Nr. 53 - Nicola Righetti, Petro Tolochko, Aytalina Kulichkina, Azade Kakavand, Daisuke Nakamura, Yuru Li, Paul Pressmann, Stephanie Geise, Annie Waldherr: A Longitudinal Approach to the Analysis of Social Media Engagement: The Case of Anger-Driven Climate-Skeptic Message Propagation During the 2021 German Elections

Social media have become an important space for political agenda formation and mobilization, with user engagement playing a key role in spreading messages. Accordingly, prior research has extensively examined social media users’ engagement and sharing behaviors. In this study, we examine the advantages of longitudinal modeling for analyzing social media engagement compared with cross-sectional approaches, focusing on the relationship between emotional reactions—particularly anger—and content propagation. Although cross-sectional approaches are commonly used to analyze social media data, engagement patterns are inherently temporal and therefore naturally call for a longitudinal, time-sensitive approach. We argue that longitudinal methods can be more effective than cross-sectional ones for analyzing time-evolving engagement dynamics. These methods indeed represent a middle ground between familiar cross-sectional approaches and sophisticated time-series techniques, avoiding some pitfalls of the former while relying on simpler assumptions than the latter. Longitudinal methods, in fact, reduce omitted variable bias, help account for time-varying factors—including algorithmic amplification and network propagation confounders—and accommodate irregular or sparse social media data. We empirically explore the differences between longitudinal and cross-sectional analysis by comparing estimates of the effect of anger on sharing using 1,137 environmentally themed Facebook posts by German political parties during the 2021 federal election, focusing on Alternative für Deutschland (AfD) and Die Grünen (The Greens), which represent opposite ends of the environmental policy spectrum. Cross-sectional estimates were derived using three sampling strategies: the last observation per post, the first post-election observation, and a randomly selected observation per post. Bayesian multilevel regression with a negative binomial specification was applied across both longitudinal and cross-sectional models. Our results indicate that longitudinal modeling yields more conservative and precise estimates, whereas cross-sectional methods tend to exaggerate effect sizes and interparty differences. All models suggest that anger is positively associated with sharing for AfD and negatively for Die Grünen, but longitudinal analysis provides greater inferential stability by controlling for time-invariant confounders and algorithmic amplification. Overall, the findings underscore the value of incorporating temporal dynamics into social media research, while also highlighting the challenges of applying longitudinal approaches to digital trace data, particularly with regard to data access.

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About the authors:

Nicola Righetti

Nicola Righetti is an Assistant Professor in the Department of Communication Sciences, Humanities, and International Studies at the University of Urbino Carlo Bo, Italy. His research focuses on advancing computational social science methods and data-driven inquiries into how digital media shape communication, public discourse, and socio-political behavior. He has contributed to several international research projects, including studies on polarization in social media communication (PolarVis) and the development of innovative AI methods to study online communication (vera.ai). He has also led projects to inform policy for institutions such as the Media Authority of North Rhine-Westphalia, focusing on social media communication during elections, as well as for the European Commission, where he is providing policy-relevant research supporting the implementation of the Digital Services Act (DSA) by very large online platforms. In 2024, he was a ZeMKI Visiting Research Fellow.

Petro Tolochko

Petro Tolochko is a postdoctoral researcher at the Department of Communication at the University of Vienna. His research interests include quantitative methods in the social sciences, social networks, quantitative text analysis, and automated language processing in the social sciences.

Aytalina Kulichkina

Aytalina Kulichkina is a postdoctoral researcher in the Department of Communication at the University of Vienna. Her research interests include political communication, social media, and computational methods in social science.

Azade Kakavand

Azade Kakavand is a postdoctoral scholar in Computational Methods at the Social Media Observatory of the Research Institute Social Cohesion and the Media Research Methods Lab at the Leibniz Institute for Media Research, at the Leibniz Institute for Media Research | Hans-Bredow-Institut (HBI).

Daisuke Nakamura

Daisuke Nakamura is a master’s student in Communication Science and a Research Assistant at the Computational Communication Science Lab, University of Vienna. His research interests include political social media influencers, polarization, political extremism, and computational methods in social science.

Yuru Li

Yuru Li is a PhD candidate at the Center for Media, Communication and Information (ZeMKI) at the University of Bremen. Her research focuses on computational approaches to political communication, visual communication, and social media study. She is currently affiliated with the Lab for Digital Communication and Information Diversity and has experience in applying computer vision and NLP in social media research.

Paul Pressmann

Paul Pressmann is a doctoral researcher at the Centre for Media, Communication and Information Research (ZeMKI) at the University of Bremen. His research focuses on climate change-related science communication, particularly at the intersection of computational communication science, digital transformations and public discourse. He develops and applies large-scale data and AI-based methods to study visual practices, polarization and the role of new communicators in online climate communication.

Stephanie Geise

Stephanie Geise is Professor of Communication and Media Sciences with a focus on Methodological Innovation at the Centre for Media, Communication and Information Research (ZeMKI) at the University of Bremen. Her research interests include political communication, visual communication, digital communication, methods of empirical communication research, in particular computer-based observation methods; process of media reception and media impact: political participation; political protest (protest movements).

Annie Waldherr

Annie Waldherr is Professor of Computational Communication Science at the University of Vienna. She studies the changing structures and dynamics in today’s digitized public spheres, combining computational and conventional empirical methods.