UPF Talk 02/2025: Studies, Methods & Outlook

Max Pellert (https://mpellert.at)

https://mpellert.at/upf_talk_02_25/upf_talk_02_25.pdf

https://mpellert.at

Currently: Group Leader at the Barcelona Supercomputing Center in the Department for Computational Social Science and Humanities

Before: Professor for Social and Behavioural Data Science (interim, W2) at the University of Konstanz

https://mpellert.at

Assistant Professor (Business School of the University of Mannheim)

I worked in industry at SONY Computer Science Laboratories in Rome, Italy

PhD from the Complexity Science Hub Vienna and the Medical University of Vienna in Computational Social Science

Studies in Psychology and History and Philosophy of Science

Msc in Cognitive Science and Bsc in Economics (both University of Vienna)

Basics: Extracting Signals from Text

One example: Linguistic Inquiry and Word Count, LIWC (pronounced “Luke”)

Simple word matching method

Generated and validated by psychologists (Pennebaker et al., 2001-today)

Examples of LIWC classes:
Positive Affect, Negative Affect
Anxiety, Sadness, Anger
Social processes

Basics: Extracting Signals from Text

More advanced examples using deep learning
Classifiers based on transformer architectures (RoBERTa)
Large general purpose language models adapted to the task of emotion classification

https://huggingface.co/maxpe

https://huggingface.co/oliverguhr/german-sentiment-bert

And many many more…

Sentiment Analysis

Has gotten a somewhat bad name: “Why don’t we run something on the text?”

Often conceptually flawed + noisy data + inadequate annotation schemes to create many different tools

Results can be cherry-picked by optimizing on the tool

But, we argue, used right it can be a valuable research instrument

Sentiment Analysis Evidence

Individual text level (for example a single tweet): Not reliable, sarcasm, irony, performative nature of social media: we need a substantial number of texts to get through the noise (especially with dictionary methods, also base rates are low)

Individual person level: Associations sometimes higher (for example for depression: Eichstaedt et al., 2018) and sometimes lower (PANAS scale: Beasley & Mason, 2015) with (rather) stable personality traits

Group level (geographical): Debated, for example Twitter heart disease study (Eichstaedt et al., 2015), methods have to be validated and checked for robustness (Jaidka et al., 2020)

Our contribution: Macroscopically validating if we are able to capture momentary feeling of a population on a daily level

World Happiness Report