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Detection and resolution of rumours in social media : a survey

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Zubiaga, Arkaitz, Ahmet, Aker, Bontcheva, Kalina, Liakata, Maria and Procter, Rob (2018) Detection and resolution of rumours in social media : a survey. ACM Computing Surveys, 51 (2). pp. 32-67. ISSN 0360-0300.

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Official URL: https://doi.org/10.1145/3161603

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Abstract

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e. items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this survey we introduce and discuss two types of rumours that circulate on social media; long-standing rumours that circulate for long periods of time, and newly-emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far towards the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

Item Type: Journal Article
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
Library of Congress Subject Headings (LCSH): Social media, Natural language processing (Computer science), Data mining
Journal or Publication Title: ACM Computing Surveys
Publisher: Association for Computing Machinery
ISSN: 0360-0300
Official Date: March 2018
Dates:
DateEvent
March 2018Published
20 February 2018Available
10 November 2017Accepted
Volume: 51
Number: 2
Page Range: pp. 32-67
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 19 December 2017
Date of first compliant Open Access: 16 March 2018
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
611233Seventh Framework Programmehttp://dx.doi.org/10.13039/100011102
EP/I004327/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
687847H2020 European Research Councilhttp://dx.doi.org/10.13039/100010663
654024H2020 European Research Councilhttp://dx.doi.org/10.13039/100010663
UNSPECIFIEDAlan Turing Institutehttp://dx.doi.org/10.13039/100012338

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