What is it about computational communication science?

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.

Guest Natali Helberger, Distinguished Professor of Law & Digital Technology, discusses the challenges of regulating new technologies, including AI. They also explore upcoming EU regulations such as the Digital Services Act, Digital Markets Act, and AI Act. The podcast delves into the influence of AI and platforms on society's digital infrastructure, the regulation of large online platforms, and the importance of transparency. It also examines the categorization of AI systems into different risk levels and the potential impact of regulation on academic freedom.

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.

Measuring racism in news media using computational methods, including dictionary-based approaches. Discussion on the advantages of using dictionaries in computational communication science, and the challenges of identifying terms and validating data sources.

Anke Stoll developed a dictionary to detect German incivility using manual and automated approaches. The podcast discusses the challenges and potentials of using dictionaries in computational communication science, including the advantages of cost-effectiveness and scalability, difficulties in keyword collection, and the potential of incorporating machine learning. They also explore the evaluation of dictionaries, the importance of training models on relevant data, and the challenges of building dictionaries, recommending freely accessible dictionaries and coding schemes.

Frederic R. Hopp, Emese Domahidi, and Mario Haim discuss morality's impact on daily life and social cohesion. They explore how morality is tied to media content and the challenges of measuring it. The podcast also delves into the connection between morality and the brain, the role of neural representations, and bridging political divides. Lastly, it touches on media literacy, the spread of morally framed messages on social media, and the concept of moral reframing.

Jana Bernhard, a researcher studying algorithmic topic modeling for political communication analysis, discusses her approach to topic modeling using algorithmic embeddings to analyze political communication in Austria. She explains the need for more sophisticated methods, the challenges involved, and the importance of human validation. The podcast also emphasizes the need for simpler and more transparent techniques in topic modeling.

CCS researcher Waqas Ejaz discusses using topic modeling to analyze news coverage on climate change in Pakistan. They explore the challenges of data access and deciding the number of topics. The podcast also delves into the pros and cons of computational methods and manual sampling, compares global media coverage, and highlights the challenges of climate journalism in Pakistan.