Detecting Hate Speech on the World Wide Web

By William Warner, Julia Hirschberg. 2012

731 citations recorded by the source corpus.

Subfields: Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Challenges in Detoxifying Language Models (2021, background): “While detecting hate speech and offensive language (Warner and Hirschberg, 2012; Kwok and Wang, 2013; Davidson et al., 2017; Zampieri et al., 2019), mostly in the context of online community moderation, has long been a subject of research; the”
HateCheck: Functional Tests for Hate Speech Detection Models (2020, background): “Counter speech has also been identified as a source of classification error in previous research (Warner and Hirschberg, 2012; van Aken et al., 2018; Vidgen et al., 2020a).”
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021, neutral): “(Warner and Hirschberg, 2012).”
Ex Machina: Personal Attacks Seen at Scale (2016, background): “In order to get reliable estimates of whether a comment is a personal attack, each comment was labeled by at least 10 different Crowdflower annotators.”
Automated Hate Speech Detection and the Problem of Offensive Language (2017, background): “…hate speech, for example sentences where a relevant noun and verb occur (e.g. kill and Jews ) (Gitari et al. 2015), the POS trigram “DT jewish NN” (Warner and Hirschberg 2012), and the syntactic structure I < intensity > < user intent > < hate target > , e.g. “I f*cking hate white people” (Silva…”
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter (2016, methodology): “Warner and Hirschberg (2012) perform a similar task of detecting hate speech using a support vector machine classifier, trained on word n-grams, brown clusters, and “the occurrence of words in a 10 word window” (Warner and Hirschberg, 2012).”
Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter (2016, background): “A number of studies on profanity and hate speech detection, have crowdsourced their annotations due to the resources required to annotate large data sets and the possibility of distributing the load onto the crowd (Warner and Hirschberg, 2012; Nobata et al., 2016).”
A Survey on Hate Speech Detection using Natural Language Processing (2017, methodology): “(Nobata et al., 2016; Djuric et al., 2015; Warner and Hirschberg, 2012), YouTube (Dinakar et al., 2012), ask.fm (Van Hee et al., 2015), Formspring (Dinakar et al., 2012), Usenet (Razavi et al., 2010), Whisper 12 (Silva et al., 2016), and Xanga 13 (Chau and Xu, 2007).”
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior (2018, methodology): “A widely used and available dataset in this ﬁeld, is the one in (Waseem and Hovy 2016), employed by several studies working on hate speech detection such as in (Badjatiya et al. 2017), (Gamb¨ack and Sikdar 2017), (Park and Fung 2017), and (Jha and Mamidi 2017).”
Hate Speech Dataset from a White Supremacy Forum (2018, neutral): “The results are in line with similar works (Nobata et al., 2016; Warner and Hirschberg, 2012).”
Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, background): “, 2018), related research includes the investigation of hate speech (Badjatiya et al., 2017; Burnap and Williams, 2016; Davidson et al., 2017; Gambäck and Sikdar, 2017; Njagi et al., 2015; Schmidt and Wiegand, 2017; Vigna et al., 2017; Warner and Hirschberg, 2012), online harassment (Yin and Davison”
Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis (2016, methodology): “For the purpose of building a classiﬁer, Warner and Hirschberg (2012) deﬁne hate speech as “abu-sive speech targeting speciﬁc group characteristics, such as ethnic origin, religion, gender, or sexual orientation”.”
Understanding Abuse: A Typology of Abusive Language Detection Subtasks (2017, methodology): “A number of studies on hate speech use part-of-speech sequences to model the expression of hatred (Warner and Hirschberg, 2012; Gitari et al., 2015; Davidson et al., 2017).”
Locate the Hate: Detecting Tweets against Blacks (2013, background): “Yet there is barely any research on detecting hate speech, with only one recent case on anti-Semitism (Warner and Hirschberg 2012).”
Analyzing the Targets of Hate in Online Social Media (2016, background): “Existing eﬀorts are focused on studying posts of known hate groups or radical forums [1, 2, 5, 7, 10].”
Revealing Persona Biases in Dialogue Systems (2021, neutral): “Offensiveness Offensiveness overlaps with concepts of abusive language (Nobata et al. 2016), toxicity (Dixon et al. 2018), hate speech (Warner and Hirschberg 2012), and conversational agent safety (Dinan et al. 2019).”
Detection of Abusive Language: the Problem of Biased Datasets (2019, background): “One major distinction that has been proposed in the literature is the division into explicitly and implicitly abusive language (Waseem et al., 2017).”
One-step and Two-step Classification for Abusive Language Detection on Twitter (2017, background): “Warner and Hirschberg (2012) and Burnap and Williams (2015) are one of the early researches to use machine learning based classifiers for detecting abusive language.”
All You Need is "Love": Evading Hate Speech Detection (2018, background): “assimilating similar words [26], and more recently word embeddings.”