A Survey of Fake News

By Xinyi Zhou. 2020

913 citations recorded by the source corpus.

The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news detection and intervention. This survey reviews and evaluates methods that can detect fake news from four perspectives: the false knowledge it carries, its writing style, its propagation patterns, and the credibility of its source. The survey also highlights some potential research tasks based on the review. In particular, we identify and detail related fundamental theories across various disciplines to encourage interdisciplinary research on fake news. It is our hope that this survey can facilitate collaborative efforts among experts in computer and information sciences, social sciences, political science, and journalism to research fake news, where such efforts can lead to fake news detection that is not only efficient but, more importantly, explainable.

Subfields: Information Systems, Sociology and Political Science, Natural Language Processing, Safety, Robustness & Interpretability, Signal Processing

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “Studies on these tasks have explored (1) modifying attention mechanisms to specifically capture relationships between entities (Nan et al., 2021; Zhu et al., 2021), (2) improving coreference to mitigate gender bias in translation (Levy et al., 2021), and (3) using text entailment to develop loss…”
User Preference-aware Fake News Detection (2021, background): “3462990 disguised in news articles to mislead consumers [27, 37].”
Automated Fact-Checking for Assisting Human Fact-Checkers (2021, background): “In the literature, there have been a number of surveys on “fake news” [Zhou and Zafarani, 2020; Cardoso Durier da Silva et al., 2019], misinformation [Islam et al., 2020], factchecking [Kotonya and Toni, 2020], truth discovery [Li et al., 2016], and propaganda detection [Martino et al., 2020].”
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation (2021, background): “Reference-free Detection Approaches Reference-free hallucination detection is closely related to fake news detection (Zellers et al., 2019; Zhou and Zafarani, 2020; Zhong et al., 2020), which aims to identify deliberate disinformation in a reference-free manner on social media and usually involves c”
Fake news, disinformation and misinformation in social media: a review (2023, background): “However, they mostly focus on studying approaches from a machine learning perspective (Bondielli and Marcelloni 2019), data mining perspective (Shu et al. 2017), crowd intelligence perspective (Guo et al. 2020), or knowledge-based perspective (Zhou and Zafarani 2020).”
Deep learning for deepfakes creation and detection: A survey (2019, background): “cal or religion tensions between countries, to fool public and affect results in election campaigns, or create chaos in ﬁnancial markets by creating fake news arXiv:1909.11573v3 [cs.CV] 26 Apr 2021 2 [9]–[11]. It can be even used to generate fake satellite images of the Earth to contain objects that”