FANG: Leveraging Social Context for Fake News Detection Using Graph Representation

By Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, Min-Yen Kan. International Conference on Information and Knowledge Management, 2020

336 citations recorded by the source corpus.

We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection. Unlike previous contextual models that have targeted performance, our focus is on representation learning. Compared to transductive models, FANG is scalable in training as it does not have to maintain all nodes, and it is efficient at inference time, without the need to re-process the entire graph. Our experimental results show that FANG is better at capturing the social context into a high fidelity representation, compared to recent graphical and non-graphical models. In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data. We further demonstrate that the representations learned by FANG generalize to related tasks, such as predicting the factuality of reporting of a news medium.

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

View Paper: https://www.semanticscholar.org/paper/197c628a1fed591469e7ee6ff1c2d33074979c82

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Citations in the corpus

Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document (2021, neutral): “…has been proposed as an alternative (Li et al., 2016; Shu et al., 2017; Rashkin et al., 2017; Hassan et al., 2017; Vo and Lee, 2018; Lee et al., 2018; Li et al., 2018; Thorne and Vlachos, 2018; Lazer et al., 2018; Vosoughi et al., 2018; Zhang et al., 2020b; Alam et al., 2022; Nguyen et al., 2022).”
Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2022, methodology): “Evaluation Data We use two sets of human-written articles released by Nguyen et al. (2020) and Shu et al. (2018) to evaluate the effectiveness of our approach.”
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “…may also have real and adverse impacts, for instance, fake news in social media can create panic and chaos with mis-leading beliefs [2], [3], [4], [5], untrustworthy reviews in online review systems can affect customers’ shopping choices [6], [7], [8], network intrusions might leak private…”