Search Results for author:
Found 9 papers, 4 papers with code
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content.
Prompting Large Language Models for Topic Modeling
Topic modeling is a widely used technique for revealing underlying thematic structures within textual data.
MATK: The Meme Analytical Tool Kit
The rise of social media platforms has brought about a new digital culture called memes.
PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models
The proliferation of social media has given rise to a new form of communication: memes.
Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection
Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection.
TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore
While several countries have adopted total defence as their defence policy, very few studies have investigated its effectiveness.
Evaluating GPT-3 Generated Explanations for Hateful Content Moderation
A key concern is that these explanations, generated by LLMs, may lead to erroneous judgments about the nature of flagged content by both users and content moderators.
Decoding the Underlying Meaning of Multimodal Hateful Memes
Recent studies have proposed models that yielded promising performance for the hateful meme classification task.
On Explaining Multimodal Hateful Meme Detection Models
For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i. e., image and text) of the hateful memes.
