Using Convolutional Neural Networks to Classify Hate-Speech
Björn Gambäck, Utpal Kumar Sikdar · 2017
The paper introduces a deep learningbased Twitter hate-speech text classification system.The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech.Four Convolutional Neural Network models were trained on resp.character 4-grams, word vectors based on semantic information built using word2vec, randomly generated word vectors, and word vectors combined with character n-grams.The feature set was down-sized in the networks by maxpooling, and a softmax function used to classify tweets.Tested by 10-fold crossvalidation, the model based on word2vec embeddings performed best, with higher precision than recall, and a 78.3% F-score.