  While social networks can provide an ideal platform for up-to-date
information from individuals across the world, it has also proved to be a place
where rumours fester and accidental or deliberate misinformation often emerges.
In this article, we aim to support the task of making sense from social media
data, and specifically, seek to build an autonomous message-classifier that
filters relevant and trustworthy information from Twitter. For our work, we
collected about 100 million public tweets, including users' past tweets, from
which we identified 72 rumours (41 true, 31 false). We considered over 80
trustworthiness measures including the authors' profile and past behaviour, the
social network connections (graphs), and the content of tweets themselves. We
ran modern machine-learning classifiers over those measures to produce
trustworthiness scores at various time windows from the outbreak of the rumour.
Such time-windows were key as they allowed useful insight into the progression
of the rumours. From our findings, we identified that our model was
significantly more accurate than similar studies in the literature. We also
identified critical attributes of the data that give rise to the
trustworthiness scores assigned. Finally we developed a software demonstration
that provides a visual user interface to allow the user to examine the
analysis.
