  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.
