VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination
This addresses the problem of automating hate speech detection for social network platforms to reduce manual moderation efforts, though it appears incremental as it builds on existing ensemble methods.
The paper tackled hate speech detection on social networks by proposing a supervised ensemble model, achieving first place with a 0.730 F1 macro-score on a public dashboard and third place with 0.584 on a private dashboard at a 2019 workshop.
Nowadays, Social network sites (SNSs) such as Facebook, Twitter are common places where people show their opinions, sentiments and share information with others. However, some people use SNSs to post abuse and harassment threats in order to prevent other SNSs users from expressing themselves as well as seeking different opinions. To deal with this problem, SNSs have to use a lot of resources including people to clean the aforementioned content. In this paper, we propose a supervised learning model based on the ensemble method to solve the problem of detecting hate content on SNSs in order to make conversations on SNSs more effective. Our proposed model got the first place for public dashboard with 0.730 F1 macro-score and the third place with 0.584 F1 macro-score for private dashboard at the sixth international workshop on Vietnamese Language and Speech Processing 2019.