{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T06:21:46Z","timestamp":1781850106175,"version":"3.54.5"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>Cyberbullying on social media platforms remains a serious threat to digital well-being, requiring intelligent systems capable of detecting both explicit and subtle, emotionally charged abuse. Sentiment analysis (SA) plays a key role by interpreting emotional tone, polarity, and context, offering more nuanced and timely detection than keyword-based models. Emotions like anger, sarcasm, or veiled hostility often precede cyberbullying, especially during impulsive interactions. SA captures these affective cues, improving sensitivity to implicit abuse and coded language. This study presents the first systematic comparison of sentiment-enhanced transformer models such as ALBERT, DeBERTa, ELECTRA, HateBERT, and DeepSeek-coder-1.3b-base, fine-tuned for cyberbullying detection across Twitter (currently X), IMDB, and Amazon. Models were evaluated on predictive performance (Accuracy, Precision, Recall, F1-score), time and cost efficiency (inference time, memory, CPU\/GPU use, and energy). ELECTRA + SA outperformed all models, achieving 91.85% accuracy, precision, and recall, and a 91.84% F1-score. It also excelled in efficiency, with 0.069 seconds inference time, 23.92 MB RAM use, 7.2% CPU\/GPU usage, and 0.000075 kWh energy consumption, proving highly generalizable, sentiment-sensitive, and suitable for real-time, resource-aware deployment. These results highlight the importance of sentiment integration, dataset diversity, and computational efficiency in building scalable, real-world cyberbullying detection systems.<\/jats:p>","DOI":"10.1145\/3766075","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T11:13:00Z","timestamp":1760008380000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Sentiment-Enhanced Cyberbullying Detection Models on Social Media Platforms"],"prefix":"10.1145","volume":"20","author":[{"given":"Adamu","family":"Philipo","sequence":"first","affiliation":[{"name":"Computer Science and Technology, University of Science and Technology Beijing School of Computer and Communication Engineering","place":["Haidian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianguo","family":"Ding","sequence":"additional","affiliation":[{"name":"Computer Science, Blekinge Institute of Technology","place":["Karlskrona, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doreen","family":"Sarwatt","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, University of Science and Technology Beijing School of Computer and Communication Engineering","place":["Haidian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jumanne","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Engineering, Dar es Salaam Institute of Technology","place":["Dar es Salaam, Tanzania, United Republic of"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afidhu","family":"Yusufu","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Engineering, Dar es Salaam Institute of Technology","place":["Dar es Salaam, Tanzania, United Republic of"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmoud","family":"Daneshmand","sequence":"additional","affiliation":[{"name":"Business Intelligence and Analytics and Computer Science, Stevens Institute of Technology","place":["Hoboken, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6413-193X","authenticated-orcid":false,"given":"Huansheng","family":"Ning","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, University of Science and Technology Beijing School of Computer and Communication Engineering","place":["Haidian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-21199-7_14"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3280556"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-80334-5_2"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-19869-3"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-28073-3_3"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI51800.2020.00056"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSCI54926.2021.00098"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/SNAMS64316.2024.10883815"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.woah-1.3"},{"key":"e_1_3_1_11_2","unstructured":"Despoina Chatzakou Nicolas Kourtellis Jeremy Blackburn Emiliano De Cristofaro Gianluca Stringhini and Athena Vakali. 2017. Mean Birds: Detecting Aggression and Bullying on Twitter. arXiv.1702.06877. Retrieved from https:\/\/arxiv.org\/abs\/1702.06877"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-2456-9_73"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2003.10555"},{"key":"e_1_3_1_14_2","unstructured":"Maral Dadvar and Kai Eckert. 2018. Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study. arXiv.1812.08046. Retrieved from https:\/\/arxiv.org\/abs\/1812.08046"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_4"},{"key":"e_1_3_1_16_2","unstructured":"DeepSeek-AI. 2024. DeepSeek-Coder: A Family of Open Code Language Models. Retrieved April 15 2025 from https:\/\/huggingface.co\/DeepSeek-AI\/deepseek-coder-1.3b-base"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1051\/shsconf\/202317903032"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/math11163567"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.08.063"},{"key":"e_1_3_1_20_2","unstructured":"Google LLC. 2025. Perspective API. Retrieved April 28 2025 from https:\/\/perspectiveapi.com"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Daya Guo Qihao Zhu Dejian Yang Zhenda Xie Kai Dong Wentao Zhang Guanting Chen Xiao Bi Y. Wu Y. K. Li Fuli Luo Yingfei Xiong and Wenfeng Liang. 2024. DeepSeek-Coder: When the Large Language Model Meets Programming \u2013 The Rise of Code Intelligence. DOI:10.48550\/arXiv.2401.14196 Retrieved from https:\/\/arxiv.org\/abs\/2401.14196","DOI":"10.48550\/arXiv.2401.14196"},{"key":"e_1_3_1_22_2","first-page":"52","volume-title":"Proceedings of the 4th Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024","author":"Guo Xiaoyu","year":"2024","unstructured":"Xiaoyu Guo and Susan Gauch. 2024. Using sarcasm to improve cyberbullying detection. In Proceedings of the 4th Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024, Ritesh Kumar, Atul Kr. Ojha, Shervin Malmasi, Bharathi Raja Chakravarthi, Bornini Lahiri, Siddharth Singh, and Shyam Ratan (Eds.). ELRA and ICCL, Torino, Italia, 52\u201359."},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-024-01291-0"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-024-01291-0"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2006.03654"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883037"},{"key":"e_1_3_1_27_2","unstructured":"Laxmi Kant. 2020. IMDB Dataset - All CSV ML Data Files Download. Retrieved April 15 2025 from https:\/\/raw.githubusercontent.com\/laxmimerit\/All-CSV-ML-Data-Files-Download\/master\/IMDB-Dataset.csv"},{"key":"e_1_3_1_28_2","unstructured":"Zhenzhong Lan Mingda Chen Sebastian Goodman Kevin Gimpel Piyush Sharma and Radu Soricut. 2019. ALBERT: A lite BERT for self-supervised learning of language representations. (2019) 1\u201312. arXiv:arXiv:1909.11942. Retrieved from https:\/\/arxiv.org\/abs\/1909.11942"},{"key":"e_1_3_1_29_2","unstructured":"Zhenzhong Lan Mingda Chen Sebastian Goodman Kevin Gimpel Piyush Sharma and Radu Soricut. 2020. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. arXiv:1909.11942. Retrieved from https:\/\/arxiv.org\/abs\/1909.11942"},{"key":"e_1_3_1_30_2","unstructured":"Maggie Phil Culliton and Wei Chen. 2020. Tweet Sentiment Extraction. Retrieved April 15 2025 from https:\/\/kaggle.com\/competitions\/tweet-sentiment-extraction"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmes.2024.052291"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-41682-8_9"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-41682-8_9"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3376958"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3183046"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3230974"},{"key":"e_1_3_1_37_2","unstructured":"Massive Text Embedding Benchmark (MTEB). 2024. Amazon Polarity Dataset (MTEB). Retrieved April 15 2025 from https:\/\/huggingface.co\/datasets\/mteb\/amazon_polarity"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.3390\/analytics2030038"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3673277.3673312"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICITACEE62763.2024.10762775"},{"key":"e_1_3_1_41_2","unstructured":"Adamu Gaston Philipo Doreen Sebastian Sarwatt Jianguo Ding Mahmoud Daneshmand and Huansheng Ning. 2024. Cyberbullying Detection: Exploring Datasets Technologies and Approaches on Social Media Platforms. arXiv:2407.12154. Retrieved from https:\/\/arxiv.org\/abs\/2407.12154"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3588728"},{"key":"e_1_3_1_43_2","doi-asserted-by":"crossref","unstructured":"Tharindu Ranasinghe and Marcos Zampieri. 2023. A Text-to-Text Model for Multilingual Offensive Language Identification. arXiv:2312.03379. Retrieved from https:\/\/arxiv.org\/abs\/2312.03379","DOI":"10.18653\/v1\/2023.findings-ijcnlp.33"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.4108\/eetinis.v11i1.4703"},{"key":"e_1_3_1_45_2","first-page":"1475","volume-title":"Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","author":"Sharma R.","year":"2023","unstructured":"R. Sharma, N. L. Tan, and F. Sadat. 2023. Multimodal sentiment analysis using deep learning. In Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, Orlando, Florida, USA, 1475\u20131478."},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICOIACT59844.2023.10455817"},{"key":"e_1_3_1_47_2","unstructured":"Peiling Yi and Arkaitz Zubiaga. 2022. Cyberbullying detection across social media platforms via platform-aware adversarial encoding. arXiv:2204.00334. Retrieved from https:\/\/arxiv.org\/abs\/2204.00334"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/CISCE50729.2020.00056"}],"container-title":["ACM Transactions on the Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3766075","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:18:33Z","timestamp":1772749113000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3766075"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,18]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2,28]]}},"alternative-id":["10.1145\/3766075"],"URL":"https:\/\/doi.org\/10.1145\/3766075","relation":{},"ISSN":["1559-1131","1559-114X"],"issn-type":[{"value":"1559-1131","type":"print"},{"value":"1559-114X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,18]]},"assertion":[{"value":"2025-07-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-02-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}