Hate Speech Dataset from a White Supremacy Forum

By Ona de Gibert, Naiara Pérez, Aitor García-Pablos, Montse Cuadros. 2018

498 citations recorded by the source corpus.

Hate speech is commonly defined as any communication that disparages a target group of people based on some characteristic such as race, colour, ethnicity, gender, sexual orientation, nationality, religion, or other characteristic. Due to the massive rise of user-generated web content on social media, the amount of hate speech is also steadily increasing. Over the past years, interest in online hate speech detection and, particularly, the automation of this task has continuously grown, along with the societal impact of the phenomenon. This paper describes a hate speech dataset composed of thousands of sentences manually labelled as containing hate speech or not. The sentences have been extracted from Stormfront, a white supremacist forum. A custom annotation tool has been developed to carry out the manual labelling task which, among other things, allows the annotators to choose whether to read the context of a sentence before labelling it. The paper also provides a thoughtful qualitative and quantitative study of the resulting dataset and several baseline experiments with different classification models. The dataset is publicly available.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing, 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 Natural Language Processing

In Safety, Robustness & Interpretability

Citations in the corpus

Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech (2023, background): “Hate Speech Explanation Generation To hinder the spread of toxic speech among online social platforms, researchers developed effective ML models in recent years [2, 5].”
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection (2022, methodology): “To encourage implicit toxicity from a LLM, we ﬁnd examples of human-written sentences with implicit toxicity towards each group from hate forums (de Gibert et al., 2018) and Reddit (Breitfeller et al., 2019).”
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (2022, neutral): “…al., 2006; Giampiccolo et al., 2007; Bentivogli et al., 2009), superglue-cb (de Marneffe et al., 2019), sick (Marelli et al., 2014) , hate_speech18 (de Gibert et al., 2018), ethos-national_origin (Mollas et al., 2020), ethos-race (Mollas et al., 2020), ethos-religion (Mollas et al., 2020),…”
Refining Language Models with Compositional Explanations (2021, methodology): “4, we also conduct experiment on Stormfront → GHC by varying the number of explanations.”
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection (2020, background): “Several hate speech datasets (Ousidhoum et al. 2019; Qian et al. 2019b; de Gibert et al. 2018; Sanguinetti et al. 2018), models (Zhang, Robinson, and Tepper 2018; Mishra et al. 2018; Qian et al. 2018b,a), and shared tasks (Basile et al. 2019; Bosco et al. 2018) have been made available in the…”
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021, neutral): “A few neutrally-seeded datasets also exist (Bur-nap and Williams, 2014; de Gibert et al., 2018; Warner and Hirschberg, 2012).”
Resources and benchmark corpora for hate speech detection: a systematic review (2020, methodology): “…lexica, which is shown in the paper; this list is then automatically expanded and exploited for the automated detection of hateful tweets. de Gibert et al. (2018)(GPGC)—10,568 English sentences extracted from the right-wing forum Stormfront and manually annotated by three experts as ‘‘…”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, background): “Several hate speech datasets are publicly available, usually including a binary annotation, i.e. whether the content is hate-ful or not (Reynolds et al., 2011; Raﬁq et al., 2015; Hosseinmardi et al., 2015; de Gibert et al., 2018; ElSherief et al., 2018).”
Predicting Anti-Asian Hateful Users on Twitter during COVID-19 (2021, background): “On internet forum (de Gibert et al., 2018), Wikipedia (Wulczyn et al., 2017), News media comments (Coe et al., 2014), Twitter (Davidson 1The data and the corresponding code are available at https://github.com/anjisun221/ Anti-Asian-Slurs et al., 2017; Waseem and Hovy, 2016; Waseem, 2016; Founta et…”
ETHOS: a multi-label hate speech detection dataset (2020, methodology): “es potential receivers (generic), and again none, if there is no misogyny in the tweet. The largest online community of white nationalists, called Stormfront, was used as the source of the dataset in [11]. The content in this forum revolves around discussions of race, with various degrees of offensi”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “Otherwise, as [31] note, these issues are likely to drive type II errors in classification, i.e. labelling non-hate-speech utterances as hate speech.”