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In this episode, we’re joined by Dr. Johannes Gruber from Vrije Universiteit Amsterdam to unpack the world of language models. Johannes explains what language models really are and how they shape how we interact with information — from powering everyday chatbots like ChatGPT to supporting advanced research. We break down how these systems work behind the scenes, what they’re great at, and where we need to be cautious. Johannes also shares insights from his recent research on the feedback loops between language models, citizens’ beliefs, and democracy. It’s a closer look at why understanding both the potential and the limits of language models is so important for opinion research today.

Tune in to the #aBitOfCCS Podcast as we explore the computational workflow behind digitizing a historical society magazine. Christian Lendl joins us to discuss his paper Digitizing the Aristocratic Elite: Computational Challenges and Methods in Processing the Wiener Salonblatt (1870–1938). The episode highlights how AI-driven workflows can open new possibilities for digital humanities research. Reach out to Christian at [email protected]

In this episode, we’re joined by Prof. Eetu Mäkelä from the University of Helsinki to break down the world of word embeddings. Eetu explains what word embeddings are in simple terms, how they fit into the bigger picture of language models, and why they’re so powerful for exploring relationships in language — from the famous King–Queen example to applications in studying opinions. We look at how researchers can work with pre-trained embeddings or build their own, and how these tools open new ways to analyse language and meaning at scale. Eetu also shares where research on word embeddings is headed next and why they remain central to the evolving field of opinionated communication.

Tune in to the #aBitOfCCS Podcast as we dig into the growing tension between performance and sustainability in computational text analysis. Sean Palicki, a researcher at TUM, joins us to discuss his recent paper Don’t Look Up: Evaluating the Tradeoff between Performance and Sustainability of LLMs for Text Analysis. In this episode, we explore how large language models (LLMs) compare to lighter methods such as dictionaries and task-specific classifiers when applied to sentiment analysis, classification, and named entity recognition in political texts. We talk about the environmental costs of relying on large models, why bigger doesn’t always mean better for text analysis, and how introducing a CO₂-adjusted F1 score can help balance accuracy with sustainability. The conversation highlights a “right-fit” approach to model selection—choosing tools that are not only effective but also environmentally responsible. Reach out to Sean at [email protected] and find his website here: https://sean.web-of-us.com/ 

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.

Tune into the #aBitOfCCS Podcast as we explore how large language models classify online political speech about sexism. Aditi Dutta, a doctoral researcher at the University of Exeter, joins us to discuss her study on how automated moderation systems often misclassify anti-sexist speech as harmful—raising important questions about fairness, resistance, and digital democracy. CONTENT WARNING: This episode includes discussions and examples of sexist language online, which may be offensive or upsetting to some listeners. Read the paper here: https://arxiv.org/abs/2508.11434v1 Reach out to Aditi at [email protected] for more insights into her research.

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.

In this episode, we’re joined by Dr. Fabienne Lind from the University of Vienna, who sheds light on how computational methods transform the way we study opinionated communication. Fabienne shares her experience researching political emotions on social media in the CIDAPE project and explains what it really means to “code” when we’re working with text. We explore how computational tools help us find patterns and insights that traditional reading might miss — and why this matters for understanding public discourse today. From clear benefits to real challenges, Fabienne shows why thinking computationally is key for anyone studying text at scale. Further information about the CIDAPE Horizon Europe Project here: https://cidape.eu/

Tune in to the #aBitOfCCS Podcast as we explore how to measure something as abstract and slippery as uncertainty in political speech. Ella MacLaughlin, a PhD candidate at Utrecht University, joins us to discuss her ongoing research on how politicians in the US, UK, Germany, and the Netherlands express uncertainty in public communication. In this episode, we dive into the challenges of capturing uncertainty in political language, how it differs from other domains like biomedical science, and how we can build a dictionary for latent novel contracts.. Rather than focusing on results, we reflect on the conceptual and methodological puzzles that come with studying highly normative political language through computational tools. Reach out to Ella at [email protected] for more on her work, and check out the project website at radiunce.org! A brief note from Ella: At 17:52, I mistakenly attributed a paper to ‘Walters’ instead of the correct author ‘Walker’. Apologies for the error.

In this episode, we are joined by Prof. Helle Sjøvaag, journalism researcher and founding member of the OPINION Network. She shares how the network came to life and why it’s vital for studying how opinions form and spread online. We explore how digital spaces — from social media to news sites — shape what we think and how we express it. Helle unpacks the hidden influence of technology, power, and money on online discourse. Tune in to hear why building supportive networks is crucial in navigating these turbulent spaces — and how collective research can make a real difference. Further information here: https://www.opinion-network.eu/about

In this episode of #aBitOfCCS, Jana Bernhard-Harrer sits down with Denise Roth, a PhD student at the Strategic Communication Group at Wageningen University & Research. Denise’s research focuses on how science is communicated by political elites and the implications for the relationship between science and society. Her study, "In the Crossfire: Online Hostility Towards Public Figures Amid Politicized Science Communication" , investigates how large language models (LLMs) can be leveraged to annotate training data for a classifier capable of distinguishing hateful language from other types of online comments. Together, we explore the intersection of AI, social science, and combating online hostility in the context of politicized science communication. Don’t miss this thought-provoking discussion on the challenges and opportunities in understanding and addressing hostility towards public figures in today’s digital age. If you have any questions, you can connect with Denise here: Denise Roth - Wageningen University & Research

In the first episode, Wendo King'ang'i  and Christian Baden dive into the concept of opinionated communication — what it is, how it shows up in our daily lives, and why it matters. They also touch on the current state of research and what the future holds for this fascinating topic.   In this podcast we aim to introduce and discuss the OPINION COST Action CA21129. The network convenes early- and mid-career researchers from over 35 European countries, Israel, and the US, integrating cutting-edge expertise from different disciplines (notably, communication science, computational linguistics, IT) while networking the many, hitherto largely disconnected language communities of textual researchers. Further information here: https://www.opinion-network.eu/about

Join us on the #aBitOfCCS Podcast as we dive into political agenda-setting with Daniel Sandvej Eriksen, Post-Doc at the University of Aarhus, Department of Political Science. In this episode, we discuss Daniel’s latest research, "Initiate and Elevate! How Political Parties Can Set an Agenda." His study introduces The Issue Initiation Model, which explains how political parties proactively shape discussions . . Using computational methods, Daniel analyzes over 5.5 million tweets and 750,000 news articles from the UK and Denmark (2015-2022) to uncover how parties and MPs strategically set the agenda. Hosted by Jana Bernhard-Harrer, this episode explores the intersection of computational social science and political communication, revealing how digital traces help us understand political strategy. Preprint: https://osf.io/preprints/osf/yajsh_v1?view_only= 

In this special episode of #aBitOfCCS, Jana Bernhard-Harrer sits down with Marvin Stecker, a PhD student at the Computational Communication Science Lab at the University of Vienna. Marvin is part of the AUTHLIB project, which focuses on illiberal challenges to democracy. Unlike our usual focus on specific computational methods, this episode explores a study Marvin conducted with colleagues from OPTED: "Tools of the Trade – When Are Software Tools Mentioned in Computational Text Analysis Research?" The study examines how software tools are reported in computational communication science research and what this means for transparency and replicability. We discuss: • Key findings from a review of 406 journal articles published between 2016 and 2020. • Patterns of software tool reporting across disciplines and over time. • How factors like methodological validation and tool accessibility influence tool mentions. • Implications for transparency, replicability, and the future of computational text analysis in communication science. This episode provides valuable insights into the role of software tools in shaping computational research practices and offers a critical look at how we can improve transparency in the field. For more on Marvin’s work, connect with him at: Marvin Stecker - [email protected]