title: Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads.
abstract: As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumours, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumour. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumour threads (4,842 tweets) associated with 9 newsworthy events. We analyse this dataset to understand how users spread, support, or deny rumours that are later proven true or false, by distinguishing two levels of status in a rumour life cycle i.e., before and after its veracity status is resolved. The identification of rumours associated with each event, as well as the tweet that resolved each rumour as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumours once they have been debunked, users appear to be less capable of distinguishing true from false rumours when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumour. We also analyse the role of different types of users, finding that highly reputable users such as news organisations endeavour to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumours. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours. The findings of our study provide useful insights for achieving this aim.
authors.affiliation: Department of Computer Science, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, United Kingdom.
authors.forename: Arkaitz
authors.initials: A
authors.lastname: Zubiaga
authors.affiliation: Department of Computer Science, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, United Kingdom.
authors.forename: Maria
authors.initials: M
authors.lastname: Liakata
authors.affiliation: Department of Computer Science, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, United Kingdom.
authors.forename: Rob
authors.initials: R
authors.lastname: Procter
authors.affiliation: swissinfo.ch, Bern, Switzerland.
authors.forename: Geraldine
authors.initials: G
authors.lastname: Wong Sak Hoi
authors.affiliation: Department of Computer Science, University of Warwick, Gibbet Hill Road, CV4 7AL Coventry, United Kingdom.
authors.forename: Peter
authors.initials: P
authors.lastname: Tolmie
mesh_terms: D003142:Communication; D003717:Denial, Psychological; D061108:Social Media; D012944:Social Support