Social media as author-audience games
Authors
Ribeiro, Andre F
Publication Date
2021-11Journal Title
Data Mining and Knowledge Discovery
ISSN
1384-5810
Publisher
Springer Science and Business Media LLC
Volume
35
Issue
6
Pages
2251-2281
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Ribeiro, A. F. (2021). Social media as author-audience games. Data Mining and Knowledge Discovery, 35 (6), 2251-2281. https://doi.org/10.1007/s10618-021-00783-3
Abstract
<jats:title>Abstract</jats:title><jats:p>We present an approach for the prediction of user authorship and feedback behavior with shared content. We consider that users use models of other users and their feedback to choose what to publish next. We look at the problem as a game between authors and audiences and relate it to current content-based user modeling solutions with no prior strategic models. As applications, we consider the large-scale authorship of Wikipedia pages, movies and food recipes. We demonstrate analytic properties, authorship and feedback prediction results, and an overall framework to study content authorship regularities in social media.</jats:p>
Keywords
Article, Content models, User models, Authorship, Game theory, Social media, Distance-metric learning, Text understanding
Identifiers
s10618-021-00783-3, 783
External DOI: https://doi.org/10.1007/s10618-021-00783-3
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330050
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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