Hoaxy: A Platform for Tracking Online Misinformation

By Chengcheng Shao, Giovanni Luca Ciampaglia, Alessandro Flammini, Filippo Menczer. 2016

413 citations recorded by the source corpus.

Massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media. Examples include rumors, hoaxes, fake news, and conspiracy theories. At the same time, several journalistic organizations devote significant efforts to high-quality fact checking of online claims. The resulting information cascades contain instances of both accurate and inaccurate information, unfold over multiple time scales, and often reach audiences of considerable size. All these factors pose challenges for the study of the social dynamics of online news sharing. Here we introduce Hoaxy, a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts. We discuss the design of the platform and present a preliminary analysis of a sample of public tweets containing both fake news and fact checking. We find that, in the aggregate, the sharing of fact-checking content typically lags that of misinformation by 10-20 hours. Moreover, fake news are dominated by very active users, while fact checking is a more grass-roots activity. With the increasing risks connected to massive online misinformation, social news observatories have the potential to help researchers, journalists, and the general public understand the dynamics of real and fake news sharing.

Subfields: Communication, Information Systems, Sociology and Political Science, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Defending Against Neural Fake News (2019, methodology): “Some users of these platforms avoid fake news with tools such as NewsGuard and Hoaxy (Shao et al., 2016) and websites like Snopes and PolitiFact.”
FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media (2018, background): “First, tracking the life cycle of fake news on social media requires recording essential trajectories of fake news diffusion in general (Shao et al. 2016).”
The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016 (2018, background): “Shao et al. (2016) created and maintain a meta-analysis of fake news websites for use with their service Hoaxy, which tracks fake news media from nine different sources such as US News and World Report, CBS News, and Snopes Field Guide.5 For this study, we included a fake news website in the…”
The spread of low-credibility content by social bots (2017, neutral): “The analysis in the main text leverages Hoaxy, a system focused on tracking the spread of links to articles from low-credibility and fact-checking sources [46].”
Fake news, disinformation and misinformation in social media: a review (2023, neutral): “…Marsden et al. 2020; Jungherr and Schroeder 2021; Star-bird et al. 2019; Ireton and Posetti 2018), misinformation (Wu et al. 2019; Shu et al. 2020c; Shao et al. 2016, 2018b; Pennycook and Rand 2019; Micallef et al. 2020), malinfor-mation (Dame Adjin-Tettey 2022) (Carmi et al. 2020; Shu et al.…”
Quantifying Biases in Online Information Exposure (2018, background): “With increased focus on such activity (Lazer et al., 2018) and the emergence of services for its tracking (Shao, Ciampaglia, Flammini, & Menczer, 2016), we can examine whether users or communities characterized by high bias are more likely to spread such misinformation.”
This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News (2017, background): “launched a platform for tracking online misinformation called Hoaxy (Shao et al. 2016).”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “of how particular stories \go viral" from normal public discourse. Tracking the life cycle of fake news on social media requires recording essential trajectories of fake news diusion in general [71], as well as further investigations of the process for specic fake news pieces, such as graph-bas”
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours (2017, background): “After initial work (Qazvinian et al., 2011), increasingly advanced systems and annotation schemas have been developed to support the analysis of rumour and misin-formation in text (Kumar and Geethakumari, 2014; Zhang et al., 2015; Shao et al., 2016; Zubiaga et al., 2016b).”
False Information on Web and Social Media: A Survey (2018, background): “False information spreads far and wide on social media because there is an average delay of 12 hours between start of false information spread and that of its debunking information [86, 121].”