Combining Textual Features for the Detection of Hateful and Offensive Language
Abstract
The detection of offensive, hateful and profane language has become a critical challenge since many users in social networks are exposed to cyberbullying activities on a daily basis. In this paper, we present an analysis of combining different textual features for the detection of hateful or offensive posts on Twitter. We provide a detailed experimental evaluation to understand the impact of each building block in a neural network architecture. The proposed architecture is evaluated on the English Subtask 1A: Identifying Hate, offensive and profane content from the post datasets of HASOC-2021 dataset under the team name TIB-VA. We compared different variants of the contextual word embeddings combined with the character level embeddings and the encoding of collected hate terms.
Keywords
Cite
@article{arxiv.2112.04803,
title = {Combining Textual Features for the Detection of Hateful and Offensive Language},
author = {Sherzod Hakimov and Ralph Ewerth},
journal= {arXiv preprint arXiv:2112.04803},
year = {2021}
}
Comments
HASOC 2021, Forum for Information Retrieval Evaluation, 2021