Predicting information credibility in time-sensitive social media

By Carlos Castillo, Marcelo Mendoza, Bárbara Poblete. 2013

348 citations recorded by the source corpus.

Purpose – Twitter is a popular microblogging service which has proven, in recent years, its potential for propagating news and information about developing events. The purpose of this paper is to focus on the analysis of information credibility on Twitter. The purpose of our research is to establish if an automatic discovery process of relevant and credible news events can be achieved. Design/methodology/approach – The paper follows a supervised learning approach for the task of automatic classification of credible news events. A first classifier decides if an information cascade corresponds to a newsworthy event. Then a second classifier decides if this cascade can be considered credible or not. The paper undertakes this effort training over a significant amount of labeled data, obtained using crowdsourcing tools. The paper validates these classifiers under two settings: the first, a sample of automatically detected Twitter “trends” in English, and second, the paper tests how well this model transfers to...

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Testing the Effectiveness of Correction Placement and Type on Instagram (2020, background): “Several social media studies have examined perceived source and medium credibility, without investigating the perceived credibility of the social media content itself (e.g., Castillo et al. 2013).”
Misinformation in Social Media: Definition, Manipulation, and Detection (2019, methodology): “The methods either aim to find those suspicious instances [29; 59], or find credible clusters of discussions [6; 7; 52].”
Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads (2015, background): “For example, Castillo et al. [Castillo et al., 2013] found that the ratio between tweets supporting and debunking false rumours was 1:1 (one supporting tweet per debunking tweet) in the case of a 2010 earthquake in Chile.”
Overview of the Special Issue on Trust and Veracity of Information in Social Media (2016, background): “For instance, during an earthquake in Chile, rumors spread through Twitter that a volcano became active and there was a tsunami warning in Valparaiso [Castillo et al. 2013].”
Detection and Resolution of Rumours in Social Media (2017, background): “[Castillo et al. 2013] found that the ratio between tweets supporting and debunking false rumours was 1:1 (one supporting tweet per debunking tweet) in the case of a 2010 earthquake in Chile.”
Measuring Message Credibility (2016, background): “Other studies point to an even graver issue—that of not providing a definition at all (e.g., Borah, 2013; Castillo et al., 2013; Gaziano & McGrath, 1986; Hovland & Weiss, 1951).”
Modelling users' trust in online health rumours: an experiment-based study in China (2021, result): “It has beeen found that users trust tweets that contain more hyperlinks, and similar results were obtained for online health rumours in particular: users trusted the ones with hyperlinks (Castillo et al., 2013; Zhang et al., 2015).”
Processing Social Media Messages in Mass Emergency (2014, methodology): “Automatic classification can be used to filter out content that is unlikely to be considered credible [Gupta and Kumaraguru 2012; Castillo et al. 2013] or to annotate messages seen by users with credibility scores automatically [Gupta et al. 2014].”