Amharic Hate Speech Detection Techniques: Systematic Review

Amharic Hate Speech Detection Techniques: Systematic Review | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 September 2025 V1 Latest version Share on Amharic Hate Speech Detection Techniques: Systematic Review Authors : Abinet Bizuayehu Desta 0009-0001-3350-7014 [email protected] and Moges Alebachew Mekonen Authors Info & Affiliations https://doi.org/10.22541/au.175674794.49041244/v1 142 views 67 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The growth of hate speech on online platforms has highlighted the urgent need for effective detection mechanisms, especially for under-resourced languages like Amharic. This Systematic Literature Review (SLR) examines state-of-the-art techniques used for Amharic hate speech detection, spanning traditional machine learning, deep learning, and hybrid approaches. Insights from ten studies reveal diverse methodologies, including recurrent neural networks (e.g., LSTM, BiLSTM) and multimodal frameworks integrating text and audio features. Despite promising results, challenges persist, such as limited dataset diversity, underexplored feature engineering, and the scalability of advanced models. Evaluation metrics like accuracy and F1-scores vary significantly across studies, with the highest reported accuracy reaching 97.9%. The review underscores the need for larger, more diverse datasets, consistent evaluation frameworks, and deeper error analyses. By identifying research gaps and opportunities, this study aims to guide future advancements in Amharic hate speech detection, ensuring more robust and inclusive solutions for combating online hate speech. Supplementary Material File (abinet slr final.docx) Download 268.25 KB Information & Authors Information Version history V1 Version 1 01 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords amharic hate speech detection deep learning machine learning text classification Authors Affiliations Abinet Bizuayehu Desta 0009-0001-3350-7014 [email protected] Debark University View all articles by this author Moges Alebachew Mekonen Woldia University View all articles by this author Metrics & Citations Metrics Article Usage 142 views 67 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Abinet Bizuayehu Desta, Moges Alebachew Mekonen. Amharic Hate Speech Detection Techniques: Systematic Review. Authorea . 01 September 2025. DOI: https://doi.org/10.22541/au.175674794.49041244/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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