Large language models (LLMs) excel in many diverse applications beyond language
generation, eg, translation, summarization, and sentiment analysis. One intriguing …

Although social media platforms are a prominent arena for users to engage in interpersonal
discussions and express opinions, the facade and anonymity offered by social media may …

Social media platforms have become widely used for open communication, yet their lack of
moderation has led to the proliferation of harmful content, including hate speech. Manual …

espite their value in promoting open discourse, social media plat-forms are often exploited to
spread harmful content. Current deep learning and natural language processing models …

To counter the side effect brought by the proliferation of social media platforms, hate speech
detection (HSD) plays a vital role in halting the dissemination of toxic online posts at an early …

Content moderation on social media faces increasing challenges due to the rapid evolution
of hate speech. Identifying hate speech is challenging, especially as it constantly evolves to …

Implicit hate speech presents a significant challenge for automatic detection systems due to
its subtlety and ambiguity. Traditional models trained using empirical risk minimization …

Social media platforms provide a rich environment for analyzing user behavior. Recently,
deep learning-based methods have been a mainstream approach for social media analysis …

The rapid growth of social media platforms highlighted an urgent need for the moderation of
hateful language, which not only harms the online community and its users, but also carries …

[en] Detecting hate speech is crucial for maintaining the integrity of social media platforms,
as it involves identifying content that denigrates individuals or groups based on their …