Hate Me, Hate Me Not: Hate Speech Detection on Facebook

By Fabio Del Vigna, Andrea Cimino, Felice Dell'Orletta, Marinella Petrocchi, Maurizio Tesconi. 2017

449 citations recorded by the source corpus.

Subfields: Natural Language Processing, AI Ethics, Fairness & Society

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Citations in the corpus

Hate Speech Dataset from a White Supremacy Forum (2018, neutral): “Del Vigna et al. (2017) conduct a similar study for Italian.”
Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, background): “, 2018) or the use of (deep) neural networks (Ptaszynski et al., 2017; Pavlopoulos et al., 2017; Badjatiya et al., 2017; Vigna et al., 2017; Park and Fung, 2017; Gambäck and Sikdar, 2017).”
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection (2020, background): “Table 4 provides further details about the dataset collected. annotator agreement is 0.46 which is much higher than other hate speech datasets (Vigna et al. 2017; Ousidhoum et al. 2019).”
Resources and benchmark corpora for hate speech detection: a systematic review (2020, background): “…exclusive or non-exclusive values, accounting either for different shades of a given phenomenon, such as strong hate, weak hate, no hate (Del Vigna et al. 2017), overtly aggressive, covertly aggressive, not aggressive (Kumar et al. 2018a), hate speech, abusive but not hateful, non-offensive…”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, background): “…use supervised-approaches to tackle hate speech detection on SMPs including blogs (Warner and Hirschberg, 2012; Djuric et al., 2015; Gitari et al., 2015), Twitter (Xiang et al., 2012; Silva et al., 2016; Mathew et al., 2018a), Face-book (Del Vigna et al., 2017), and Instagram (Zhong et al., 2016).”
Benchmarking Aggression Identification in Social Media (2018, background): “…2012; Nobata et al., 2016; Waseem et al., 2017), hate speech (Pinkesh Badjatiya and Varma, 2017; Burnap and Williams, 2014; Davidson et al., 2017; Vigna et al., 2017; Djuric et al., 2015; Fortana, 2017; Gitari et al., 2015; Malmasi and Zampieri, 2018; Waseem and Hovy, 2016; Schmidt and Wiegand,…”
A Survey on Automatic Detection of Hate Speech in Text (2018, methodology): “…task were based in techniques such as Named Entity Recognition (NER) [14], Topic Extraction [52], Word Sense Disambiguation Techniques to check Polarity [31, 58], frequencies of personal pronouns in the first and second person, the presence of emoticons [16, 22] and capital letters [16].”
Spread of Hate Speech in Online Social Media (2018, background): “Facebook has been blamed by United Nations investigators in playing a leading role in the possible genocide of the Ro-hingya community in Myanmar by spreading hatespeech 3 .”
Effective hate-speech detection in Twitter data using recurrent neural networks (2018, background): “[22] LSTM 2 classes / 3 levels 1 Facebook (Hate,No-hate) (Strong,Weak,No-hate)”
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network (2018, methodology): “These methods can be divided into two categories: one relies on manual feature engineering that are then consumed by algorithms such as SVM, Naive Bayes, and Logistic Regression [4,7,9,16,24,26,25,27] (classic methods); the other represents the more recent deep learning paradigm that employs neural ”