2021/02/08 by Chrysoula Themeli, Themeli, Chrysoula, George Giannakopoulos +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Internet Traffic Analysis and Secure E-voting
paper · pdf · doi:10.48550/arxiv.2102.04521
openalex publication_date 2021/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The pervasiveness of the Internet and social media have enabled the rapid and anonymous spread of Hate Speech content on microblogging platforms such as Twitter. Current EU and US legislation against hateful language, in conjunction with the large amount of data produced in these platforms has led to automatic tools being a necessary component of the Hate Speech detection task and pipeline. In this study, we examine the performance of several, diverse text representation techniques paired with multiple classification algorithms, on the automatic Hate Speech detection and abusive language discrimination task. We perform an experimental evaluation on binary and multiclass datasets, paired with significance testing. Our results show that simple hate-keyword frequency features (BoW) work best, followed by pre-trained word embeddings (GLoVe) as well as N-gram graphs (NGGs): a graph-based representation which proved to produce efficient, very low-dimensional but rich features for this task. A combination of these representations paired with Logistic Regression or 3-layer neural network classifiers achieved the best detection performance, in terms of micro and macro F-measure.