Papers by Shraman Pramanick

2 papers
MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets (2021.findings-emnlp)

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Challenge: a growing number of harmful memes are being used for trolling, cyberbullying and abuse . a new approach to detect harmful meme images and texts is emerging .
Approach: They propose a multimodal deep neural network that detects harmful memes . they extend the recently released HarMeme dataset with additional memes and a new topic .
Outcome: The proposed framework outperforms rival methods in detecting harmful memes and their target social entities.
Detecting Harmful Memes and Their Targets (2021.findings-acl)

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Challenge: a growing body of research on meme analysis has focused on detecting harmful memes and their social entities . a meme is a form of content that is often harmless and designed to look funny . but its multimodal nature and camouflaged semantics make its analysis challenging .
Approach: They propose to use multimodal models to detect harmful memes and identify social entities that harmful meme targets.
Outcome: The proposed model can detect harmful memes and the social entities they target . the proposed model lacks the appropriate contexts and is poorly validated .

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