Leveraging Multilingual Transformers for Hate Speech Detection

Using two large hand-labelled datasets of tweets containing spam, we study the suitabil- ity of five classification algorithms and four di↵erent feature sets to the social

FIRE2020: Using Machine Learning for Detection of Hate Speech and Offensive Code-Mixed Social Media text.. Varsha Pathak a , Manish Joshi b , Prasad Joshi c , Monica Mundada d

Despite the fact that different approaches have been proposed and evaluated for auto- matic hate detection, the current situation still demands an in-depth analysis in which

Given the well-acknowledged rise in the pres- ence of toxic and abusive speech on social media platforms like Twitter and Facebook, there have been several efforts within the

For Task B, our winning system himani.c.run3 consists of a pipeline of two classifiers: the first classifier (step 1) in the pipeline labels a tweet as misogynous or not, while

• In the second phase we trained a Word2Vec model, starting from 200k comments we download from some Facebook pages known to contain hate messages.. Each of the ini- tial data

Building on this principle, we scraped data from social media communities on Facebook, acquiring what we call source-driven representations. The data is indeed used in two ways in

L’articolo descrive la nostra partecipazione al task di Named Entity rEcognition and Linking in Italian Tweets (NEEL-IT) a Evalita 2016.. Il no- stro approccio si