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Chair for Data Science & Digitization

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Chair for Data Science and Digitization

Prof. Dr. Nicolas Pröllochs

Licher Straße 62
D-35394 Gießen


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

News
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.
BWL XI: Paper in PNAS Nexus
A new research paper studying the antecedents of hate speech on social media has been accepted for publication in PNAS Nexus. Based on three large-scale datasets across three domains (politics, news media, and activism), the study demonstrates that moralized language in social media posts fosters the proliferation of hate speech.
Mehr ...
Featured Research: Moralized language predicts hate speech on social media

This study provides large-scale observational evidence that moralized language fosters the proliferation of hate speech on social media. Specifically, we analyzed three datasets from Twitter covering three domains (politics, news media, and activism) and found that the presence of moralized language in source posts was a robust and meaningful predictor of hate speech in the corresponding replies. These findings offer new insights into the mechanisms underlying the proliferation of hate speech on social media and may help to inform educational applications, counterspeech strategies, and automated methods for hate speech detection.

Featured Research: Negativity Drives Online News Consumption

Online media is important for society in informing and shaping opinions, hence raising the question of what drives online news consumption. Here, we analyze the causal effect of negative and emotional words on news consumption using a large online dataset of viral news stories. Specifically, we conducted our analyses using a series of randomized controlled trials (N = 22,743). Our dataset comprises ∼105,000 different variations of news stories from Upworthy.com that generated ∼5.7 million clicks across more than 370 million overall impressions. Although positive words were slightly more prevalent than negative words, we found that negative words in news headlines increased consumption rates (and positive words decreased consumption rates). For a headline of average length, each additional negative word increased the click-through rate by 2.3% Our results contribute to a better understanding of why users engage with online media.