A Survey on Automatic Detection of Hate Speech in Text

By Paula Fortuna, Sérgio Nunes. 2018

1,295 citations recorded by the source corpus.

The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

HateCheck: Functional Tests for Hate Speech Detection Models (2020, background): “tified linguistic challenges in hate speech detection from review articles and commentaries (e.g. Schmidt and Wiegand, 2017; Fortuna and Nunes, 2018; Vidgen et al., 2019).”
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Several surveys cover approaches to this problem (Schmidt & Wiegand, 2017; Fortuna & Nunes, 2018; Vidgen et al., 2019), and there exist reviews of offensive language datasets (Fortuna et al., 2020; Vidgen & Derczynski, 2020).”
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, background): “Hate speech There has been a lot of work in recent years on detecting hate speech in network science [61] and natural language processing [81, 65, 19].”
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021, background): “Several studies raise concerns about the quality and consistency of hate speech datasets, as well as the taxonomic schema they use (Schmidt and Wiegand, 2017b; Fortuna and Nunes, 2018; Vidgen et al., 2019a; Poletto et al., 2020).”
Racial Bias in Hate Speech and Abusive Language Detection Datasets (2019, background): “While hate speech and abusive language detection has become an important area for natural language processing research (Schmidt and Wiegand, 2017; Waseem et al., 2017; Fortuna and Nunes, 2018), there has been little work addressing the potential for these systems to be biased.”
Challenges and frontiers in abusive content detection (2019, background): “Advances in machine learning and NLP have led to marked improvements in abusive content detection systems’ performance (Fortuna & Nunes, 2018; Schmidt & Wiegand, 2017).”
What’s in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus (2021, neutral): “…sources such as Twitter (Founta et al., 2018; Basile et al., 2019; Waseem and Hovy, 2016) and Reddit (Tadesse et al., 2019; Farrell et al., 2019), with both ML-based (Badjatiya et al., 2017) and count-based approaches (Davidson et al., 2017) achieving comparable results (Fortuna and Nunes, 2018).”
Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts (2019, background): “Instead, much of the recent work has focused on explicitly toxic language (e.g., Waseem et al., 2017), with surveys of the area also overlooking this important and challenging task of recognizing this subtle toxicity (van Aken et al., 2018; Salmi-nen et al., 2018; Fortuna and Nunes, 2018).”
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection (2020, methodology): “The ﬁnal dataset is composed of 9,055 posts from Twitter and 11,093 posts from Gab.”
Demoting Racial Bias in Hate Speech Detection (2020, background): “…toll on human moderators has generated much interest in automated systems for detecting hate speech and abusive language (Schmidt and Wiegand, 2017; Fortuna and Nunes, 2018), especially language that targets particular social groups (Silva et al., 2016; Mondal et al., 2017; Mathew et al., 2019).”
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021, background): “Inferiority language is also related to assaults on human dignity (Kennedy et al., 2018), dominance (Saha et al., 2018), and declarations of superiority of the in-group (Fortuna and Nunes, 2018).”
Resources and benchmark corpora for hate speech detection: a systematic review (2020, methodology): “We carefully read each work and labeled it with a set of speciﬁcally-designed labels, sorting our list by research ﬁeld (e.g., ﬁeld-socialsciences , ﬁeld-NLP , etc.), main focus (e.g., content-resource , content-system , etc.), methodology (e.g., method-nn for neural nets, method-ml for machine lear”
Taxonomy of Risks posed by Language Models (2022, background): “…create a range of harms, such as promoting social stereotypes that perpetuate the derogatory representation or unfair treat-ment of marginalised groups [22], inciting hate or violence [57], causing profound offence [199], or reinforcing social norms that exclude or marginalise identities [15, 58].”
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2020, background): “There have been several areas of research in the detection of offensive language (Basile et al., 2019; Fortuna and Nunes, 2018; Ranasinghe and Zampieri, 2020), covering overlapping characteristics such as toxicity, hate speech, cyberbullying,”
Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application (2020, neutral): “Prior work on hate speech has often focused on a single platform, commonly Twitter, but our goal was to study hate speech in a variety of settings and to ultimately develop a model to measure hate speech without reliance on platform-specific language patterns (Fortuna and Nunes, 2018).”
Towards Ethics by Design in Online Abusive Content Detection (2020, methodology): “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…”
Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, methodology): “• Fortuna and Nunes [26] provide an end-to-end review of hate speech research, including the motivations for studying online hate, definitional challenges, dataset creation/sharing, and technical advances, both in terms of feature selection and algorithmic architecture ([26], 2018).”
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers (2021, background): “…its strategy may involve shifting the original intent, since “republican” is not a non-offensive synonym of “moron”. et al., 2017; Pavlopoulos et al., 2017; Wulczyn et al., 2017; Gamb¨ack and Sikdar, 2017; Fortuna and Nunes, 2018; Zhang et al., 2018a; Van Hee et al., 2018; Zampieri et al., 2019).”