18 citations · 24 across the 7 of their papers we have counts for
8 papers
Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis
Shivam Sharma, Mohd Khizir Siddiqui, Md. Shad Akhtar +1
Existing self-supervised learning strategies are constrained to either a limited set of objectives or generic downstream tasks that predominantly target uni-modal applications. Thi…
Detecting and Understanding Harmful Memes: A Survey
Shivam Sharma, Firoj Alam, Md. Shad Akhtar +7
The automatic identification of harmful content online is of major concern for social media platforms, policymakers, and society. Researchers have studied textual, visual, and audi…
DISARM: Detecting the Victims Targeted by Harmful Memes
Shivam Sharma, Md. Shad Akhtar, Preslav Nakov +1
Internet memes have emerged as an increasingly popular means of communication on the Web. Although typically intended to elicit humour, they have been increasingly used to spread h…
EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko +5
Machine learning models that learn from dynamic graphs face nontrivial challenges in learning and inference as both nodes and edges change over time. The existing large-scale graph…
Detecting Harmful Memes and Their Targets
Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee +4
Among the various modes of communication in social media, the use of Internet memes has emerged as a powerful means to convey political, psychological, and socio-cultural opinions.…
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets
Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov +3
Internet memes have become powerful means to transmit political, psychological, and socio-cultural ideas. Although memes are typically humorous, recent days have witnessed an escal…