Detecting Offensive Language in Tweets Using Deep Learning
This addresses the problem of filtering offensive language on social media for users and platforms, but it is incremental as it builds on existing deep learning methods.
The paper tackled detecting hateful content in tweets by proposing an ensemble of RNN classifiers with user-related features, achieving higher classification quality than state-of-the-art algorithms on a dataset of 16k tweets.
This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.