Multimodal Deep Learning with Discriminant Descriptors for Offensive Memes Detection
Abstract
References
Index Terms
- Multimodal Deep Learning with Discriminant Descriptors for Offensive Memes Detection
Recommendations
Multimodal Religiously Hateful Social Media Memes Classification based on Textual and Image Data
Multimodal hateful social media meme detection is an important and challenging problem in the vision-language domain. Recent studies show high accuracy for such multimodal tasks due to datasets that provide better joint multimodal embedding to narrow the ...
A Multitask Framework for Sentiment, Emotion and Sarcasm aware Cyberbullying Detection from Multi-modal Code-Mixed Memes
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalDetecting cyberbullying from memes is highly challenging, because of the presence of the implicit affective content which is also often sarcastic, and multi-modality (image + text). The current work is the first attempt, to the best of our knowledge, in ...
On the Origins of Memes by Means of Fringe Web Communities
IMC '18: Proceedings of the Internet Measurement Conference 2018Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation of memes across multiple Web ...
Comments
Information & Contributors
Information
Published In
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 608Total Downloads
- Downloads (Last 12 months)500
- Downloads (Last 6 weeks)25
Other Metrics
Citations
Cited By
View allView Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in