Open Access
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
Article Number 03030
Number of page(s) 7
Section Computing
Published online 09 August 2021
  1. Rajesh Basak, Shamik Sural, Niloy Ganguly, and Soumya K. Ghosh, “Online Public Shaming on Twitter: Detection, Analysis, and Mitigation.”, IEEE Transaction-2329-924X,2019. [Google Scholar]
  2. S. Poria, E. Cambria, D. Hazarika, and P. Vij, “A deeper look into sarcastic tweets using deep convolutional neural networks,” Creative Commons Attribution 4.0,arXiv preprint DOI:1610.08815, 2016. [Google Scholar]
  3. Sreelakshmi K Rafeeque P C, “An Effective Approach for Detection of Sarcasm in Tweets”, International CET Conference on Control, Communication, and Computing (IC4)DOI-978-1-5386-4966-4,2018. [Google Scholar]
  4. Mukul Anand, Dr.R.Eswari, “ Classification of Abusive Comments in Social Media using Deep Learning”, IEEE DOI-978-1-5386-7808-4,2019. [Google Scholar]
  5. Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, Yi Chang, “Abusive Language Detection in Online User Content”, IW3C2, DOI-10.1145/2872427.2883062,2019. [Google Scholar]
  6. Soham Deshmukh, Rahul Rade, “Tackling Toxic Online Communication with Recurrent Capsule Networks”, IEEE-DOI:10.1109/INFOCOMTECH.2018.8722433 2018. [Google Scholar]
  7. Mai Ibrahim, Marwan Torki and Nagwa El-Makky, “Imbalanced Toxic Comments Classification using Data Augmentation and Deep Learning”, 2018. [Google Scholar]
  8. Sangita Holkar, S. D. Sawarkar, Shubhangi Vaikole, “Audio and Video Toxic Comments Detection and Classification”, International Journal of Engineering Research & Technology (IJERT), Vol. 9 Issue 12, IISSN: 2278–0181,2020. [Google Scholar]
  9. Mehdi Surani, Ramchandra Mangrulkar,” Online Public Shaming Approach using Deep Learning Techniques”, Journal of the University of Shanghai for Science and Technology ISSN: 1007-6735,2021. [Google Scholar]
  10. Manav Kohli, Emily Kuehler and John Palowitch. “Paying attention to toxic comments.” Stanford University,2018. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.