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  • Publication
    Modeling and Predicting News Consumption on Twitter
    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.
  • Publication
    Spreading the News: How Can Journalists Gain More Engagement for their Tweets
    News media face many serious concerns as their distribution channels are gradually being taken over by third parties (e.g., people sharing news on Twitter and Facebook, and GoogleNews acting as a news aggregator). If traditional media is to survive at all, it needs to develop innovative strategies around these channels, to maximize audience engagement with the news they provide. In this paper, we focus on the issue of developing one such strategy for spreading news on Twitter. Using a corpus of 1M tweets from 200 journalist Twitter accounts and audience responses to these tweets, we develop predictive models to identify the features of both journalists and news tweets that impact audience attention. These analyses reveal that different combinations of features influence audience engagement differentially from one news category to the next (e.g., sport versus business). From these findings, we propose a set of guidelines for journalists, designed to maximize engagement with the news they tweet. Finally, we discuss how such analyses can inform innovative dissemination strategies in digital media.
      409Scopus© Citations 10