What is the drawback to methods that rely on textual cues?
They don't provide wider discourse information
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet. Previous research suggests that such hateful content tends to come from users who share a set of common stereotypes and form communities around them. The current state-of-the-art approaches to hate speech detection are oblivious to user and community information and rely entirely on textual (i.e., lexical and semantic) cues. In this paper, we propose a novel approach to this problem that incorporates community-based profiling features of Twitter users. Experimenting with a dataset of 16k tweets, we show that our methods significantly outperform the current state of the art in hate speech detection. Further, we conduct a qualitative analysis of model characteristics. We release our code, pre-trained models and all the resources used in the public domain.
This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creativecommons.org/licenses/by/4.0/. Hate speech, a term used to collectively refer to offensive language, racist comments, sexist remarks, etc., is omnipresent in social media. Users on social media platforms are at risk of being exposed to content that may not only be degrading but also harmful to their mental health in the long term. Pew Research Center highlighted the gravity of the situation via a recently released report BIBREF0 . As per the report, 40% of adult Internet users have personally experienced harassment online, and 60% have witnessed the use of offensive names and expletives. Expectedly, the majority (66%) of those who have personally faced harassment have had their most recent incident occur on a social networking website or app. While most of these websites and apps provide ways of flagging offensive and hateful content, only 8.8% of the victims have actually considered using such provisions. These statistics suggest that passive or manual techniques for curbing propagation of hateful content (such as flagging) are neither effective nor easily scalable BIBREF1 . Consequently, the efforts to automate the detection and moderation of such content have been gaining popularity in natural language processing (nlp) BIBREF2 , BIBREF3 . Several approaches to hate speech detection demonstrate the effectiveness of character-level bag-of-words features in a supervised classification setting BIBREF4 , BIBREF5 , BIBREF6 . More recent approaches, and currently the best performing ones, utilize recurrent neural networks (rnns) to transform content into dense low-dimensional semantic representations that are then used for classification BIBREF1 , BIBREF7 . All of these approaches rely solely on lexical and semantic features of the text they are applied to. Waseem and Hovy c53cecce142c48628b3883d13155261c adopted a more user-centric approach based on the idea that perpetrators of hate speech are usually segregated into small demographic groups; they went on to show that gender information of authors (i.e., users who have posted content) is a helpful indicator. However, Waseem and Hovy focused only on coarse demographic features of the users, disregarding information about their communication with others. But previous research suggests that users who subscribe to particular stereotypes that promote hate speech tend to form communities online. For example, Zook zook mapped the locations of racist tweets in response to President Obama's re-election to show that such tweets were not uniformly distributed across the United States but formed clusters instead. In this paper, we present the first approach to hate speech detection that leverages author profiling information based on properties of the authors' social network and investigate its effectiveness. Author profiling has emerged as a powerful tool for NLP applications, leading to substantial performance improvements in several downstream tasks, such as text classification, sentiment analysis and author attribute identification BIBREF8 , BIBREF9 , BIBREF10 . The relevance of information gained from it is best explained by the idea of homophily, i.e., the phenomenon that people, both in real life as well as on the Internet, tend to associate more with those who appear similar. Here, similarity can be defined along various axes, e.g., location, age, language, etc. The strength of author profiling lies in that if we have information about members of a community $c$ defined by some similarity criterion, and we know that the person $p$ belongs to $c$ , we can infer information about $p$ . This concept has a straightforward application to our task: knowing that members of a particular community are prone to creating hateful content, and knowing that the author p is connected to this community, we can leverage information beyond linguistic cues and more accurately predict the use of hateful/non-hateful language from $p$ . The questions that we seek to address here are: are some authors, and the respective communities that they belong to, more hateful than the others? And can such information be effectively utilized to improve the performance of automated hate speech detection methods? In this paper, we answer these questions and develop novel methods that take into account community-based profiling features of authors when examining their tweets for hate speech. Experimenting with a dataset of $16k$ tweets, we show that the addition of such profiling features to the current state-of-the-art methods for hate speech detection significantly enhances their performance. We also release our code (including code that replicates previous work), pre-trained models and the resources we used in the public domain.