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Aggressive Social Media Post Detection System Containing Symbolic Images
- Source :
- Lecture Notes in Computer Science, 18th Conference on e-Business, e-Services and e-Society (I3E), 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⟩, Lecture Notes in Computer Science ISBN: 9783030293734, I3E
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- Part 6: Social Media and Analytics; International audience; 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.
- Subjects :
- Online Social Networks
business.industry
Computer science
Deep learning
Offensive
Convolutional Neural Network
02 engineering and technology
Crawling
Augmentation
Convolutional neural network
Cyberbullying
World Wide Web
Social group
Cyber-aggression
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Social media
[INFO]Computer Science [cs]
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-030-29373-4
- ISBNs :
- 9783030293734
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science, 18th Conference on e-Business, e-Services and e-Society (I3E), 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⟩, Lecture Notes in Computer Science ISBN: 9783030293734, I3E
- Accession number :
- edsair.doi.dedup.....5efba5fd7d40bfb03fcb123d162fc72a
- Full Text :
- https://doi.org/10.1007/978-3-030-29374-1_34⟩