The internet, and particularly social media, have grown exponentially over the last decades. The nature of social media allows anyone to go online and create content they find interesting, whether appropriate or not. One .
The internet, and particularly social media, have grown exponentially over the last decades. The nature of social media allows anyone to go online and create content they find interesting, whether appropriate or not. One form of inappropriate content is hate speech—offensive or threatening speech targeting certain people based on their ethnicity, religion, sexual orientation, and the like.
Hate speech detection models are computational systems that can identify and classify online comments as hate speech.
« These models are crucial in moderating online content and mitigating the spread of harmful speech, particularly on social media », said Assistant Professor Roy Lee from the Singapore University of Technology and Design (SUTD). Evaluating the performance of hate speech detection models is important, but traditional evaluation using held-out test sets often fail to properly assess the model’s performance due to inherent bias within the datasets.
To overcome this limitation, HateCheck and Multilingual HateCheck (MHC) were introduced as functional tests that capture the complexity and diversity of hate speech by simulating real-world scenarios. In their research paper titled « SGHateCheck: Functional tests for detecting hate speech in low-resource languages of Singapore », Asst. Prof. Lee and his team build on the frameworks of HateCheck and MHC to develop SGHateCheck, an artificial intelligence (AI)-powered tool that can distinguish between hateful and non-hateful comments in the specific context of Singapore and Southeast Asia.
