Department for Computational Social Science

Head of Department: Prof. Dr. Raphael Heiberger (SOWI VII)

CSS (computational social science) is located at the interface of social science and computer science. Social phenomena are therefore the main focus of our research. They are, however, analyzed using "new" data types.
In particular, this includes process-generated data, such as minutes of plenary proceedings, scientific texts and collaborations, or messages on social media channels (such as tweets). Methodologically, CSS combines inferential statistics with iterative calculation rules (e.g. algorithms), or Bayesian probability classifications. These can then be used to identify topics within large quantities of texts, to predict economic growth, or to investigate the coevolution of social relationships and attributes.
In addition to data issues and the intertwined development of innovative methods, our group also focuses on their "social science fit". This means, we are commited to ensuring the validity and reliability of data and also want to ensure the theoretical applicability of the methods.

Latest publications

  1. Unger, S., Erhard, L., Wieczorek, O., Koß, C., Riebling, J., & Heiberger, R. H. (2022). Benefits and detriments of interdisciplinarity on early career scientists’ performance. An author-level approach for U.S. physicists and psychologists. PLOS ONE, 17(6), 1–20. https://doi.org/10.1371/journal.pone.0269991
  2. Heiberger, R. H. (2022). Applying Machine Learning in Sociology: How to Predict Gender and Reveal Research Preferences. KZfSS Kölner Zeitschrift Für Soziologie Und Sozialpsychologie. https://doi.org/10.1007/s11577-022-00839-2
  3. Windzio, M., & Heiberger, R. H. (2022). The Social Ecology of Intergenerational Closure in School Class Networks. Socio-spatial Conditions of Parents’ Norm Generation and Their Effects on Students’ Interpersonal Conflicts. Social Networks. https://doi.org/10.1016/j.socnet.2021.12.009
  4. Sicakkan, H. G., & Heiberger, R. H. (2022). Between Europeanism and Nativism: Exploring a Cleavage Model of European Public Sphere in Social Media. Javnost - The Public, 0(0), 1–19. https://doi.org/10.1080/13183222.2022.2067724
  5. Windzio, M., & Heiberger, R. (2022). Talking About Education: How Topics Vary Between International Organizations. In K. Martens & M. Windzio (Eds.), Global Pathways to Education : Cultural Spheres, Networks, and International Organizations (pp. 239--266). Springer International Publishing. https://doi.org/10.1007/978-3-030-78885-8_9
  6. Erhard, L., Heiberger, R. H., & Windzio, M. (2021). Diverse Effects of Mass Media on Concerns about Immigration: New Evidence from Germany, 20012016. European Sociological Review, jcab063. https://doi.org/10.1093/esr/jcab063
  7. Erhard, L., Heiberger, R. H., & Windzio, M. (2021). Diverse Effects of Mass Media on Concerns about Immigration: New Evidence from Germany, 2001–2016. European Sociological Review. https://doi.org/10.1093/esr/jcab063
  8. Kaffai, M., & Heiberger, R. H. (2021). Modeling Non-Pharmaceutical Interventions in the COVID-19 Pandemic with Survey-Based Simulations. PLOS ONE, 16(10), e0259108. https://doi.org/10.1371/journal.pone.0259108
  9. Wieczorek, O., Unger, S., Riebling, J., Erhard, L., Koß, C., & Heiberger, R. (2021). Mapping the field of psychology: Trends in research topics 1995–2015. Scientometrics. https://doi.org/10.1007/s11192-021-04069-9
  10. Heiberger, R., Majó-Vázquez, S., Castro Herrero, L., Nielsen, R. K., & Esser, F. (2021). Do Not Blame the Media! The Role of Politicians and Parties in Fragmenting Online Political Debate. The International Journal of Press/Politics, 19401612211015120. https://doi.org/10.1177/19401612211015122
  11. Heiberger, R. H., & Muñoz-Najar Galvez, S. (2021). Text Mining and Topic Modelling. In Handbook of Computational Social Science. Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9781003025245-24/text-mining-topic-modeling-raphael-heiberger-sebastian-munoz-najar-galvez?context=ubx&refId=fb133dd5-e885-4910-b6e6-0248511435d6
  12. Heiberger, R. H., Munoz-Najar Galvez, S., & McFarland, D. A. (2021). Facets of Specialization and Its Relation to Career Success: An Analysis of U.S. Sociology, 1980 to 2015. American Sociological Review, 86(5), Article 5. https://doi.org/10.1177/00031224211056267
  13. Camarasa, C., Heiberger, R., Hennes, L., Jakob, M., Ostermeyer, Y., & Rosado, L. (2020). Key Decision-Makers and Persuaders in the Selection of Energy-Efficient Technologies in EU Residential Buildings. Buildings, 10(4), 70. https://doi.org/10.3390/buildings10040070
  14. Breure, A. S. H., & Heiberger, R. H. (2019). Reconstructing science networks from the past. Journal of Historical Network Research, 3(1), 92--117. https://doi.org/10.25517/jhnr.v3i1.52
  15. Munoz-Najar Galvez, S., Heiberger, R., & McFarland, D. (2019). Paradigm Wars Revisited: A Cartography of Graduate Research in the Field of Education (1980–2010). American Educational Research Journal, 57(2), 612--652. https://doi.org/10.3102/0002831219860511
  16. Kibanov, M., Heiberger, R. H., Rödder, S., Atzmueller, M., & Stumme, G. (2019). Social studies of scholarly life with sensor-based ethnographic observations. Scientometrics, 119(3), 1387--1428. https://doi.org/10.1007/s11192-019-03097-w
  17. Heiberger, R. H., & Schmitz, A. (2019). Zur globalen Einbettung nationaler Schuldennetzwerke. In J. Fuhse & K. Krenn (Eds.), Netzwerke in gesellschaftlichen Feldern (pp. 249--274). Springer Fachmedien. https://doi.org/10.1007/978-3-658-22215-4_10
  18. Heiberger, R. H. (2018). Predicting economic growth with stock networks. Physica A: Statistical Mechanics and Its Applications, 489, 102--111. https://doi.org/10.1016/j.physa.2017.07.022
  19. Heiberger, R. H., & Koss, C. (2018). Computerlinguistische Textanalyse und Debatten im Parlament. In J. Brichzin, D. Krichewsky, L. Ringel, & J. Schank (Eds.), Soziologie der Parlamente: Neue Wege der politischen Institutionenforschung (pp. 391--418). Springer Fachmedien. https://doi.org/10.1007/978-3-658-19945-6_15
  20. Heiberger, R. H., Schwarzer, B., & Riebling, J. R. (2017). Eine Frage des Marktes? Regionale Unterschiede von Heimentgelten stationärer Pflegeeinrichtungen. Berliner Journal für Soziologie, 27(2), 209--241. https://doi.org/10.1007/s11609-017-0341-7
  21. Heiberger, R. H., & Wieczorek, O. J. (2016). Choosing Collaboration Partners. How Scientific Success in Physics Depends on Network Positions. ArXiv:1608.03251 Physics. http://arxiv.org/abs/1608.03251
  22. Heiberger, R. H., & Riebling, J. R. (2016). Installing computational social science: Facing the challenges of new information and communication technologies in social science. Methodological Innovations, 9, 1--11. https://doi.org/10.1177/2059799115622763
  23. Heiberger, R. H. (2015). Die Bedeutung institutioneller Regeln für das Handeln an Börsen. Eine soziologische Perspektive auf Aktienrückkäufe in Deutschland. Berliner Journal für Soziologie, 25(3), 303--331. https://doi.org/10.1007/s11609-015-0286-7
  24. Schmitz, A., Heiberger, R. H., & Blasius, J. (2015). Das globale Feld der Macht als „Tertium Comparationis“. Österreichische Zeitschrift für Soziologie, 40(3), 247--263. https://doi.org/10.1007/s11614-015-0171-9
  25. Heiberger, R. H. (2015). Shifts in Collective Attention and Stock Networks. In M. T. Thai, N. P. Nguyen, & H. Shen (Eds.), Computational Social Networks (Vol. 9197, pp. 296--306). Springer International Publishing. https://doi.org/10.1007/978-3-319-21786-4_26
  26. Heiberger, R. H. (2015). Collective Attention and Stock Prices: Evidence from Google Trends Data on Standard and Poor’s 100. PLoS ONE, 10(8), e0135311. https://doi.org/10.1371/journal.pone.0135311
  27. Heiberger, R. H. (2015). Die soziale Konstruktion von Preisen. Beeinflussung von Kultur, Netzwerken und institutionellen Regeln von Aktienkursen. Springer VS.
  28. Heiberger, R. H., & Riebling, J. R. (2015). U.S. and Whom? Structures and Communities of International Economic Research. Journal of Social Structure, 16(9), Article 9.
  29. Heiberger, R. H. (2014). Stock network stability in times of crisis. Physica A: Statistical Mechanics and Its Applications, 393, 376--381. https://doi.org/10.1016/j.physa.2013.08.053
  30. Heinze, T., Heidler, R., Heiberger, R. H., & Riebling, J. (2013). New patterns of scientific growth: How research expanded after the invention of scanning tunneling microscopy and the discovery of Buckminsterfullerenes. Journal of the American Society for Information Science and Technology, 64(4), 829--843. https://doi.org/10.1002/asi.22760
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