Tackling Online Abuse: A Survey of Automated Abuse Detection Methods

By Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova. 2019

92 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence, Information Systems, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In Safety, Robustness & Interpretability

Citations in the corpus

HateCheck: Functional Tests for Hate Speech Detection Models (2020, background): “However, even state-of-the-art models exhibit substantial weaknesses (see Schmidt and Wiegand, 2017; Fortuna and Nunes, 2018; Vidgen et al., 2019; Mishra et al., 2020, for reviews).”
A Multimodal Framework for the Detection of Hateful Memes (2020, background): “[25] discuss emerging trends, resources and challenges, as well as outline the various approaches used in the domain of online abusive language detection.”
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021, background): “Model limitations Systems trained on existing datasets have been shown to lack accuracy, robustness and generalisability, creating a range of false positives and false negatives (Schmidt and Wiegand, 2017; Mishra et al., 2019; Vidgen and Derczynski, 2020; Röttger et al., 2020; Mathew et al., 2020).”
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus (2021, background): “several datasets and approaches being proposed in recent years (Schmidt and Wiegand, 2017; Mishra et al., 2019; Vidgen and Derczynski, 2020; Kiritchenko and Mohammad, 2018).”
Towards Ethics by Design in Online Abusive Content Detection (2020, background): “Recent surveys by Schmidt and Wie-gand (2017); Fortuna and Nunes (2018); Mishra et al. (2019); Vidgen et al. (2019); Vidgen and Der-czynski (2020); Salawu et al. (2020) summarize the advancements in these areas focusing mostly on the technical issues and the variety of machine learning approaches…”
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective (2020, background): “Recent surveys by Schmidt and Wiegand (2017), Fortuna and Nunes (2018), Mishra et al. (2019), Vidgen, Harris, Nguyen, Tromble, Hale, and Margetts (2019), Vidgen and Derczynski (2020), and Salawu, He, and Lumsden (2020) summarize the advancements in these areas focusing mostly on the technical…”
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations (2021, background): “NLP-aided approaches to detect abusive behavior online is an active research area (Schmidt and Wiegand, 2017; Mishra et al., 2019; Corazza et al., 2020).”
On Cross-Dataset Generalization in Automatic Detection of Online Abuse (2020, methodology): “Several publicly available datasets have been created for the task (Mishra et al., 2019; Vidgen and Derczynski, 2020).”
Joint Modelling of Emotion and Abusive Language Detection (2020, background): “Much of the recent research has focused on detecting explicit abuse, that comes in the form of expletives, derogatory words or threats, with substantial success (Mishra et al., 2019b).”