Discrimination discovery is the data mining problem of unveiling discriminatory practices by analyzing a dataset of historical decision records. In this paper, we focus on discovering discrimination from tweets using deep learning models. One challenge here is that it is dicult to obtain a large well-labeled dataset required by the training of deep learning models for the purpose of discrimination analysis. We develop a two-phase deep learning model to address this challenge. Our model rst learns text representations based on weakly-labeled tweets (containing some specic hashtags), then trains the classier
['Shuhan Yuan', 'Xintao Wu', 'Yang Xiang']
A Two Phase Deep Learning Model for Identifying Discrimination from Tweets