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Analysing How People Orient to and Spread Rumours in Social Media by Looking at
Conversational Threads
#MMPMID26943909
Zubiaga A
; Liakata M
; Procter R
; Wong Sak Hoi G
; Tolmie P
PLoS One
2016[]; 11
(3
): e0150989
PMID26943909
show ga
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.
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