What is it about computational communication science?

• Observing Opinions: What is Machine Learning?

  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.

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• #aBitOfCCS on Safeguarding Anti-Sexist Speech Online with Aditi Dutta hosted by Jana Bernhard-Harrer

  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/2508.11434v1Reach out to Aditi at [email protected] for more insights into her research.

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• Observing Opinions: What are Dictionaries?

  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.

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• Observing Opinions: What is Pre-Processing?

  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.

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• Observing Opinions: Thinking About Text Computationally

  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/

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