Beyond News Contents: The Role of Social Context for Fake News Detection

By Kai Shu. Web Search and Data Mining, 2017

672 citations recorded by the source corpus.

Social media is becoming popular for news consumption due to its fast dissemination, easy access, and low cost. However, it also enables the wide propagation of fake news, i.e., news with intentionally false information. Detecting fake news is an important task, which not only ensures users receive authentic information but also helps maintain a trustworthy news ecosystem. The majority of existing detection algorithms focus on finding clues from news contents, which are generally not effective because fake news is often intentionally written to mislead users by mimicking true news. Therefore, we need to explore auxiliary information to improve detection. The social context during news dissemination process on social media forms the inherent tri-relationship, the relationship among publishers, news pieces, and users, which has the potential to improve fake news detection. For example, partisan-biased publishers are more likely to publish fake news, and low-credible users are more likely to share fake news. In this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world datasets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8aa0872637794215c95d313ae35f3f16461a86df

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Citations in the corpus

A Survey on Natural Language Processing for Fake News Detection (2018, methodology): “For example, in Shu et al. (2017b) the model using only social engagements data (id 19,25) defeated the model using only textual data (id 17,18,23,24).”
Stance Detection on Social Media: State of the Art and Trends (2020, neutral): “A more recent study by [Shu et al. 2019] used three features extracted from user’s interactions, news author and contents of news article to better detect the fake-news.”
FakeBERT: Fake news detection in social media with a BERT-based deep learning approach (2021, methodology): “In the existing approaches [1, 33, 40], for the detection of fake news, many useful methods have been presented using traditional machine learning models.”
Fake News Detection on Social Media using Geometric Deep Learning (2019, background): “Social context features include user demographics (such as age, gender, education, and political afﬁliation [37, 21]), social network structure [38, 35] (in the form of connections between users such as friendship or follower/followee relations) and user reactions (e.g. posts accompanying a news…”
A Survey of Fake News (2020, methodology): “…such as squared error [Ma et al. 2018] and cross-entropy [Zhang et al. 2018], using some optimization algorithm, such stochastic gradient descent (SGD) [Wang et al. 2018], Adam [Kingma and Ba 2014], and the alternating direction method of multipliers (ADMM) [Boyd et al. 2011; Shu et al. 2019c].”
Mining Dual Emotion for Fake News Detection (2019, background): “Beyond news content, social contexts such as texts of comments and reposts, viewpoints and stances of the crowd, and user credibility are emphasized as well [9, 11, 13, 23, 25].”
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, methodology): “The Tri-Relationship Fake News (TriFN) detection framework [39] – although similar to our approach – neither differentiated user engagements in terms of stance and temporal patterns, nor modeled source–source citations.”
Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023, background): “While most detection eforts focus on content-level features, some work highlights the importance of contextual factors and proposes frameworks to translate and operationalize publisher-news-user tri-relationship [97] and intrinsic uncertainty of misinformation detection [58].”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, methodology): “Existing studies often connect fake news to terms and concepts such asmaliciously false news [Allcott and Gentzkow 2017; Shu et al. 2017a; Waldrop 2017], false news [Vosoughi et al. 2018], satire news [Berkowitz and Schwartz 2016], disinformation (i.e., deception) [Kshetri and Voas 2017],…”
dEFEND: Explainable Fake News Detection (2019, neutral): “Network-based features are extracted by constructing specific networks, such as the diffusion networks [46], interaction networks [41], and propagation networks [30, 39].”
Fake news, disinformation and misinformation in social media: a review (2023, neutral): “User-based aspects refer to information captured from OSN users such as user profile information (Shu et al. 2019b; Wang et al. 2019c; Hamdi et al. 2020; Nyow and Chua 2019; Jiang et al. 2019) and user behavior (Cardaioli et al. 2020) such as user engagement (Uppada et al. 2022; Jiang et al. 2019;…”