# A Dictionary-based Approach to Racism Detection in Dutch Social Media

arXiv: https://arxiv.org/abs/1608.08738 · HTML: https://mlsanity.com/paper/1608.08738/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2016.

## Built on by (most cited first, 15 of 15)

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- [Hate Speech Classification Using SVM and Naive BAYES](https://mlsanity.com/paper/2204.07057/) (2022)
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- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)
- [Don't Erase, Inform! Detecting and Contextualizing Harmful Language in Cultural Heritage Collections](https://mlsanity.com/paper/2505.24538/) (2025)
- [Hate Speech Detection and Classification in Amharic Text with Deep Learning](https://mlsanity.com/paper/2408.03849/) (2024)
- [Towards Fairness Assessment of Dutch Hate Speech Detection](https://mlsanity.com/paper/2506.12502/) (2025)
- [Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2506.23930/) (2025)
- [Explain the Flag: Contextualizing Hate Speech Beyond Censorship](https://mlsanity.com/paper/2604.14970/) (2026)

## Abstract (arXiv)

We present a dictionary-based approach to racism detection in Dutch social media comments, which were retrieved from two public Belgian social media sites likely to attract racist reactions. These comments were labeled as racist or non-racist by multiple annotators. For our approach, three discourse dictionaries were created: first, we created a dictionary by retrieving possibly racist and more neutral terms from the training data, and then augmenting these with more general words to remove some bias. A second dictionary was created through automatic expansion using a textttword2vec model trained on a large corpus of general Dutch text. Finally, a third dictionary was created by manually filtering out incorrect expansions. We trained multiple Support Vector Machines, using the distribution of words over the different categories in the dictionaries as features. The best-performing model used the manually cleaned dictionary and obtained an F-score of 0.46 for the racist class on a test set consisting of unseen Dutch comments, retrieved from the same sites used for the training set. The automated expansion of the dictionary only slightly boosted the model's performance, and this increase in performance was not statistically significant. The fact that the coverage of the expanded dictionaries did increase indicates that the words that were automatically added did occur in the corpus, but were not able to meaningfully impact performance. The dictionaries, code, and the procedure for requesting the corpus are available at: https://github.com/clips/hades