Research Unit Computational Communication Science
Prof. Haim's research group examines computational communication science methods, focusing on algorithmic influences in journalism, media use, and political communication in digital democracies.
Research Interests:
Computational Methods • Political Communication • Meta Science
Information
Prof. Haim's research and teaching unit (RTU) develops and applies methods of Computational Communication Science (CCS) to study digital communication. Prof. Haim and his team focus on socially relevant research questions, such as the influence of algorithms in the areas of journalism, media usage, as well as political and interpersonal communication. Typical research questions address the role of intermediaries (e.g., messaging apps, search engines, social media) in evolving digital public spheres, algorithmic influences on individual media preferences and perceptions, or changing habits in news consumption, engagement, and individual opinion formation. The connection to current developments in computer science enables the RTU Haim to reflect on the potential and applicability of modern methods for media and communication research. Typical methodological approaches include the use of APIs, scraping, data donations, or tracking for data collection, as well as social network analysis, computational text and image analyses, agent-based modeling, large language models, as well as supervised and unsupervised machine learning. The team is also actively involved in the development and establishment of reliable standards and ethical norms for CCS and works, not least, at the intersection with Meta Science to promote transparency and replicability in research.
Research Projects
This project develops and tests protocols and digital tools to enhance replicability in Computational Communication Science. The goal is to support researchers early in the publication process to foster more transparent and robust research practices.
This project represents the second phase of the RepCCS project.
Further information on the project RepCCS.
Duration: 03/2025 - 02/2028
Leadership: Prof. Dr. Mario Haim, Dr. Johannes Breuer
Financial Support: Deutsche Forschungsgemeinschaft (DFG)
The project explores how data donation can be integrated into survey infrastructures for collecting digital trace data. It analyzes representation and measurement errors and develops strategies to mitigate biases caused by low participation rates.
Furhter information about the project. Integrating data donation in survey infrastructure.
Duration: 03/2024 - 02/2027
Leadership: Dr. Valerie Hase (LMU München), Prof. Dr. Florian Keusch (Universität Mannheim), Prof. Dr. Frauke Kreuter (LMU München), Prof. Dr. Mark Trappmann (Institut für Arbeitsmarkt- und Berufsforschung
Financial Support: Deutsche Forschungsgemeinschaft (DFG)
The HyCCS project makes data-driven methods in communication and social sciences accessible. With innovative teaching approaches, the tidycomm R package, and open-access materials, it supports students and educators in integrating computational methods into research and teaching.
Further information about the project Hybrid Teaching Computational Social Science.
Duration: 04/2024 - 04/2026
Leadership: Lara Kobilk
Financial Support: Stiftung Innovation in der Hochschullehre (SIH)
The interdisciplinary project KLIMA-MEMES investigates how humorous texts, images and videos shared online influence political decision-making in the context of public discourse on climate change.
More information about the project KLIMA-MEMES.
Duration: 04/2023-03/2026
Leadership: Prof. Dr. Mario Haim
Financial Support: Bayerisches Forschungsinstitut für digitale Transformation (bidt)
This project investigates factors affecting the replicability of Computational Communication Science, addressing challenges like data privacy and platform dependence. It contributes to promoting a transparent and replicable scientific culture.
This project represents the first phase of the RepCCS project.
Further information about the project RepCCS.
Duration: 03/2022 - 02/2025
Leadership: Prof. Mario Haim, Dr. Johannes Breuer
Financial Support: Deutsche Forschungsgemeinschaft (DFG)
This international and interdisciplinary project develops prototypes of responsible AI applications in collaboration with local news media and examines conditions that promote the responsible development and use of AI applications in local journalism.
More information about the project AI in local journalism.
Duration: 10/2022 - 12/2025
Leadership: Prof. Dr. Mario Haim
Financial Support: Volkswagen Stiftung (VW)
In the dynamic world of online media and algorithmically curated media environments, understanding the intricate online content consumption and user interaction is crucial. M3 offers a nuanced resource to social science in which it provides an up-to-date corpus of textual online media content as seen through regularly updated patterns of online media use.
Further information about the project Munich Media Monitoring
Duration: 2021 - open
Leadership: Prof. Dr. Mario Haim
Financial Support: LMU München
My PhD research investigates ideal point estimation of political parties and other political entities through computational methods using text data. This project compares a wide range of algorithms to assess their performance and efficiency in identifying political positions from textual sources. By systematically evaluating these algorithms, my research aims to uncover strengths and limitations, ultimately contributing to the improvement of computational tools in a range of social science disciplines like communication research, political science, or psychology.
Duration: 02/2022 - 02/2027
Leadership: Patrick Parschan
Financial Support: LMU München, Bayerisches Institut für digitale Transformation (bidt)
Philipp Knöpfle’s dissertation explores replicability in communication science. Using AI and statistical analyses, it identifies methodological barriers and develops a framework for more transparent and robust replication practices.
Duration: 03/2023 - open
Dissertation by Philipp Knöpfle
The project explores how data donation can be integrated into survey infrastructures for collecting digital trace data. It analyzes representation and measurement errors and develops strategies to mitigate biases caused by low participation rates.
Furhter information about the project Integrating data donation in survey infrastructure.
Duration: 03/2024 - 02/2027
Leadership: Dr. Valerie Hase (LMU München), Prof. Dr. Florian Keusch (Universität Mannheim), Prof. Dr. Frauke Kreuter (LMU München), Prof. Dr. Mark Trappmann (Institut für Arbeitsmarkt- und Berufsforschung
Financial Support: Deutsche Forschungsgemeinschaft (DFG)
Related websites, research, or programs
The Team
Prof. Dr. Mario Haim
Professor
Computational Communication Science • Political Communication • Computational Journalism
Dr. Valerie Hase
Academic Staff
Computational Social Science • Digital Journalism • Crisis and Conflict Communication
Philipp Knöpfle, M.Sc.
Academic Staff
Open Science • Meta Science • Computational Methods • AI & LLMs
Dr. Lara Kobilke, M.A.
Academic Staff
Everyday Political Conversations • Psychological Reactance • Political Participation • Social Media Challenges
Dr. Simon Lübke
Nadezhda Ozornina, M.A.
Academic Staff
Computational Communication Research • Multimodal Communication • Multilingual Text Analysis • Political Communication
Patrick Parschan (née
Schwabl), M.A.
Academic Staff
Natural language processing • Ideal point estimation • Algorithmic bias
Dr. Roxana Portugal
Dr. Johanna Schindler
Academic Staff
Group Phenomena • Digital Communication • Media Uses and Effects • Political Communication
Elisabeth Schmidbauer, M.A.
Academic Staff
Data Donation • Podcasts • political communication
Anna-Katharina Wurst, M.Sc.
Academic Staff
Political (online) communication • Computational methods • Research data management