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arXiv · 2112.04803

Combining Textual Features for the Detection of Hateful and Offensive Language

Sherzod Hakimov·Ralph Ewerth
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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.

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Sherzod Hakimov, Ralph Ewerth. 2021-12-09. Combining Textual Features for the Detection of Hateful and Offensive Language. https://arxiv.org/abs/2112.04803

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