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PHEME dataset of rumours and non-rumours
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Zubiaga, Arkaitz, Wong Sak Hoi, Geraldine, Liakata, Maria and Procter, Rob (2016) PHEME dataset of rumours and non-rumours. [Dataset]
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Official URL: https://doi.org/10.6084/m9.figshare.4010619.v1
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 | |||||||||
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| 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 |
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| Divisions: | Faculty of Science > Computer Science | |||||||||
| Type of Data: | Observational data | |||||||||
| Library of Congress Subject Headings (LCSH): | Social media, Natural language processing (Computer science), Data mining | |||||||||
| Publisher: | Figshare | |||||||||
| Official Date: | 24 October 2016 | |||||||||
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| Status: | Not Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Media of Output: | .json | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| 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. |
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