Combining Textual Features for the Detection of Hateful and Offensive Language

• URL: http://arxiv.org/abs/2112.04803v1
• Date: Thu, 9 Dec 2021 09:50:20 GMT
• Title: Combining Textual Features for the Detection of Hateful and Offensive Language
• Authors: Sherzod Hakimov and Ralph Ewerth
• Abstract summary: 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.
• Score: 5.064332352040358
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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