What is it about computational communication science?

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.

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.

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.

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.

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!

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/

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/

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

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.

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.

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 this episode, we’re joined by Prof. Damian Trilling from Vrije Universiteit Amsterdam, who opens the door to the world of machine learning for opinion research. Damian explains how citizens consume and share news today — and how machine learning helps us make sense of these patterns at scale. We unpack the difference between supervised and unsupervised machine learning and explore how blending both can strengthen research projects. Damian also shares why these methods hold so much promise for the future of studying opinionated communication and news use in the digital age.

In this episode, we’re joined by Dr. Valerie Hasse from LMU Munich to demystify one of the most widely used tools in computational text analysis: the dictionary. Valerie explains how computational dictionaries relate (or don’t!) to the everyday dictionaries we know, and breaks down how they actually work behind the scenes. We explore what dictionaries are good for, when to build your own versus using ready-made ones, and where they shine — especially for studying opinions, emotions, and media narratives. Valerie also opens up about the real challenges that come with using dictionaries, from biases to technical hurdles, and whether they still matter in the age of large language models. She gives clear answers and practical insights into a tool that helps researchers decode massive amounts of text.

In this episode, Prof. Jamal Abdul Nasir from the University of Galway reveals why pre-processing is the backbone of all text analysis. He breaks down key steps like defining documents, tokenization, removing stop words, unification, and stemming vs. lemmatization. Jamal also explains unigrams vs. bigrams and how modern NLP techniques like byte-pair encoding are changing the game. Plus, he shares practical tips for making your pre-processing transparent and reproducible, helping your research stand strong and scale up.