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Social media network homophily among political candidates in multilevel settings

Bibliographic Data

ID12575554
AuthorsH Coffe (0000-0002-0020-0227, University of Bath, corresponding author), Iulia Cioroianu (0000-0002-5543-6819, University of Bath), Maxime Vandenberghe (0000-0001-8385-3014, Ghent University)
Year2022
Volume34
Issue3
Pages357-377
Publication date2022-08-29
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueRegional & Federal Studies (JOURNAL)
Journal identifiersISSN: 1359-7566 • E-ISSN: 1743-9434
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/13597566.2022.2107632
OpenAlexW4293452338
LanguageEN
Citations received1
References cited45

Our study investigates political candidates’ networks in the multilevel political setting of Belgium. Using Twitter data collected during the four months preceding the May 2019 regional, federal and European elections, we examine the extent to which network homophily – defined as the tendency to interact with similar others – occurs among political candidates along parliament, language and party lines. Relying on a unique dataset of 20 061 retweets between 935 candidates, we find that network interactions are most likely to occur among co-partisans and candidates speaking the same language. Candidates campaigning for the same parliament also tend to retweet among each other, although this tendency is not strong. Overall, the findings confirm the strong divide of Belgian politics along language lines and Belgium’s ‘partitocracy’ in which parties are the main actors in the representation and policy-making process

Homophily · Multilevel model · Parliament · Political communication · Political economy · Political science · Politics · Public relations · Representation (politics · Social media · Social network (sociolinguistics · Social science · Sociology · Computer Science · Electoral Systems and Political Participation · Law · Social Capital and Networks · Social Media and Politics

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Unique citing works1
Citations per year1
Citation span2025 - 2025 (1)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 1

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