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.
Unsupervised learning approaches are quite common for detecting offensive messages in text by applying concepts from NLP to exploit the lexical syntactic features of sentences , or using AI-solutions and bag-of-words based text-representations . The latter is known to be less effective for automatic detection, since hatred users apply various obfuscation tricks, such as replacing a single character in offensive words. For instance, applying a binary classifier onto a representation of words has already been attempted on Amazon data in the past , but it only performed well on a binary classification problem. Another unsupervised learning based solution is the work by @cite_8 , in which the authors proposed a set of criteria that a tweet should exhibit in order to be classified as offensive. They also showed that differences in geographic distribution of users have only marginal effect on the detection performance. Despite the above observation, we explore other features that might be possible to improve the detection accuracy in the solution outlined below.
@cite_8
2473555522
Hate speech in the form of racist and sexist remarks are a common occurrence on social media. For that reason, many social media services address the problem of identifying hate speech, but the definition of hate speech varies markedly and is largely a manual effort (BBC, 2015; Lomas, 2015). We provide a list of criteria founded in critical race theory, and use them to annotate a publicly available corpus of more than 16k tweets. We analyze the impact of various extra-linguistic features in conjunction with character n-grams for hatespeech detection. We also present a dictionary based the most indicative words in our data.