Predicting information credibility in time-sensitive social media

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 Twitter topics in Spanish, automatically detected during a natural disaster. Findings - There are measurable differences in the way microblog messages propagate. The paper shows that these differences are related to the newsworthiness and credibility of the information conveyed, and describes features that are effective for classifying information automatically as credible or not credible. Originality/value - The paper first tests the approach under normal conditions, and then the paper extends the findings to a disaster management situation, where many news and rumors arise. Additionally, by analyzing the transfer of our classifiers across languages, the paper is able to look more deeply into which topic-features are more relevant for credibility assessment. To the best of our knowledge, this is the first paper that studies the power of prediction of social media for information credibility; considering model transfer into time-sensitive and language-sensitive contexts.