What datasets were used in this work?
DATA-TWITTER-WH, DATA-TWITTER-W, DATA-TWITTER-DAVID, DATA-TWITTER-F, DATA-WIKI-ATT, DATA-WIKI-AGG, DATA-WIKI-TOX, DATA-FOX-NEWS, DATA-GAZZETTA, DATA-FACEBOOK, Arabic News, GermEval, Ask.fun
Abuse on the Internet represents an important societal problem of our time. Millions of Internet users face harassment, racism, personal attacks, and other types of abuse on online platforms. The psychological effects of such abuse on individuals can be profound and lasting. Consequently, over the past few years, there has been a substantial research effort towards automated abuse detection in the field of natural language processing (NLP). In this paper, we present a comprehensive survey of the methods that have been proposed to date, thus providing a platform for further development of this area. We describe the existing datasets and review the computational approaches to abuse detection, analyzing their strengths and limitations. We discuss the main trends that emerge, highlight the challenges that remain, outline possible solutions, and propose guidelines for ethics and explainability
With the advent of social media, anti-social and abusive behavior has become a prominent occurrence online. Undesirable psychological effects of abuse on individuals make it an important societal problem of our time. Munro munro2011 studied the ill-effects of online abuse on children, concluding that children may develop depression, anxiety, and other mental health problems as a result of their encounters online. Pew Research Center, in its latest report on online harassment BIBREF0 , revealed that INLINEFORM0 of adults in the United States have experienced abusive behavior online, of which INLINEFORM1 have faced severe forms of harassment, e.g., that of sexual nature. The report goes on to say that harassment need not be experienced first-hand to have an impact: INLINEFORM2 of American Internet users admitted that they stopped using an online service after witnessing abusive and unruly behavior of their fellow users. These statistics stress the need for automated abuse detection and moderation systems. Therefore, in the recent years, a new research effort on abuse detection has sprung up in the field of NLP. That said, the notion of abuse has proven elusive and difficult to formalize. Different norms across (online) communities can affect what is considered abusive BIBREF1 . In the context of natural language, abuse is a term that encompasses many different types of fine-grained negative expressions. For example, Nobata et al. nobata use it to collectively refer to hate speech, derogatory language and profanity, while Mishra et al. mishra use it to discuss racism and sexism. The definitions for different types of abuse tend to be overlapping and ambiguous. However, regardless of the specific type, we define abuse as any expression that is meant to denigrate or offend a particular person or group. Taking a course-grained view, Waseem et al. W17-3012 classify abuse into broad categories based on explicitness and directness. Explicit abuse comes in the form of expletives, derogatory words or threats, while implicit abuse has a more subtle appearance characterized by the presence of ambiguous terms and figures of speech such as metaphor or sarcasm. Directed abuse targets a particular individual as opposed to generalized abuse, which is aimed at a larger group such as a particular gender or ethnicity. This categorization exposes some of the intricacies that lie within the task of automated abuse detection. While directed and explicit abuse is relatively straightforward to detect for humans and machines alike, the same is not true for implicit or generalized abuse. This is illustrated in the works of Dadvar et al. davdar and Waseem and Hovy waseemhovy: Dadvar et al. observed an inter-annotator agreement of INLINEFORM0 on their cyber-bullying dataset. Cyber-bullying is a classic example of directed and explicit abuse since there is typically a single target who is harassed with personal attacks. On the other hand, Waseem and Hovy noted that INLINEFORM1 of all the disagreements in annotation of their dataset occurred on the sexism class. Sexism is typically both generalized and implicit. In this paper, we survey the methods that have been developed for automated detection of online abuse, analyzing their strengths and weaknesses. We first describe the datasets that exist for abuse. Then we review the various detection methods that have been investigated by the NLP community. Finally, we conclude with the main trends that emerge, highlight the challenges that remain, outline possible solutions, and propose guidelines for ethics and explainability. To the best of our knowledge, this is the first comprehensive survey in this area. We differ from previous surveys BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 in the following respects: 1) we discuss the categorizations of abuse based on coarse-grained vs. fine-grained taxonomies; 2) we present a detailed overview of datasets annotated for abuse; 3) we provide an extensive review of the existing abuse detection methods, including ones based on neural networks (omitted by previous surveys); 4) we discuss the key outstanding challenges in this area; and 5) we cover aspects of ethics and explainability.