverse impact on our lives. Given the prominence of microblogging networks as a source of news for most individuals, fake news now spreads at a faster pace and has a more profound impact than ever before. This makes detection of fake news an extremely important challenge. Fake news articles, just like genuine news articles, leverage multimedia content to manipulate user opinions but spread misinformation. A shortcoming of the current approaches for the detection of fake news is their inability to learn a shared representation of multimodal (textual + visual) information. We propose an end-to-end network, Multimodal Variational Autoencoder (MVAE), which uses a bimodal variational autoencoder coupled with a binary classifier for the task of fake news detection. The model consists of three main components, an encoder, a decoder and a fake news detector module. The variational autoencoder is capable of learning probabilistic latent variable models by optimizing a bound on the marginal likelihood of the observed data. The fake news detector then utilizes the multimodal representations obtained from the bimodal variational autoencoder to classify posts as fake or not. We conduct extensive experiments on two standard fake news datasets collected from popular microblogging websites: Weibo and Twitter. The experimental results show that across the two datasets, on average our model outperforms state-of-the-art methods by margins as large as ~ 6% in accuracy and ~ 5% in F1 scores.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/2f42ec0d6fe1172122333b36337f09ff5d3d400b

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Citations in the corpus

Deep Multimodal Data Fusion (2024, background): “Khattar et al. [81] propose a VAE-based end-to-end architecture for fake news classification problem.”
SpotFake: A Multi-modal Framework for Fake News Detection (2019, methodology): “They named it as Multimodal Variational Autoencoder for Fake News Detection (MVAE).”
Media Forensics and DeepFakes: An Overview (2020, background): “isolation but together with the accompanying text [272], audio [273], and all available contextual information [274].”
DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification (2020, background): “…Many studies on claim veri-ﬁcation generally extract an appreciable quantity of credibility-indicative features around semantics (Ma et al., 2018b; Khattar et al., 2019; Wu et al., 2020), emotions (Ajao et al., 2019), stances (Ma et al., 2018a; Kochkina et al., 2018; Wu et al., 2019), write…”
Cross-modal Ambiguity Learning for Multimodal Fake News Detection (2022, methodology): “Previous works inmultimodal fake news detection have used simple concatenation as the fusion approach [13, 15], which may fail to capture complex cross-modal interactions [1].”
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, methodology): “We use the same model as in the original work (Khattar et al., 2019).”
Hierarchical Multi-modal Contextual Attention Network for Fake News Detection (2021, methodology): “We compare the proposed HMCAN with stateof-the-art baselines on three public datasets: WEIBO [12], TWITTER [12, 13], and PHEME [42].”
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, methodology): “Textual [6, 34, 39, 44] and visual [18, 43] features have been widely used to model news article contents, either by feature extraction, unsupervised semantics encoding, or learned representation.”
A Survey on Deep Learning for Multimodal Data Fusion (2020, background): “Khattar, Goud, Gupta, and Varma (2019) designed a multimodal variational framework based on the encoder-decoder architecture.”