Profiling Hate Speech Spreaders on Twitter: SVM vs. Bi-LSTM

AuthorVogel, Inna; Meghana, Meghana
TypeConference Paper
AbstractHate speech is a crime that has been growing in recent years, especially in online communication. It can harm the individual or a group of people by targeting their conscious or unconscious intrinsic characteristics. Additionally, the psychological burden of manual moderation has necessitated the need for automated hate speech detection methods. In this notebook, we describe our profiling system to the PAN at CLEF 2021 lab ""Profiling Hate Speech Spreaders on Twitter"". The aim of the task is to determine whether it is possible to identify hate speech spreaders on Twitter automatically. Our final submitted system uses character 𝑛-grams as features in combination with an SVM and achieves an overall average accuracy of 69.5% for the English and Spanish datasets. Additionally, we experimented with a Bi-LSTM model and trained it with Sentence-BERT, achieving slightly worse performance results. The experiments show that it is difficult to detect solidly hate speech spreaders on Twitter as hate speech is not only the use of profanity.
ConferenceConference and Labs of the Evaluation Forum (CLEF) 2021