A Hybrid Deep BiLSTM-CNN for Hate Speech Detection in Multi-social media

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-05-06 DOI:10.1145/3657635

Ashwini Kumar, Santosh Kumar, Kalpdrum Passi, Aniket Mahanti

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引用次数: 0

Abstract

Nowadays, ways of communication among people have changed due to advancements in information technology and the rise of online multi-social media. Many people express their feelings, ideas, and emotions on social media sites such as Instagram, Twitter, Gab, Reddit, Facebook, YouTube, etc. However, people have misused social media to send hateful messages to specific individuals or groups to create chaos. For various Governance authorities, manually identifying hate speech on various social media platforms is a difficult task to avoid such chaos. In this study, a hybrid deep-learning model, where bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) are used to classify hate speech in textual data, has been proposed. This model incorporates a GLOVE-based word embedding approach, dropout, L2 regularization, and global max pooling to get impressive results. Further, the proposed BiLSTM-CNN model has been evaluated on various datasets to achieve state-of-the-art performance that is superior to the traditional and existing machine learning methods in terms of accuracy, precision, recall, and F1-score.

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用于检测多元社交媒体中仇恨言论的混合深度 BiLSTM-CNN

如今，由于信息技术的进步和在线多元社交媒体的兴起，人与人之间的交流方式发生了变化。许多人在 Instagram、Twitter、Gab、Reddit、Facebook、YouTube 等社交媒体网站上表达自己的情感、想法和情绪。然而，有人滥用社交媒体向特定个人或群体发送仇恨信息，制造混乱。对于各治理部门来说，要避免这种混乱局面，人工识别各种社交媒体平台上的仇恨言论是一项艰巨的任务。本研究提出了一种混合深度学习模型，利用双向长短期记忆（BiLSTM）和卷积神经网络（CNN）对文本数据中的仇恨言论进行分类。该模型采用了基于 GLOVE 的单词嵌入方法、剔除、L2 正则化和全局最大池化，取得了令人印象深刻的结果。此外，还在各种数据集上对所提出的 BiLSTM-CNN 模型进行了评估，结果表明该模型在准确率、精确度、召回率和 F1 分数方面都优于传统和现有的机器学习方法，达到了最先进的性能。

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来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.