Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes

By Srijan Kumar, Robert West, Jure Leskovec. The Web Conference, 2016

325 citations recorded by the source corpus.

Subfields: Communication, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f27848c39e14238e9104b926441c5fc79b84cb4f

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Citations in the corpus

Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence (2021, background): “While adversaries do try to insert disinformation, popular pages are usually quickly corrected (Kumar et al., 2016).”
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (2018, background): “Kumar et al. (2016) studies the problem of detecting hoax articles on Wikipedia.”
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media (2018, background): “For example, some individuals and organizations spread fake news in social media for financial and political gains.(2,3) It is also reported that fake news has an influence on the 2016 U.”
CSI: A Hybrid Deep Model for Fake News Detection (2017, background): “From a slightly dierent point of view, one popular approach has been to measure the response an article received by studying its propagation on a social graph [12, 16, 17, 35].”
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter (2017, background): “In addition to contrasting linguistic realizations behind different types of suspicious news on Twitter, we are interested in qualitatively evaluating differences in retweet patterns across suspicious news types (Lumezanu and Klein, 2012; Mendoza et al., 2010; Kumar et al., 2016).”
Fake news, disinformation and misinformation in social media: a review (2023, unknown): “…Lazer et al. 2018; Weiss et al. 2020; Tandoc Jr et al. 2021; Guadagno and Guttieri 2021), disinformation (Kapantai et al. 2021; Shu et al. 2020a, c; Kumar et al. 2016; Bhattacharjee et al. 2020; Marsden et al. 2020; Jungherr and Schroeder 2021; Star-bird et al. 2019; Ireton and Posetti 2018),…”
False Information on Web and Social Media: A Survey (2018, methodology): “[50] conducted an experiment with hoax articles created by hoaxsters on Wikipedia.”
Quantifying Engagement with Citations on Wikipedia (2020, background): “Automated or semi-automated tools [17, 39, 44] can help improve user experience [29, 69], content variety [42, 67], and quality [1, 20, 28].”
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (2017, background): “Biyani et al. (2016) examine the unique linguistic styles found in clickbait articles, and Kumar et al. (2016) also characterize hoax documents on Wikipedia.”
Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023, background): “While some researchers have explored the psychological foundations of this phenomenon [39], others have advocated unpacking the characteristics of fake news to understand why corrections may not necessarily stick to consumers [61, 85].”
Community Interaction and Conflict on the Web (2018, background): “…primarily on controversies [4, 13, 20, 21, 45, 52] and anti-social behavior, in the form of trolling [11, 12, 42], sockpup-petry [39], harassment and cyberbullying [8, 23, 32, 35, 55, 66, 79], vandalism [43], hate speech [9, 15, 51, 56, 72], and others [17, 24, 40, 41, 44, 46, 54, 68, 78, 81].”