Attention to news and its dissemination on Twitter: A survey |
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| Affiliation: | 1. DEIB, Politecnico di Milano, via Ponzio 34/5, 20133 Milano, Italy;2. IEIIT, Consiglio Nazionale delle Ricerche, via Ponzio 34/5, 20133 Milano, Italy;1. LIMED Laboratory, Computer Science Department, University of Bejaia, 06000 Bejaia, Algeria;2. Université de Lyon, CNRS, Université Lyon 1, LIRIS, UMR5205 F-69622, France;1. Department of Physics, University of Cambridge, Cambridge, UK;2. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK;3. Department of Electrical Engineering, University of Cambridge, Cambridge, UK;4. MRC Cancer Unit, University of Cambridge, Cambridge, UK;1. Department of Media Communication, Sungshin Women’s University, Bomun-ro, Sungbuk-gu, Seoul, South Korea;2. Department of Media and Communication, Dongguk University, Seoul, Republic of Korea;3. Department of Strategic, Legal and Management Communication, School of Communications, Howard University, 525 Bryant Street, NW, Washington, DC 20059, USA |
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| Abstract: | In recent years, news media have been hugely disrupted by news promotion, commentary and sharing in online, social media (e.g., Twitter, Facebook, and Reddit). This disruption has been the subject of a significant literature that has largely used AI techniques – machine learning, text analytics and network models – to both (i) understand the factors underlying audience attention and news dissemination on social media (e.g., effects of popularity, type of day) and (ii) provide new tools/guidelines for journalists to better disseminate their news via these social media. This paper provides an integrative review of the literature on the professional reporting of news on Twitter; focusing on how journalists and news outlets use Twitter as a platform to disseminate news, and on the factors that impact readers’ attention and engagement with that news on Twitter. Using the precise definition of a news-tweet (i.e., divided into user, content and context features), the survey structures the literature to reveal the main findings on features affecting audience attention to news and its dissemination on Twitter. From this analysis, it then considers the most effective guidelines for digital journalists to better disseminate news in the future. |
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| Keywords: | Computational journalism Digital journalism Social media News articles Twitter Journalism News Audience engagement Audience attention |
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