What is it about computational communication science? (Emese Domahidi & Mario Haim)

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.

In the second episode Emese Domahidi (Assistant Professor at TU Ilmenau) and Mario Haim (Assistant Professor at the U of Leipzig) discuss with Wouter van Atteveldt (Associate Professor at Vrije Universiteit Amsterdam) the role of communication science in the field. Main topics are the nature and role of data for the social sciences and challenges in collaborations with computer scientists. We touch on topics like open science, reproducibility and replicability for computational communication science and whether we need a cultural change to achieve these goals. Last but not least we talk about a new book Computational Analysis of Communication that Wouter co-edited with Damian Trilling and Carlos Arcila.

References

Lazer, D., Pentland, A. (Sandy), Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Life in the network: The coming age of computational social science. Science (New York, N.Y.), 323(5915), 721–723. https://doi.org/10.1126/science.1167742

Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). stm: An R Package for Structural Topic Models. Journal of Statistical Software, 91(2), 1–40. https://doi.org/10.18637/jss.v091.i02

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

What is it about Computational Communication Science?

As "big data" and "algorithms" affect our daily communication, lots of new research questions arise at the intersection between societies and technologies, asking for human wellbeing in times of permanent smartphone usage or the role of huge platforms for our news environment. The growing discipline of Computational Communication Science (CCS) takes on a combinatory perspective between social and computer science. In this podcast, Emese Domahidi (@MissEsi) and Mario Haim (@DrFollowMario) open this discussion for students and young scholars, one guest and one question at a time.

Credits

Artwork: Kristina Schneider (@kriesse)

Sounddesign: Nico van Capelle

Exciting background music in the beginning from musicfox.com

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!).

Let's dive into another CCS study, together with Jana Bernhard. This time, hosted by Mario Haim, Jana talks about her approach to topic modelling through algorithmic embeddings to analyze political communication in Austria from 2012 to 2021. Jana and Mario discuss the potential need for more sophisticated methods, they explain the approach Jana has taken, and they discuss whether it was actually worth it. Her study is currently in preparation for publication but interested listeners can get a sneak peak at https://de.slideshare.net/secret/DDmhlvuMusUFY6. Oh, and if you want to guest or host a future episode, please don't hesitate reaching out to us.

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 (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.

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!

Today's CCS study is about the application and particularly the development of dictionaries to apply to quantitative text analyses. Anke Stoll (together with Lena Wilms and Marc Ziegele in this publication from 2023) developed a dictionary to detect German incivility. She did so through a combination of manual and automated approaches, through classic word lists and word embeddings. Hosted by Emese Domahidi, Anke takes us through her approach, the challenges, and of course the potentials she sees with these kinds of techniques. The journal article was just published in Communication Methods and Measures. Oh, and if you want to guest or host a future episode, please don't hesitate reaching out to us.

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

Ethan Zuckerman, Associate Professor of Public Policy, Communication and Information at the U of Massachusetts Amherst, is our guest, and he is on a mission to fix platforms. Not because he thinks they are inherently bad, but because there are several things about platforms that research (not least CCS) tells us are flawed. Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich) talk with Ethan about why social media seems to be broken, what possible ways to fix it might be, how different regions of the world are approaching this challenge, and whether suing Facebook might make a difference.

P.S.: We now also have a website for our podcast --> https://aboutccs.net/

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/

Today, Emese Domahidi (Assistant Professor at TU Ilmenau) and Mario Haim (Assistant Professor at the U of Leipzig) discuss together with Valerie Hase (Research and Teaching Assistant at the U of Zurich) ways, approaches, guidelines, and routes to get started with computational communication science (CCS). We talk learning materials, compare intrinsic and extrinsic motivation, provide ideas and suggestions on where and how to find help and companions, and we tell our very own stories of how we got started with CCS.

Conferences, Divisions, & Working Groups

http://ic2s2.org/

- https://twitter.com/IC2S2

https://www.icahdq.org/group/compmethds

- https://twitter.com/ica_cm

- Slack channel via https://twitter.com/fe_loe/status/1395020548019720193

https://www.dgpuk.de/de/methoden-der-publizistik-und-kommunikationswissenschaft.html

- https://twitter.com/dgpuk_meth

https://www.cssmethods.uzh.ch/en.html

https://cssamsterdam.github.io/

https://tadapolisci.slack.com

Journals

https://computationalcommunication.org/ccr

https://www.tandfonline.com/toc/hcms20/current

References

van Atteveldt, W., Trilling, D., & Arcila Calderon, C. (2021). Computational analysis of communication. Wiley Blackwell. https://cssbook.net/

Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, tidy, transform, visualize, and model data. O'Reilly.

Summer Schools

https://github.com/chkla/css-schools

https://essexsummerschool.com/

https://sicss.io/

https://wiki.digitalmethods.net/Dmi/DmiAbout

Introductory Tutorials

https://www.tidytextmining.com/

https://tutorials.quanteda.io/

https://content-analysis-with-r.com/

https://bookdown.org/joone/ComputationalMethods/

https://tm4ss.github.io/docs/

https://www.mzes.uni-mannheim.de/socialsciencedatalab/article/advancing-text-mining/

https://bookdown.org/ndphillips/YaRrr/

https://r4ds.had.co.nz/

Credibility is a crucial concept in communication science and received severely increased attention, again, with CCS. That is, it serves everybody as a signpost to navigate the web whilst also being scrutinized by some via (AI-driven) signals that suggest trustworthiness. Cuihua (Cindy) Shen is Professor of Communication and Co-Director of the Computational Communication Research Lab at the Department of Communication at UC Davis. In this episode, she, Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich) talk about the concept of credibility and its particular role with mis- and disinformation. Of course, we also talk AI and what credibility is worth when a machines can generate whatever we've learnt to be trustworthy.

P.S.: We now also have a website for our podcast --> https://aboutccs.net/

P.P.S.: This is the last episode of this season. We're off to a (longer? ;-)) summer pause but look forward to being in touch soon!

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 (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.

Join us in the newest episode of #aBitOfCCS Podcast featuring Dr. Mónika Simon, a Postdoctoral researcher at the UvA, unraveling Narratives of (Dis)Trust in the digital realm.

In this episode, Dr. Simon discusses her research focused on tracing information flows in contemporary media, utilizing advanced computational methods and cross-platform analysis.

Explore her paper "Linked in the dark: A network approach to understanding information flows within the Dutch Telegramsphere" co-authored with K. Welbers, A. C. Kroon, and D. Trilling.

Access the paper at ⁠https://www.researchgate.net/publication/364452085_Linked_in_the_dark_A_network_approach_to_understanding_information_flows_within_the_Dutch_Telegramsphere

For further inquiries or information, you can reach Dr. Mónika Simon atm.simon@uva.nl. Tune in to this episode for a captivating exploration of the intricate world of information flows, providing valuable insights into the digital age and the dynamics of trust and distrust in media.

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.

Dive into the latest episode of #aBitOfCCS Podcast featuring Sean-Kelly Palicki, a PhD candidate at TU Munich, as he explores multilingual document sampling and the impact of keyword translation strategies on automated text analysis. In this engaging conversation with host Jana Bernhard, Sean discusses key findings from his study, "Selecting Relevant Documents for Multilingual Content Analysis," published in Computational Communication Research. Check out the full study at https://doi.org/10.5117/CCR2023.2.5.PALI. For further inquiries, reach out to Sean-Kelly at sean.palicki@tum.de. Don't miss this insightful episode on the nuances of document selection in computational communication research!

In this first episode of the second season, Fabienne Lind (@FabienneLind) discusses with Emese Domahidi (@MissEsi) and Mario Haim (@DrFollowMario) about the English centrism in academia and how this affects our CCS research. This particularly includes the method of content analysis where we use pre-trained models and/or build on training data that have been affected by a largely western and English-speaking perspective. And we discuss multi-lingual text analysis and the many advantages as well as challenges this approach offers.

In this first-ever episode, Emese Domahidi (Assistant Professor at TU Ilmenau) and Mario Haim (Assistant Professor at the U of Leipzig) discuss the relevance of a social-scientific perspective in the computer-scientifically driven field of artificial intelligence. We briefly dig into Kate Crawford's recent book (https://www.katecrawford.net/) as well as the European Union's "Guidelines for Trustworthy AI." And we compare rather distinct understandings of relevance when it comes to a computational perspective within communication science. All of this accumulates in the introduction of our new podcast in which we will tackle the urgent questions of CCS. 

References

Crawford, K. (2021). Atlas of AI. Yale University Press.

EU Ethics Guidelines for Trustworthy AI: https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

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

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. 

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.

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

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.

Step into the world of language model-based chatbots with our latest podcast episode! Join us for an in-depth exploration of the study titled "The Silence of the LLMs: Cross-Lingual Analysis of Political Bias and False Information Prevalence in ChatGPT, Google Bard, and Bing Chat." In this insightful episode, our host engages in a compelling interview with the researchers behind the study—Aleksandra Urman from the Department of Informatics at the University of Zurich (urman@ifi.uzh.ch) and Mykola Makhortykh from the Institute of Communication and Media Studies at the University of Bern (mykola.makhortykh@unibe.ch).

Discover key findings from their groundbreaking research, offering a cross-lingual analysis of political bias and false information prevalence in large language model-based chatbots. Uncover the implications of their work on the trustworthiness of AI-driven chat systems.

For further inquiries or to join the conversation, reach out to Aleksandra and Mykola via email. This episode provides a thought-provoking journey into the complexities of language models, political bias, and the prevalence of false information in the realm of contemporary chatbot technologies. Access the full study here: https://osf.io/q9v8f/download

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

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

Let's dive into a CCS study, together with Waqas Ejaz. In this episode, hosted by Valerie Hase, Waqas tells us about why and how he used topic modeling (LDA) for analyzing news coverage on climate change in a low-income country such as Pakistan. In that, and apart from data access, Waqas and Valerie discuss the sensitive decision of the appropriate number of topics in topic modeling. Ejaz, W., Ittefaq, M. and Jamil, S. (2023). Politics triumphs: A topic modeling approach for analyzing news media coverage of climate change in Pakistan. JCOM 22(01), A02. https://doi.org/10.22323/2.22010202 #aBitOfCCS offers brief heads-ups from the fascinating world of computational communication science. If you want to guest or host a future episode, please don't hesitate reaching out to us.

Tune in to #aBitOfCCS Podcast as we explore cross-cultural communication in a pandemic with Ofer Shinar, a research student and teaching assistant at Tel-Aviv University, currently at LMU Munich. Ofer shares insights from his study, "Semantic Network Analysis of Students' Confessions During a Global Pandemic: A Cross-National Study," delving into intercultural media usage and Semantic Network Analysis. Hosted by Jana Bernhard, this episode offers a brief yet insightful journey into the method of semantic network anlaysis. For further discussion or inquiries, connect with Ofer at ofershinar@mail.tau.ac.il. Find the study slides here (https://www.slideshare.net/ofershinar/semantic-network-analysis-of-student-confessions-during-a-global-pndemicpptx) for a deeper dive into this intriguing research!

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!

In today's episode, Frederic R. Hopp (⁠@Freddy_Hopp) discusses with Emese Domahidi (⁠@MissEsi⁠) and Mario Haim (⁠@DrFollowMario⁠) about morality. What's that, why does it affect our daily lifes and our social cohesion, what does it have to do with media content, and how can it be measured? CCS research offers a wide variety of tools to handle morality but also comes with quite a lot of challenges. Freddy takes us through them and discusses with us how research on morality is also affected by current societal developments.

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.

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.

Everyone is talking about Artificial Intelligence (AI), so we want to bring some differentiation into the bigger picture. For this, Jean Burgess, Distinguished Professor of Digital Media in and founding director of the Digital Media Research Centre (DMRC) at Queensland University of Technology, is our guest. She has been focusing on social implications of digital media technologies, platforms, and cultures, as well as new and innovative digital methods for studying them, for quite some time and has recently become Associate Director of the national Australian Research Council Centre of Excellence for Automated Decision-Making and Society (ADMS). From that, she's perfect to discuss with us--Emese Domahidi (Professor at TU Ilmenau) and Mario Haim (Professor at LMU Munich)--about what AI really is and where the hype is coming from, what role different disciplines play and where methods come into play.

P.S.: We now also have a website for our podcast --> https://aboutccs.net/

Links

https://www.admscentre.org.au/

https://research.qut.edu.au/dmrc/