Modeling and Predicting News Consumption on Twitter

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Title: Modeling and Predicting News Consumption on Twitter
Authors: Orellana-Rodriguez, ClaudiaKeane, Mark T.
Permanent link: http://hdl.handle.net/10197/10878
Date: 11-Jul-2018
Online since: 2019-07-10T11:52:19Z
Abstract: Relatively little is known about the news consumption amongst social media users, despite the proliferation of news sharing, distribution platforms and news aggregators. In this paper, we present the Twitter News Model (TNM), a computational data-driven approach to elucidate the dynamics of news consumption on Twitter. We apply the TNM to a dataset of interactions between users and journalists/ newspapers to reveal what drives users’ consumption of news on Twitter, and predictively relate users’ news beliefs, motivations, and attitudes to their consumption of news.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Start page: 321
End page: 329
Copyright (published version): 2018 the Authors
Keywords: News consumptionDigital journalismSocial mediaAudience engagementNews
DOI: 10.1145/3209219.3209245
Other versions: http://www.um.org/umap2018/
Language: en
Status of Item: Peer reviewed
Is part of: UMAP '18 Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
Conference Details: UMAP 2018: User Modeling, Adaptation and Personalization, Nanyang Technological University, Singapore, July 8-11 2018
ISBN: 978-1-4503-5589-6
Appears in Collections:Insight Research Collection

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