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Crowdsourcing the annotation of rumourous conversations in social media

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Zubiaga, Arkaitz, Liakata, Maria, Procter, Rob, Bontcheva, Kalina and Tolmie, Peter (2015) Crowdsourcing the annotation of rumourous conversations in social media. In: World Wide Web Conference, Florence, Italy, 18-22 May 2015. Published in: WWW 2015 Companion ISBN 9781450334730. doi:10.1145/2740908.2743052 (Submitted)

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Abstract

Social media are frequently rife with rumours, and the study of rumour conversational aspects can provide valuable knowledge about how rumours evolve over time and are discussed by others who support or deny them. In this work, we present a new annotation scheme for capturing rumour-bearing conversational threads, as well as the crowdsourcing methodology used to create high quality, human annotated datasets of rumourous conversations from social media. The rumour annotation scheme is validated through comparison between crowdsourced and reference annotations. We also found that only a third of the tweets in rumourous conversations contribute towards determining the veracity of rumours, which reinforces the need for developing methods to extract the relevant pieces of information automatically.

Item Type: Conference Item (Paper)
Subjects: H Social Sciences > HM Sociology
P Language and Literature > P Philology. Linguistics
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Social media, Rumor in mass media, Human computation
Journal or Publication Title: WWW 2015 Companion
Publisher: ACM
ISBN: 9781450334730
Official Date: 2015
Dates:
DateEvent
2015Published
May 2015Completion
DOI: 10.1145/2740908.2743052
Status: Peer Reviewed
Publication Status: Submitted
Access rights to Published version: Restricted or Subscription Access
Funder: Seventh Framework Programme (European Commission) (FP7)
Grant number: 611233 (FP7)
Conference Paper Type: Paper
Title of Event: World Wide Web Conference
Type of Event: Conference
Location of Event: Florence, Italy
Date(s) of Event: 18-22 May 2015
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