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Chair for Data Science & Digitization

Chair for Data Science and Digitization

Prof. Dr. Nicolas Pröllochs

Licher Straße 74
D-35394 Gießen


Personal Website: https://www.nproellochs.com

News
BWL XI: Media Coverage of Our Research (FAZ, SZ, ZDF, Zeit, Stern, Handelsblatt, ...)
Our research on Russian propaganda on social media during the invasion of Ukraine has been covered in various media outlets.
BWL XI: Paper in EPJ Data Science
A new research paper studying the spread of Russian propaganda on social media during the 2022 invasion of Ukraine has been accepted for publication in EPJ Data Science (IF: 3.6).
BWL XI: Paper in European Journal of Information Systems
A new research paper has been accepted for publication in the European Journal of Information Systems (VHB: A). The paper employs neurophysiological measurements to study the role of affect when users assess online news as real or fake.
BWL XI: Two papers accepted at CSCW
Two new research papers have been accepted for publication in the Proceedings of the ACM on Human-Computer Interaction (CSCW).
BWL XI: Recent Media Coverage (The Atlantic, ARD, FAZ, ...)
Our paper in Nature Human Behaviour studying the effect of negativity on click rates has been featured in various media outlets.
BWL XI: Paper in Communications of the ACM
A new article has been accepted for publication in Communications of the ACM. The paper discusses threats emerging from alt-tech social media platforms.
BWL XI: Research Featured in the Financial Times
Our research studying Russian propaganda on social media during the 2022 invasion of Ukraine has been featured in the Financial Times.
BWL XI: Paper accepted at WWW
A new research paper has been accepted for publication at The Web Conference (WWW). The Web Conference is a flagship conference in data science with a very low acceptance rate (CORE Ranking A*).
BWL XI: Paper in Nature Human Behaviour
A new research paper examining the causal impact of negativity on news consumption has been accepted for publication in Nature Human Behaviour (IF: 24.25). The results of the study demonstrate a robust and causal negativity bias in news consumption from a massive dataset from the field.
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