What is it about computational communication science?

Explore the latest episode of #aBitOfCCS Podcast featuring Mar Castillo Campos, a research assistant at Loyola Andalucía University, as she delves into the use of computational methods, including GPT and CNNs, for automating media bias detection. In a conversation with host Jana Bernhard, Mar discusses the simplicity yet effectiveness of this method in uncovering biases by comparing media coverage from different sources on the same story.

Discover more in Mar's study titled "Natural Language Processing Methods Applied to the Study of Media Coverage" available at https://comunicacionymetodos.com/index.php/cym/article/view/171/123.

For additional information or inquiries, contact Mar at mcastillo@uloyola.es. Don't miss this episode for a concise exploration of how computational methods offer a unique perspective on media bias in the realm of communication research and journalism studies!

In this episode, we look at the question of how digital media affects the well-being of users - a question that researchers have been debating for a long time.

From a communication science perspective, there are many questions in this field of research and new approaches to solving them using computational methods. In this episode, we look in particular at the measurement of media use and the new opportunities presented by digital data and computational methods, as well as the associated challenges. Doug A. Parry (Senior Lecturer at Stellenbosch University) is one of the leading experts in this field and an expert in innovative data formats for measuring media use. He talks to Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich) about the topic.

Parry, D.A., Davidson, B.I., Sewall, C.J.R. et al. (2021). A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nature Human Behaviour, 5, 1535–1547. https://doi.org/10.1038/s41562-021-01117-5

Katya Ognyanova (Associate Professor at Rutgers U) is our guest and she is an expert on studying social networks. What's the societal problem with that, we hear you ask. Well, a lot of political knowledge and information and particularly mis- and disinformation spreading on the internet builds on social networking parameters such as strong and weak ties or partisanship among groups. Katya talks Emese (Professor at TU Ilmenau) and Mario (Professor at LMU Munich) through network essentials, the social aspects of (mis-)information, and the role of CCS in all of that.

In this episode we talk about platforms and their power. This includes the relevance of social media metrics to users, the gatekeeping function of platforms, and fragmentation trends. For these topics, our guest is the ideal expert to talk to: Subhayan Mukerjee (Assistant Professor at the National U of Singapore) is a computer scientist, mathematician and (computational) communication scholar. What's more, he also brings a global perspective on the use of news and the power of platforms, as Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich) talk with him about the needs for adequate methodology and, maybe even more importantly, for adequate theory.

Continuing with political language online, we seek to understand the relevance and divergence of news on the internet. Sounds trivial? Well, unfortunately, it isn't: What is "contemporary" news is decided upon by many rather than a few, it contains journalistically verified messages as well as mis- and disinformation and fake news. Jo(sephine) Lukito (Assistant Professor at the U of Texas at Austin’s School of Journalism and Media) guides us, Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich), through the exciting and "hybrid" online news environment as well as through her own research investigating particularly the malicious political language within online public spheres. Of course, CCS plays a large role in that too, as Jo is a strong advocate of computational methods and especially of multi-platform research.

It is not very hard to find dispute, also harsh dispute, online. A phenomenon also called digital contention, this raises several questions such as why are controversies more pronounced on the web? Have people turned into a rude mob in recent years or does the web help the quarrelsome to become more present? Also, what does this mean for our research, the theories and methods we apply? On that, Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich) talk with Christian Baden (Associate Professor at the Department of Communication and Journalism and the Smart Institute at the Hebrew U of Jerusalem) who is not only interested in the topic for his own research but who is also heading the oft-mentioned EU-funded OPINION network (https://www.opinion-network.eu/) that brings together scholars working to automatically detect and extract opinions from unstructued data.

Let's put on your legal suit and join Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich) welcoming Natali Helberger (Distinguished Professor of Law & Digital Technology, with a special focus on AI at the U of Amsterdam). We talk about the difficulties that come with regulating newly emerging technology. We also talk about all kinds of upcoming EU regulations (such as the Digital Services Act, DSA, the Digital Markets Act, DMA, and the AI Act) and the challenges of these, but also about the differences to other jurisdictional systems. Finally, we put this into perspective of CCS, talking about what will likely change in the new future for researchers (take-home message: a lot!).

In this episode, Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich) talk to Ágnes Emőke Horvát (Assistant Professor in Communication and Computer Science at Northwestern University where she leads the Lab on Innovation, Networks, and Knowledge, LINK) about what gender biases are, their origins and how prevalent these systematic misrepresantions are. Moving to Computational Communication Science, we then discuss how gender biases (and inequalities, more generally) affect our research, our data, tools, measures, and models. And we tackle the big question how potential routes forward could look like.

How to measure racism in news media is the main question in today's episode. Ahrabhi Kathirgamalingam looks into racist and discriminative language as well as dynamics of racism in some 30 years of German-speaking news media. As that's quite a lot of data, of course Ahrabhi also builds on CCS methods. Yet, in addition to the mere amount of data, coding racism also bears big questions of validity and ethics for coders and annotators -- an issue where CCS might also be able to help. In this episode hosted by Jana Bernhard, Ahrabhi talks us through dictionaries and the many options to construct and validate dictionaries in this area. Her research is part of her PhD project about which she is happily reachable via ahrabhi.kathirgamalingam@univie.ac.at. Also, some results were presented at the 2023 ICA in Toronto. Oh, and if you want to guest or host a future episode, please don't hesitate reaching out to us.

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. 

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.