Lies, Damn Lies and Viral Content

By Craig L. Silverman. 2015

Subfields: AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Automated Fact Checking: Task Formulations, Methods and Future Directions (2018, methodology): “Ferreira and Vlachos (2016) released a dataset for rumor debunking using data collected from the Emergent project (Silverman, 2015).”
Misleading Online Content: Recognizing Clickbait as "False News" (2015, background): “Keywords Clickbait, fake news, news verification, automated deception detection, automation, reader behavior.”
Swedish teenagers’ difficulties and abilities to determine digital news credibility (2019, background): “New media and modern journalism can, in many ways, facilitate the spread of exaggerations and lies (Del Vicario et al., 2016; Silverman, 2015).”
A survey on fake news and rumour detection techniques (2019, background): “In addition, clickbait headlines have been identified as one of the major contributors to the spread of fake news over the web [86].”
Emergent: a novel data-set for stance classification (2016, methodology): “Proceedings of NAACL-HLT 2016, pages 1163–1168, San Diego, California, June 12-17, 2016. c©2016 Association for Computational Linguistics”
Combating Fake News (2019, background): “Silverman [102] observed that 13% of 1,600 news articles had incoherent headlines and content and used declarative headlines pairedwith article bodies that are skeptical about the claim in the headline [54].”
Detection and Resolution of Rumours in Social Media (2017, background): “…the ACM TOIS journal [Papadopoulos et al. 2016; Rijke 2016], Full Fact’s report on The State of Automated Factchecking [Babakar and Moy 2016], reports and discussion on rumours on Snopes30 and Craig Silverman’s books on rumours and journalistic verification practices [Silverman 2013; 2015a; 2015b].”