Zubiaga, Arkaitz, Wong Sak Hoi, Geraldine, Liakata, Maria and Procter, Rob (2016) PHEME dataset of rumours and non-rumours. [Dataset]

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Abstract

Breaking news leads to situations of fast-paced reporting in social media, producing all kinds of updates related to news stories, albeit with the caveat that some of those early updates tend to be rumours, i.e., information with an unverified status at the time of posting. Flagging information that is unverified can be helpful to avoid the spread of information that may turn out to be false. Detection of rumours can also feed a rumour tracking system that ultimately determines their veracity. In this paper we introduce a novel approach to rumour detection that learns from the sequential dynamics of reporting during breaking news in social media to detect rumours in new stories. Using Twitter datasets collected during five breaking news stories, we experiment with Conditional Random Fields as a sequential classifier that leverages context learnt during an event for rumour detection, which we compare with the state-of-the-art rumour detection system as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying a piece of information to deem it a rumour, but instead we detect rumours from the tweet alone by exploiting context learnt during the event. Our classifier achieves competitive performance, beating the state-of-the-art classifier that relies on querying tweets with improved precision and recall, as well as outperforming our best baseline with nearly 40% improvement in terms of F1 score. The scale and diversity of our experiments reinforces the generalisability of our classifier.

Item Type:	Dataset
Alternative Title:	Data for Detection and resolution of rumours in social media : a survey
Subjects:	H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Type of Data:	Observational data
Library of Congress Subject Headings (LCSH):	Social media, Natural language processing (Computer science), Data mining
Publisher:	University of Warwick, Department of Computer Science
Official Date:	24 October 2016
Dates:	Date Event 24 October 2016 Published
Status:	Not Peer Reviewed
Publication Status:	Published
Media of Output (format):	.json
Access rights to Published version:	Open Access (Creative Commons)
Copyright Holders:	University of Warwick
Description:	Data record consists of a zip archive containing sub-folders organised according to event and an accompanying readme file. The data is structured as follows. Each event has a directory, with two subfolders, rumours and non-rumours. These two folders have folders named with a tweet ID. The tweet itself can be found on the 'source-tweet' directory of the tweet in question, and the directory 'reactions' has the set of tweets responding to that source tweet. Twitter retains the ownership and rights of the content of the tweets.
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID 611233 Seventh Framework Programme http://dx.doi.org/10.13039/100011102 EP/K000128/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 687847 H2020 European Research Council http://dx.doi.org/10.13039/100010663 654024 H2020 European Research Council http://dx.doi.org/10.13039/100010663 UNSPECIFIED Alan Turing Institute http://dx.doi.org/10.13039/100012338
Related URLs:	Related item in WRAP Other Other Other
Contributors:	Contribution Name Contributor ID Contact Person Zubiaga, Arkaitz 64180

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