ORCID Profile
0000-0001-5263-4812
Current Organisations
Universität Zürich
,
Queensland University of Technology
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Publisher: Informa UK Limited
Date: 06-04-2021
Publisher: Figshare
Date: 2018
Publisher: Informa UK Limited
Date: 13-11-2018
Publisher: SAGE Publications
Date: 23-10-2018
Abstract: Incidental exposure to shared news on Facebook is a vital but understudied aspect of how citizens get involved with politics. This experiment investigates the influence of recommender characteristics (tie strength, political knowledge, political similarity) and different media sources (tabloids, legacy, and digital-born outlets) including multiple mediators (e.g., social pressure, outlet credibility) on incidental exposure to political news on Facebook. A 3 × 3 multi-stimulus, between-subject experiment with two additional quasi-factors and 135 different stimuli was conducted using a representative s le ( N = 507). Results showed that strong ties and recommenders with high knowledge increase news exposure, but the impact of knowledge is limited to recommenders with similar political opinions. Similar effects occur for different media types, which also have an independent impact on news exposure. Structural equation modeling reveals that media source effects are mediated through media perceptions, whereas recommender effects work via the desire for social monitoring and perceived issue importance.
Publisher: ifpuk - Institute for Media and Communication Studies at FU Berlin
Date: 2019
DOI: 10.17174/DCR.V6.6
Publisher: Figshare
Date: 2018
Publisher: Informa UK Limited
Date: 14-08-2020
Publisher: SAGE Publications
Date: 07-2021
DOI: 10.1177/20539517211033566
Abstract: Social bots – partially or fully automated accounts on social media platforms – have not only been widely discussed, but have also entered political, media and research agendas. However, bot detection is not an exact science. Quantitative estimates of bot prevalence vary considerably and comparative research is rare. We show that findings on the prevalence and activity of bots on Twitter depend strongly on the methods used to identify automated accounts. We search for bots in political discourses on Twitter, using three different bot detection methods: Botometer, Tweetbotornot and “heavy automation”. We drew a s le of 122,884 unique user Twitter accounts that had produced 263,821 tweets contributing to five political discourses in five Western democracies. While all three bot detection methods classified accounts as bots in all our cases, the comparison shows that the three approaches produce very different results. We discuss why neither manual validation nor triangulation resolves the basic problems, and conclude that social scientists studying the influence of social bots on (political) communication and discourse dynamics should be careful with easy-to-use methods, and consider interdisciplinary research.
Publisher: Informa UK Limited
Date: 02-10-2018
No related grants have been discovered for Tobias R. Keller.