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Chair for Data Science & Digitalization

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BWL XI: Paper in Nature Communications
A new article has been accepted for publication in Nature Communications (IF: 15.7). In this work, we perform a large-scale quasi-experimental study to analyze whether community fact-checks reduce the spread of misleading posts on the social media platform X (formerly Twitter).
BWL XI: Paper in EPJ Data Science
A new research paper studying political communication on TikTok has been accepted for publication in EPJ Data Science.
BWL XI: Two papers accepted at WWW
Two new research papers have been accepted for publication in the Proceedings of the ACM Web Conference (WWW '26). WWW is a premier publication outlet in data science with a low acceptance rate (CORE Ranking A*).
BWL XI: Paper in Nature's Scientific Reports
A new research paper has been accepted for publication in Nature's Scientific Reports. In this study, we empirically investigate the helpfulness of the context provided in community-created fact-checks on the social media platform X (formerly Twitter).
BWL XI: Study on Deepfakes at IC2S2
Our study "Characterizing Deepfakes on X" has been accepted for presentation at the International Conference on Computational Social Science (IC2S2 '25).
BWL XI: Media Coverage in TIME Magazine & The Atlantic
Our research on community-based fact-checking has been featured in TIME Magazine and The Atlantic.
BWL XI: Paper accepted in PNAS Nexus
A new research paper has been accepted for publication in PNAS Nexus. In our study, we estimate the link between online political advertising and election outcomes during the 2021 German federal election.
BWL XI: DFG Grant for Research on Community-Based Fact-Checking
The German Research Foundation (DFG) has awarded a new research grant to Prof. Dr. Nicolas Pröllochs. The funding will support our research on community-based fact-checking on social media.
BWL XI: Paper in Nature Reviews Psychology
A new article has been accepted for publication in Nature Reviews Psychology (IF: 16.8). Together with an interdisciplinary team of domain experts, we describe how natural language processing (NLP) can be used to analyse text data in behavioural science.
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Featured Research: Community-Based Fact-Checking Reduces the Spread of Misleading Posts on Social Media

Social media platforms increasingly rely on community-based fact-checking systems such as X’s Community Notes to combat misinformation at scale. In this study, we analyze more than 431 million reposts across 237,180 fact-checked cascades and provide large-scale causal evidence that community notes reduce the subsequent spread of misleading posts by 61.2% on average. We further find that community notes increase the likelihood that users delete misleading posts by 94.3%. However, notes often appear too late to prevent the early, most viral stage of diffusion, limiting their overall system-wide impact. Our findings highlight both the promise and current limitations of community-based fact-checking systems in reducing misinformation on social media.

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.