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 \texttt{word2vec} 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

本文介绍了一种基于辞典的检测荷兰社交媒体评论的种族主义方法，针对两个公共比利时社交媒体网站的评论进行了标记，使用了三个话语辞典，并训练多个支持向量机，最优表现的模型使用手动清理的字典，在测试集上获得了0.46的F-得分，自动扩展的字典仅略微提高了模型性能，在语料库中增加的单词并未对性能产生任何明显影响。

基于词典的荷兰社交媒体种族主义检测方法