Topic Words‐Based Multilingual Hateful Linguistic Resources Construction for Developing Multilingual Hateful Content Detection Model Using Deep Learning Technique

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Abstract
Nowadays, social media platforms provide space that allows communication and sharing of various resources using a variety of natural languages in different cultural and multilingual aspects. Although this interconnectedness offers numerous benefits, it also exposes users to the risk of encountering offensive (OFFN) and harmful content, including hateful speech. In order to create a model for detecting hateful content in resource‐rich languages, lexicons, word embedding, topic modeling, and transformer language models were applied. Low‐resource languages, including Ethiopian languages, suffering in lack of such linguistic resources. Multilingual hateful content detection brings complex challenges due to cultural and linguistic varieties. The paper proposes a multilingual hateful content identification model using a transformer language model and hybrid lexicon techniques to enhance hateful content recognition in low‐resource Ethiopian languages. First, hateful content disseminated on Facebook in Ethiopian‐languages target was identified as (insult, identity hate, antagonistic, and threat) using topic modeling techniques. Then, we compiled different hateful terms from sources such as guidelines and proclamations related to the Ethiopian context. We created Ethiopian context‐based transformer language models. We utilized topic words‐based datasets to construct pretrained transformer language models and multilingual lexicons of major Ethiopian languages. Finally, their performance was compared by integrating them into deep learning‐based low‐resource Ethiopian languages’ hateful content detection framework. Among applied deep learning algorithms with Ethiopian language linguistic resources, word2vec‐based multilingual lexicons with convolutional neural network (CNN) outperform than others. The result indicated that constructing topic words based multilingual word2vec lexicons outperformed than transformers language model based on topics modeling for low‐resource Ethiopian languages, effectively produce the promising hate speech (HATE) detection approach of low‐resource Ethiopian languages.

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