Detecting Hate Speech in Social Media

By Shervin Malmasi, Marcos Zampieri. 2017

346 citations recorded by the source corpus.

In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78% accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.

Subfields: Artificial Intelligence, Communication, Law, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, background): “Several text-only hate speech datasets have been released, mostly based on Twitter [83, 82, 11, 24, 20], and various architectures have been proposed for classifiers [45, 54, 53].”
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior (2018, methodology): “Another dataset that has already used in different studies (e.g., (Malmasi and Zampieri 2017), (Olteanu, Talamadupula, and Varshney 2017)) is the one presented in (Davidson et al. 2017).”
Hate Speech Dataset from a White Supremacy Forum (2018, background): “Other proposals focus on the annotation of hate speech as opposed to texts containing derogatory or offensive language (Davidson et al., 2017; Malmasi and Zampieri, 2017, 2018; Watanabe et al., 2018).”
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) (2019, background): “Examples of offensive content investigated in previous studies include hate speech (Davidson et al., 2017; Malmasi and Zampieri, 2017, 2018), cyber-bulling (Dinakar et al., 2011), and aggression (Kumar et al., 2018).”
Predicting the Type and Target of Offensive Posts in Social Media (2019, background): “…(Kumar et al., 2018), (cyber-)bullying (Xu et al., 2012; Dadvar et al., 2013), toxic comments , hate speech (Kwok and Wang, 2013; Djuric et al., 2015; Burnap and Williams, 2015; Davidson et al., 2017; Malmasi and Zampieri, 2017, 2018), and offensive language (Wiegand et al., 2018).”
Exploring Hate Speech Detection in Multimodal Publications (2019, background): “Malmasi and Zampieri [15] worked on Davidson’s dataset improving his results using more elaborated features.”
Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling (2018, methodology): “With the feature engineering approach, extracted features serve as input to classic machine learning algorithms such as naive bayes, logistic regression, support vector machines, and random forest (Schmidt and Wiegand, 2017; Malmasi and Zampieri, 2017).”
Benchmarking Aggression Identification in Social Media (2018, methodology): “As such the use (semi-) automatic methods to identify such behaviour has become important and has attracted more attention from the research community in recent years (Davidson et al., 2017; Malmasi and Zampieri, 2017).”
Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation (2019, background): “It appears in media such as newspapers or TV news, but one of the main focus of hate speech with very diverse targets has appeared in social networks [12, 8, 10].”