Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/250895 
Year of Publication: 
2022
Citation: 
[Journal:] Politics and Governance [ISSN:] 2183-2463 [Volume:] 10 [Issue:] 1 [Publisher:] Cogitatio [Place:] Lisbon [Year:] 2022 [Pages:] 121-132
Publisher: 
Cogitatio, Lisbon
Abstract: 
The EU is diagnosed with a participation deficit, rooted in a lack of public communication. While news media are the primary source of information about EU politics, social media have become an important channel for political information. Importantly, social media platforms offer unique opportunities for citizens to engage with information about the EU. Such engagement is under‐researched despite users' responses offering valuable information about the potential effects of EU news on public engagement. Therefore, we systematically analyze social media users' engagement with news about the EU. Drawing on the concepts of news values and shareworthiness, we investigate the proximity, conflictuality, negativity, and emotionality of EU news content posted on mainstream media Facebook accounts to explain the variation in reactions, shares, and number of comments. Using semi‐supervised machine learning, we analyze articles from the largest newspapers in Austria for the period 2015-2019, along with Facebook users' reactions to them. Results resonate only partly with prior literature, with negativity of EU news leading to more reactions and shares but fewer comments; emotionality, to fewer reactions and shares but more comments; and conflict mainly decreasing user engagement. Concerning proximity, a national angle leads to distinctly more engagement, whereas news about other EU member states and the EU as such do mostly not. Our study contributes to the discussion on how citizens engage with information on the EU and how to promote informed debate on social media through elites' communication.
Subjects: 
automated content analysis
computational methods
European Union
Facebook
news
social media
user engagement
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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