Open Access
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
Article Number 03034
Number of page(s) 6
Section Computing
Published online 05 May 2022
  1. Kovács, G., Alonso, P. & Saini, R. “Challenges of Hate Speech Detection in Social-Media”. SN COMPUT. SCI. 2, 95 (2021). [CrossRef] [Google Scholar]
  2. V. Jain, V. Kumar, V. Pal and D. K. Vishwakarma, “Detection of Cyberbullying on Social Media Using Machine learning”. (2021). [Google Scholar]
  3. Salminen, J., Hopf, M., Chowdhury, S.A. et al. “Developing an online hate classifier for multiple social media platforms”. (2020). [Google Scholar]
  4. Venkateshwarlu Konduri, Sarada Padathula, Asish Pamu and Sravani Sigadam, “Hate Speech Classification of social media posts using Text Analysis and Machine Learning”. Paper 5204, (2020). global-forum-proceedings/2020/5204-2020.pdf [Google Scholar]
  5. Velioglu, Riza & Rose, Jewgeni, “Detecting Hate Speech in Memes” Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge. (2020). Detecting Hate Speech in Memes Using Multimod al Deep Learning Approaches Prizewinning solution to Hateful Memes Challenge [Google Scholar]
  6. Hou, Y., Xiong, D., Jiang, T., Song, L., & Wang, Q. “Social media addiction: Its impact, mediation, and intervention”. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 13(1), Article 4. (2019). [CrossRef] [Google Scholar]
  7. Kumar, R., Ojha, A.K., Malmasi, S., Zampieri, M., “Benchmarking aggression identification in social media”. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC), (2018) [Google Scholar]
  8. Zimmerman, Steven & Fox, Chris & Kruschwitz, Udo. “Improving Hate Speech Detection with Deep Learning Ensembles”. (2018). I mproving Hate Speech Detection with Deep Learni ng Ensembles [Google Scholar]
  9. Samuel Gibbs. “What can be done about abuse on social media?” Guardian News & Media Limited, 12, (2017) [Google Scholar]
  10. Davidson, Thomas & Warmsley, Dana & Macy, Michael & Weber, Ingmar, “Automated Hate Speech Detection and the Problem of Offensive Language”. (2017). Automated Hate Speech Detection and the Proble m of Offensive Language [Google Scholar]
  11. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. “An Introduction to Statistical Learning”. With Applications in R. Springer Publishing Company, Incorporated, ISBN, (2014). [Google Scholar]
  12. Bernd Hollerit, Mark Kroll, and Markus Strohmaier. “Towards linking buyers and sellers: Detecting commercial intent on twitter”. Published in WWW ’13 Companion 2013 Computer Science, (2013). [Google Scholar]
  13. Ostrowski, David, “Semantic Filtering in social media for Trend Modelling”. Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC. 399–404. doi: 10.1109/ICSC.2013, 78. (2013) [Google Scholar]
  14. Cheng Xiang Zhai, Charu C. Aggarwal. “Mining Text Data”. Science + Business Media. Springer, (2012). [Google Scholar]
  15. K. Lee, D. Palsetia, R. Narayanan, M.M.A. Patwary, A. Agrawal, and A. Choudhary. “Twitter trending topic classification”. In Data Mining Workshops (ICDMW). (2011) [Google Scholar]
  16. S. Asur and B. A. Huberman, “Predicting the Future with Social Media”. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 492–499, doi: 10.1109/WI-IAT.2010.63. (2010), [Google Scholar]
  17. Song Jie Gong. “A collaborative filtering recommendation system algorithm based on user clustering and item clustering”. Journal of Software, vol. 5, (2010). [Google Scholar]
  18. Kristin P. Bennett and Erin J. Bredensteiner. “Duality and Geometry in SVM Classifiers”. In In Proc. 17th International Conf, on Machine Learning, pages 57–64, (2000). [Google Scholar]

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