What is it about computational communication science?

• #aBitOfCCS on Performance vs. Sustainability in Text Analysis with Sean Palicki hosted by Jana Bernhard-Harrer

  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/ 

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• 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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