Skip to Main content Skip to Navigation
Conference papers

Aggressive Social Media Post Detection System Containing Symbolic Images

Abstract : Social media platforms are an inexpensive communication medium help to reach other users very quickly. The same benefit is also utilized by some mischievous users to post objectionable images and symbols to certain groups of people. This types of posts include cyber-aggression, cyberbullying, offensive content, and hate speech. In this work, we analyze images posted on online social media sites to hurt online users. In this research, we designed a deep learning based system to classify aggressive post from a non-aggressive post containing symbolic images. To show the effectiveness of our model, we created a dataset crawling images from Google search to query aggressive images. The validation shows promising results.
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download
Contributor : Hal Ifip <>
Submitted on : Tuesday, March 17, 2020 - 2:54:42 PM
Last modification on : Wednesday, June 9, 2021 - 3:26:02 PM
Long-term archiving on: : Thursday, June 18, 2020 - 3:18:15 PM


 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2022-01-01

Please log in to resquest access to the document


Distributed under a Creative Commons Attribution 4.0 International License



Kirti Kumari, Jyoti Singh, Yogesh Dwivedi, Nripendra Rana. Aggressive Social Media Post Detection System Containing Symbolic Images. 18th Conference on e-Business, e-Services and e-Society (I3E), Sep 2019, Trondheim, Norway. pp.415-424, ⟨10.1007/978-3-030-29374-1_34⟩. ⟨hal-02510116⟩



Record views