MA Computational Communication Science

Michael Scharkow

JGU Mainz

04/2025

Description

Computational communication science focuses on analyzing communication processes and structures using computational methods such as automated content analysis or agent-based modeling.

In this seminar, we will examine key concepts, theoretical foundations, and empirical studies within this research field. We will also apply computational methods in practical exercises to better understand how they can be used to investigate media content and processes of media use.

Schedule

Sitzung Datum Thema
1 23.04.2025 Introduction
2 30.04.2025 CCS Paper Potpourri
3 07.05.2025 Ethical and legal perspectives
4 21.05.2025 Digital trace data
5 28.05.2025 Digital trace data
6 04.06.2025 Automatic content analysis
7 11.06.2025 Automatic content analysis
8 18.06.2025 Automatic content analysis
9 25.06.2025 Automatic content analysis
10 02.07.2025 Simulation and computational experiments
11 09.07.2025 Simulation and computational experiments
12 16.07.2025 Q & A

Session 1

Topics

  • Introduction, course structure, credits
  • What is Computational Communication Science?

Reading

Hilbert et al. (2019); Lazer et al. (2009)

Tasks for the next session

Select a study on Moodle and repare a poster (A1) for the study you selected.

Session 2

Topics

Poster Session: Interesting studies in computational communication science.

Session 3

Topics

  • Basic of research ethics
  • Application of ethical principles to computational research
  • Risks in computational research on, with and by big social media platforms

Reading

Salganik (2018, Ch. 6); Freelon (2018); 2025 Reddit/AI experiment

Tasks for the next session

Refresh your R skills.

Session 4

Topics

  • basics of digital trace data
  • analyzing digital download packages (DDP)

Reading

Ohme et al. (2023)

Tasks for the next session

  1. Complete the first coding assignments in R.
  2. Work through the second chapter on digital trace data in R.

Session 5

Topics

  • analyzing large-scale digital trace data
  • combining trace data with survey or content analysis data

Reading

Parry & Toth (2025);Clemm von Hohenberg et al. (2024)

Session 6

Topics

  • collecting data for content analysis
  • automatic content analysis basics

Reading

Van Atteveldt et al. (2022)

Tasks for the next session

  1. Complete the next coding assignments in R.

Session 7

Topics

  • automatic text analysis

Reading

Van Atteveldt et al. (2022)

Tasks for the next session

  1. Complete the next coding assignments in R.

Session 8

Topics

  • automatic image analysis

Reading

Van Atteveldt et al. (2022)

Tasks for the next session

  1. Complete the next coding assignments in R.

Session 9

Topics

  • simulation and generative agents

Reading

Van Atteveldt et al. (2022)

Tasks for the next session

  1. Complete the next coding assignments in R.

Session 10

Topics

  • simulation and generative agents
  • quarto publishing

Tasks for the next session

  1. Check example paper qmd file.

Session 11

Topics

  • quarto documents with branding
  • closeread extension

Tasks for the next session

  1. Bring all your term-paper questions.

References

Clemm von Hohenberg, B., Stier, S., Cardenal, A. S., Guess, A. M., Menchen-Trevino, E., & Wojcieszak, M. (2024). Analysis of Web Browsing Data: A Guide. Social Science Computer Review, 42(6), 1479–1504. https://doi.org/10.1177/08944393241227868
Freelon, D. (2018). Computational Research in the Post-API Age. Political Communication, 35(4), 665–668. https://doi.org/10.1080/10584609.2018.1477506
Hilbert, M., Barnett, G., Blumenstock, J., Contractor, N., Diesner, J., Frey, S., González-Bailón, S., Lamberson, P., Pan, J., Peng, T.-Q., et al. (2019). Computational communication science: A methodological catalyzer for a maturing discipline. International Journal of Communication, 13, 3912–3934.
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009). Computational social science. Science, 323(5915), 721–723.
Ohme, J., Araujo, T., Boeschoten, L., Freelon, D., Ram, N., Reeves, B. B., & Robinson, T. N. (2023). Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking. Communication Methods and Measures, 18(2), 124–141. https://doi.org/10.1080/19312458.2023.2181319
Parry, D., & Toth, R. (2025). Extracting Meaningful Measures of Smartphone Usage from Android Event Log Data: A Methodological Primer. Computational Communication Research, 7(1), 1. https://doi.org/10.5117/ccr2025.1.8.parr
Salganik, M. (2018). Bit by bit: Social research in the digital age. Princeton University Press.
Van Atteveldt, W., Trilling, D., & Calderón, C. A. (2022). Computational analysis of communication. Wiley Blackwell.