Modeling and Predicting News Consumption on Twitter
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|Title:||Modeling and Predicting News Consumption on Twitter||Authors:||Orellana-Rodriguez, Claudia; Keane, 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 consumption; Digital journalism; Social media; Audience engagement; News||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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