Laurens Müter


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Fine-Tuning Language Models on Dutch Protest Event Tweets
Meagan Loerakker | Laurens Müter | Marijn Schraagen
Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)

Being able to obtain timely information about an event, like a protest, becomes increasingly more relevant with the rise of affective polarisation and social unrest over the world. Nowadays, large-scale protests tend to be organised and broadcast through social media. Analysing social media platforms like X has proven to be an effective method to follow events during a protest. Thus, we trained several language models on Dutch tweets to analyse their ability to classify if a tweet expresses discontent, considering these tweets may contain practical information about a protest. Our results show that models pre-trained on Twitter data, including Bernice and TwHIN-BERT, outperform models that are not. Additionally, the results showed that Sentence Transformers is a promising model. The added value of oversampling is greater for models that were not trained on Twitter data. In line with previous work, pre-processing the data did not help a transformer language model to make better predictions.