What is it about 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!

It is the season finale and Emese Domahidi (TU Ilmenau) and Mario Haim (LMU Munich) reflect on what it is about computational communication science. We start by briefly looking back at the previous twelve episodes to characterize the ongoing endeavors and challenges of CCS before spending the larger part of this episode on discussing CCS' coming of age. We use some sports metaphors to depict the establishment of collaborations, of professional norms and values, and of to-be-built research infrastructure. And we discuss whether CCS is just   tool or its own field, what a field actually is, and how this and we relate to the (post-?)discipline of communication science.

Ultimately, we peak at a special methods series that we plan for this podcast. And we very kindly ask you to tell us who you really are and where you are at. For this, we have prepared a very short questionnaire that we would love you to fill out (until October 2022):

>> https://www.soscisurvey.de/ccs-pod/

References

Fuchs, C., & Qiu, J.L. (2018). Ferments in the field: Introductory reflections on the past, present and future of communication studies. Journal of Communication, 68(2), 219-232. https://doi.org/10.1093/joc/jqy008

van Atteveldt, W., & Peng, T.-Q. (2018). When communication meets computation: Opportunities, challenges, and pitfalls in computational communication science. Communication Methods and Measures, 12(2–3), 81–92. https://doi.org/10.1080/19312458.2018.1458084

Waisbord, S. (2019). Communication: A post-discipline. Polity.

In this episode, Emese Domahidi (TU Ilmenau) and Mario Haim (LMU Munich) talk to David Lazer (Northeastern U in Boston, MA), distinguished professor of political science as well as computer science and one of the founding fathers of the broader field of computational social science. With a focus on mis- and disinformation, we learn from him why it is so difficult to measure human behavior both and why it has become both more challenging but also more adressable online. Of course, we touch ethical and legal questions in this regard as well as the global inequalities when it comes to research and data access. And we talk where computational social science is currently at and what should and will be the next big steps in this emerging field. Oh, and of course you will notice that, while we were already short on time, we recorded this episode shortly after rumors were confirmed that Elon Musk wanted to buy Twitter but before said individual pulled back out of the acquisition process. 

References

Lazer, D., Hargittai, E., Freelon, D., Gonzalez-Bailon, S., Munger, K., Ognyanova, K., & Radford, J. (2021). Meaningful measures of human society in the twenty-first century. Nature, 595(7866), 189–196. https://doi.org/10.1038/s41586-021-03660-7

Lazer, D. M. J., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., Freelon, D., Gonzalez-Bailon, S., King, G., Margetts, H., Nelson, A., Salganik, M. J., Strohmaier, M., Vespignani, A., & Wagner, C. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060–1062. https://doi.org/10.1126/science.aaz8170

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Alstyne, M. V. (2009). Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742

In this episode, Emese Domahidi (Assistant Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich) discuss with Jacob T. Fisher (Assistant Professor at the U of Illinois Urbana-Champaign) about the role of coding for communication scholars. Jacob just co-organized (along with Josephine Lukito, Frederic R. Hopp, and Felicia Loecherbach) the first ICA Hackathon and talks about his experience at the event in the podcast. From there, we tackle topics such as what programming and developing actually are and how to teach coding skills in a way that makes sense for the social sciences, what knowledge we need to be able to collaborate with computer scientists, whether we need computer scientists in the first place, and what programming language(s) communication scholars should learn. Additionally, we discuss how to use and sell this knowledge in business and how programming is a challenge at different career levels.

References

The ICA 2022 Pre-conference Hackathon: Opening Communication Science. https://www.hackingcommsci.org/

van Atteveldt, W., Trilling, D., & Arcila, C. (in press). Computational Analysis of Communication. Wiley Blackwell. Book homepage: https://cssbook.net/

The ICA's annual conference 2022 will start in a couple of days. In this episode, Emese Domahidi (TU Ilmenau) and Mario Haim (LMU Munich) discuss with Annie Waldherr (University of Vienna), current vice chair of the ICA's Computational Methods division, how to network in CCS. We touch upon the value of networking and how to network especially in the emerging field of CCS, given your specific career level. Of course, we also talk about the various receptions, the ICA dance, and other networking events at the conference. Finally, we talk about other opportunities to network, be it because one is unable to attend, be it at other conferences, or be it completely outside of conferences. 

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

Emese Domahidi (Assistant Professor at TU Ilmenau) and Mario Haim (Assistant Professor at the U of Leipzig) interview Till Keyling (former Senior Data Scientist at ProSiebenSat.1 and now Team Lead Software Engineering Data Science at PAYBACK) on how to become a data scientist. After learning what data science is, we look at what communication scientists can bring to the table, what university is capable of equipping us with, and what it is that potential employers look for in future data scientists. Also, do not miss out on Till talking us through an application process.

References

Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design patterns: Elements of reusable object-oriented software. Addison-Wesley.

Robinson, E., & Nolis, J. (2020). Build a career in data science. Manning.

Martin, R.C. (2008). Clean code: A handbook of agile software craftsmanship. Prentice Hall.

Martin, R.C. (2017). Clean architecture: A craftsman's guide to software structure and design. Prentice Hall.

Links and Podcasts

https://news.ycombinator.com/

https://towardsdatascience.com/

https://towardsdatascience.com/podcast/home