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.

Preview	PDF WRAP-detection-resolution-rumours-social-media-Zubiaga-2018.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (834Kb) | Preview
	PDF WRAP-detection-resolution-rumours-social-media-Zubiaga-2017.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (654Kb)

Request Changes to record.

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:	Date Event March 2018 Published 20 February 2018 Available 10 November 2017 Accepted
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 ID RIOXX Funder Name Funder ID 611233 Seventh Framework Programme http://dx.doi.org/10.13039/100011102 EP/I004327/1 [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

Request changes or add full text files to a record

Repository staff actions (login required)

View Item

Downloads