Department of Media and Communication
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Computational Communication Research

The Research and Teaching Unit (RTU) of Prof. Haim applies and advances computational methods for the study of digital communication in democracies. As such, Prof. Haim and his team focus on research questions of societal relevance where algorithmic influences might play a role, especially within but not limited to the fields of journalism, media use, and political or interpersonal communication. Typical research questions involve the role of intermediaries (e.g., messenger apps, search engines, social networking sites) within changing digital public spheres, algorithmic influences on individual media preferences and perceptions, or changing habits in news use, engagement, and opinion formation. By tying itself to recent developments in computer science, the RTU Haim thereby seeks to reflect on the potential and applicability of cutting-edge methods for media and communication studies. Typical methodological approaches include the use of APIs, scraping, data donations, or tracking for data collection vis-à-vis network analysis, computational text and image analyses, agent-based modeling, supervised or unsupervised machine learning. The team is also actively engaged in developing reliable standards and ethical norms for computational communication research.

Selected questions and publications of interest to us

How do people interact and collaborate via digital communication channels?

  • Haim, M., Breuer, J., & Stier, S. (2021). Do news actually “find me”? Using digital behavioral data to study the news-finds-me phenomenon. Social Media + Society, 7(3). https://dx.doi.org/10.1177/20563051211033820
  • Hase, V., Schäfer, M.S., Metag, J., Bischofberger, M., & Henry, L. (2022). Engaging the Public or Asking Your Friends? Analyzing Science-Related Crowdfunding Using Behavioral and Survey Data. Public Understanding of Science. https://doi.org/10.1177/09636625221113134

How do journalists and politicians communicate online?

  • Haim, M. (2022). The German data journalist in 2021. Journalism Practice, Advance Online Publication. https://dx.doi.org/10.1080/17512786.2022.2098523
  • Hase, V., Boczek, K., & Scharkow, M. (2022). Adapting to Affordances & Audiences? A Cross-Platform, Mixed-Methods Analysis of the Platformization of News. 72st Annual Conference of the International Communication Association (ICA). 26–30. May 2022, Paris.
  • Jungblut, M. & Haim, M. (2021). Visual gender stereotyping in campaign communication: Evidence on female and male candidate imagery in 28 countries. Communication Research, Advance Online Publication.https://dx.doi.org/10.1177/00936502211023333
  • Schindler, J. (2022). How Does the Internet Change Group Processes? Applying the Model of Collective Information Processing (MCIP) to Online Environments. In B. Krämer, & P. Müller (Hrsg.), Questions of Communicative Change and Continuity. In Memory of Wolfram Peiser (S. 96-117). Nomos. https://doi.org/10.5771/9783748928232

How do algorithms affect what people see online?

  • Haim, M., Scherr, S., & Arendt, F. (2021). How search engines may help reduce drug-related suicides. Drug and Alcohol Dependence, 226(108874). https://dx.doi.org/10.1016/j.drugalcdep.2021.108874
  • Schwabl, P., Haim, M., & Unkel, J. (2022). Searching for biased information? Informational strategies and algorithmic curation. Presented at the 72nd Annual Conference of the ICA, Paris, France.
  • Schwabl, P., Unkel, J., Haim, M. (im Druck). Vielfalt bei Google? Vielzahl, Ausgewogenheit und Verschiedenheit wahlbezogener Suchergebnisse. In C. Holtz-Bacha (Hrsg.), Die (Massen-)Medien im Wahlkampf: Die Bundestagswahl 2021. Wiesbaden: Springer Fachmedien.

How do digital platforms change opinion formation?

  • Arendt, F., Haim, M., & Beck, J. (2019). Fake News, Warnhinweise und perzipierter Wahrheitsgehalt: Zur unterschiedlichen Anfälligkeit für Falschmeldungen in Abhängigkeit von der politischen Orientierung. Publizistik, 64(2), 181-204. https://dx.doi.org/10.1007/s11616-019-00484-4
  • Haim, M., Kümpel, A. S., & Brosius, H.-B. (2018). Popularity cues in online media: A review of conceptualizations, operationalizations, and general effects. Studies in Communication and Media, 7(2), 186-207. https://dx.doi.org/10.5771/2192-4007-2018-2-58
  • Haim, M. & Maurus, K. (2021). Stereotypes and sexism? Effects of gender, topic, and user comments on journalists' credibility. Journalism, Advance Online Publication. https://dx.doi.org/10.1177/14648849211063994

How can methods from computer science be brought into social science?

  • Breuer, J., Kmetty, Z., Haim, M., & Stier, S. (2022). User-centric approaches for collecting Facebook data in the 'post-API age': Experiences from two studies and recommendations for future research. Information, Communication & Society, Advance Online Publication. https://dx.doi.org/10.1080/1369118X.2022.2097015
  • Hase, V., Mahl, D., & Schäfer, M. S. (2022). Der „Computational Turn“: ein „interdisziplinärer Turn“? Ein systematischer Überblick zur Nutzung der automatisierten Inhaltsanalyse in der Journalismusforschung. Medien & Kommunikationswissenschaft, 70(1-2): 60-78. https://doi.org/10.5771/1615-634X-2022-1-2-60
  • Haim, M. (2021). Gütekriterien und Handlungsempfehlungen für die Entwicklung von Forschungssoftware in der Kommunikations- und Medienwissenschaft. Medien & Kommunikationswissenschaft, 69(1), 65-79. https://dx.doi.org/10.5771/1615-634X-2021-1-65
  • Haim, M. (2020). Agent-based testing: An automated approach toward artificial reactions to human behavior. Journalism Studies, 21(7), 895-911. https://dx.doi.org/10.1080/1461670x.2019.1702892
  • Haim, M. & Nienierza, A. (2019). Computational observation: Challenges and opportunities of automated observation within algorithmically curated media environments using a browser plug-in. Computational Communication Research, 1(1), 79-102. https://dx.doi.org/10.5117/ccr2019.1.004.haim

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