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 a team of journalists 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 for
assessing the veracity of rumours.

本文的研究对象是社交媒体上流传的谣言，以及它们的真实性。研究者通过采集了 9 个事件中包括 330 条谣言推文在内的数据，并应用机器学习算法进行分析，发现了在谣言存在疑问时，人们更倾向于支持这些未经证实的谣言。研究结果表明需要开发更为高效的机器学习算法来判断谣言的真实性，从而避免虚假信息的散播。