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Determining the Veracity of Rumours on Twitter

Giasemidis, Georgios; Singleton, Colin; Agrafiotis, Ioannis; Nurse, Jason R. C.; Pilgrim, Alan; Willis, Chris and Greetham, D. V. (2016). Determining the Veracity of Rumours on Twitter. In: Social Informatics. SocInfo 2016. (Spiro, E. and Ahn, Y. Y. eds.), Lecture Notes in Computer Science, Springer, pp. 185–205.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-47880-7_12
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Abstract

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 mis- information 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.

Item Type: Conference or Workshop Item
Copyright Holders: 2016 Springer
ISBN: 3-319-47879-6, 978-3-319-47879-1
ISSN: 0302-9743
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
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Item ID: 55705
Depositing User: Danica Vukadinovic Greetham
Date Deposited: 18 Feb 2019 13:54
Last Modified: 04 May 2019 11:30
URI: http://oro.open.ac.uk/id/eprint/55705
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