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Nowadays, Social network sites (SNSs) such as Facebook, Twitter are commonplaces where people show their opinions, sentiments and share information withothers. However, some people use SNSs to post abuse and harassment threats inorder to prevent other SNSs users from expressing themselves as well as seekingdifferent opinions. To deal with this problem, SNSs have to use a lot ofresources including people to clean the aforementioned content. In this paper,we propose a supervised learning model based on the ensemble method to solvethe problem of detecting hate content on SNSs in order to make conversations onSNSs more effective. Our proposed model got the first place for publicdashboard with 0.730 F1 macro-score and the third place with 0.584 F1macro-score for private dashboard at the sixth international workshop onVietnamese Language and Speech Processing 2019.

VAIS Hate Speech Detection System: A Deep Learning based Approach for System Combination