{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:23:07Z","timestamp":1780392187813,"version":"3.54.1"},"reference-count":38,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out in detecting hate speeches in English, German, and other languages, very few works have been carried out in the context of the Bengali language. In contrast, millions of people communicate on social media in Bengali. The few existing works that have been carried out need improvements in both accuracy and interpretability. This article proposed encoder\u2013decoder-based machine learning model, a popular tool in NLP, to classify user\u2019s Bengali comments from Facebook pages. A dataset of 7,425 Bengali comments, consisting of seven distinct categories of hate speeches, was used to train and evaluate our model. For extracting and encoding local features from the comments, 1D convolutional layers were used. Finally, the attention mechanism, LSTM, and GRU-based decoders have been used for predicting hate speech categories. Among the three encoder\u2013decoder algorithms, attention-based decoder obtained the best accuracy (77%).<\/jats:p>","DOI":"10.1515\/jisys-2020-0060","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T04:06:38Z","timestamp":1620792398000},"page":"578-591","source":"Crossref","is-referenced-by-count":94,"title":["Bangla hate speech detection on social media using attention-based recurrent neural network"],"prefix":"10.1515","volume":"30","author":[{"given":"Amit Kumar","family":"Das","sequence":"first","affiliation":[{"name":"Computer Science & Engineering (CSE), East West University, Dhaka , Dhaka , Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Al Asif","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering (CSE), East West University, Dhaka , Dhaka , Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anik","family":"Paul","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering (CSE), East West University, Dhaka , Dhaka , Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Nur","family":"Hossain","sequence":"additional","affiliation":[{"name":"Computer Science & Engineering (CSE), East West University, Dhaka , Dhaka , Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"2025120523322276772_j_jisys-2020-0060_ref_001","unstructured":"Duggan M. Online harassment 2017. Washington: Pew Research Center; 2017."},{"key":"2025120523322276772_j_jisys-2020-0060_ref_002","doi-asserted-by":"crossref","unstructured":"Emon EA, Rahman S, Banarjee J, Das AK, Mittra T. A deep learning approach to detect abusive bengali text. In 2019 7th International Conference on Smart Computing Communications (ICSCC), 2019. p. 1\u20135. 10.1109\/ICSCC.2019.8843606.","DOI":"10.1109\/ICSCC.2019.8843606"},{"key":"2025120523322276772_j_jisys-2020-0060_ref_003","unstructured":"Kallas P. Top 15 most popular social networking sites and apps [august 2018][online], 2018. https:\/\/www.dreamgrow.com\/top-15-most-popularsocial-networking-sites\/."},{"key":"2025120523322276772_j_jisys-2020-0060_ref_004","unstructured":"Tarik I. Demographics of facebook population in Bangladesh, April 2018. http:\/\/digiology.xyz\/demographicsfacebook-population-bangladesh-april-2018\/."},{"key":"2025120523322276772_j_jisys-2020-0060_ref_005","unstructured":"W. A. Social and hootsuite. (2018) 2018 digital yearbook[online]. 2018. https:\/\/digitalreport.wearesocial.com\/."},{"key":"2025120523322276772_j_jisys-2020-0060_ref_006","unstructured":"Unb D. 49% bangladeshi school pupils face cyberbullying[online]. 2016. https:\/\/cutt.ly\/Wh7h8x0."},{"key":"2025120523322276772_j_jisys-2020-0060_ref_007","unstructured":"Ethnologue. 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