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As breaking news unfolds people increasingly rely on social media to stayabreast of the latest updates. The use of social media in such situations comeswith the caveat that new information being released piecemeal may encouragerumours, many of which remain unverified long after their point of release.Little is known, however, about the dynamics of the life cycle of a socialmedia rumour. In this paper we present a methodology that has enabled us tocollect, identify and annotate a dataset of 330 rumour threads (4,842 tweets)associated with 9 newsworthy events. We analyse this dataset to understand howusers spread, support, or deny rumours that are later proven true or false, bydistinguishing two levels of status in a rumour life cycle i.e., before andafter its veracity status is resolved. The identification of rumours associatedwith each event, as well as the tweet that resolved each rumour as true orfalse, was performed by a team of journalists who tracked the events in realtime. Our study shows that rumours that are ultimately proven true tend to beresolved faster than those that turn out to be false. Whilst one can readilysee users denying rumours once they have been debunked, users appear to be lesscapable of distinguishing true from false rumours when their veracity remainsin question. In fact, we show that the prevalent tendency for users is tosupport every unverified rumour. We also analyse the role of different types ofusers, finding that highly reputable users such as news organisations endeavourto post well-grounded statements, which appear to be certain and accompanied byevidence. Nevertheless, these often prove to be unverified pieces ofinformation that give rise to false rumours. Our study reinforces the need fordeveloping robust machine learning techniques that can provide assistance forassessing the veracity of rumours.

Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads