What is it about computational communication science?

Fabienne Lind, Emese Domahidi, and Mario Haim discuss the English centrism in academia and how it affects computational communication science research. They explore challenges and propose solutions for global survey research, analyze text data in different languages, and discuss the impact of new data types like images and videos. They emphasize the importance of geographical diversity, interdisciplinary collaboration, and gaining new perspectives in computational communication science.

What is it about Computational Communication Science -- and about big societal problems? We -- Emese Domahidi (@⁠⁠MissEsi⁠⁠) and Mario Haim (@⁠⁠DrFollowMario⁠⁠) -- are back with season 2 and with two exciting changes: First, we do not address "big data" and "algorithms" up front anymore but discuss societal problem that have been addressed by computational communication sciene recently. For that, we talk to several awesome scholars from a broad variety of sub fields. Second, we start a sub series entitled #aBitOfCCS in which individual papers from CCS are discussed in great detail and directly with the authors. And the best thing is that (while we already have recorded some of these episodes) you can become an active part of it!

The podcast discusses the evolution and challenges of computational communication science, including career opportunities and the intersection with other disciplines. It explores collaborative research projects and efforts to establish infrastructure in the field. The concept of communication science as a post-discipline is explored, and plans for a special series and listener participation are discussed.

David Lazer, a leading scholar on misinformation and computational social science, discusses the challenges in measuring human behavior, the conflict between data ownership and accountability, the problem of misinformation on platforms, and the issues of consent and privacy in research on Twitter data.

Emese Domahidi, Mario Haim, and Jacob T. Fisher discuss the role of coding for communication scholars. Topics include teaching coding skills, collaborating with computer scientists, programming languages for communication scholars, and using coding knowledge in business. They also explore the challenges of programming at different career levels and the value of coding in various industries.

Annie Waldherr, current vice chair of the ICA's Computational Methods division, joins the hosts to discuss networking in computational communication science. They explore the importance of networking in CCS, strategies for building connections, and the value of in-person networking opportunities at conferences. The chapter also highlights the role of online platforms like social media in networking and the value of interdisciplinary collaboration.

Flipping things upside-down in this episode, Emese Domahidi (TU Ilmenau) and Mario Haim (LMU Munich) discuss with Claudia Wagner (RWTH Aachen and GESIS) about whether and how computer science really needs the social sciences. Claudia's background as a trained computer scientist as well as her current role as Professor of Applied Computational Social Sciences allowed us to really dive into opposing expectations, clichés, hurdles, and especially the benefits of interdisciplinary work at the intersection between the computer and the social sciences. We also discuss the concepts of algorithmically infused societies as well as "up-ductive" feedback loops, to ultimately discuss best practices for the perfect interdisciplinary collaboration that is computational social science.  Reference Wagner, C., Strohmaier, M., Olteanu, A., Kıcıman, E., Contractor, N., & Eliassi-Rad, T. (2021). Measuring algorithmically infused societies. Nature, 595, 197-204. https://doi.org/10.1038/s41586-021-03666-1

In this episode Emese Domahidi (Assistant Professor at TU Ilmenau) and Mario Haim (Assistant Professor at the U of Leipzig) discuss with Juhi Kulshrestha (Assistant Professor at U Konstanz) what makes algorithms online a research object. We touch on topics like filter bubbles and echo chambers, biases, how to investigate algorithms, the role of platforms and companies, data sources and possible effects of algorithmic curation. Last but not least, we discuss how far this field of resesarch has come by now and which future directions might be fruitful. References Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330–347. Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., & Karahalios, K. (2019). Search bias quantification: Investigating political bias in social media and web search. Information Retrieval Journal, 22(1), 188–227. Urman, A., Makhortykh, M., Ulloa, R., & Kulshrestha, J. (2021). Where the Earth is flat and 9/11 is an inside job: A comparative algorithm audit of conspiratorial information in web search results. arXiv preprint arXiv:2112.01278.

Together with Tetsuro Kobayashi (Associate Professor at City U of Hong Kong), Emese Domahidi (Assistant Professor at TU Ilmenau) and Mario Haim (Assistant Professor at the U of Leipzig) discuss the dilemma with social-media tech giants like Facebook or Tencent which undoubtedly have but are hesitant to share adequate data with independent research. We also discuss how varying types of data have changed with the rise of computational communication science. And we talk about possible ways to move forward in order to establish more independent data sources to conduct up-to-date social-scientific research with.  References Henrich, J. (2020). The WEIRDest people in the world: How the West became psychologically peculiar and particularly prosperous. Picador.

Together with Felicia Löcherbach (PhD candidate at VU Amsterdam), Emese Domahidi (Assistant Professor at TU Ilmenau) and Mario Haim (Assistant Professor at the U of Leipzig) discuss what research software is and why to code your own research software. Felicia gives unique insights into the topic using the example of a research software she developed from scratch. We also touch on topics like rewards and challenges, ethics, data security, systematic testing vs. quick and easy solutions and how to find support if you start your own research software project.   References Loecherbach, F., & Trilling, D. (2020). 3bij3 – Developing a framework for researching recommender systems and their effects. Computational Communication Research, 2(1), 53–79. https://doi.org/10.5117/CCR2020.1.003.LOEC https://github.com/FeLoe/3bij3