Identifying Cyberbullying Post on Social Networking Platform Using Machine Learning Technique-Reference-Cited by-同舟云学术

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Identifying Cyberbullying Post on Social Networking Platform Using Machine Learning Technique

Published:2022 Issue: Volume: Page:186-195
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
Short-container-title:

Author:

Roy Pradeep Kumar,Singh Ashish,Tripathy Asis Kumar,Das Tapan Kumar

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-4807-6_18

Reference22 articles.

1. Cecillon, N., Labatut, V., Dufour, R., Linarès, G.: Abusive language detection in online conversations by combining content-and graph-based features. Front. Big Data 2, 8 (2019)

2. Chapin, J.: Adolescents and cyber bullying: the precaution adoption process model. Edu. Inf. Technol. 21(4), 719–728 (2016)

3. Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., Vakali, A.: Mean birds: detecting aggression and bullying on twitter. In: Proceedings of the 2017 ACM on Web Science Conference, pp. 13–22 (2017)

4. Cheng, L., Li, J., Silva, Y.N., Hall, D.L., Liu, H.: Xbully: cyberbullying detection within a multi-modal context. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 339–347 (2019)

5. Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: European Conference on Information Retrieval, pp. 693–696. Springer (2013)

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1. Securing Social Spaces: Cyberbullying Detection with ML and DL on Social Media Platforms;2023 International Conference on Sustainable Communication Networks and Application (ICSCNA);2023-11-15

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