A Lexicon-based Approach for Hate Speech Detection

By Njagi Dennis Gitari, Zuping Zhang, Hanyurwimfura Damien, Jun Long. 2015

474 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

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”
Understanding Abuse: A Typology of Abusive Language Detection Subtasks (2017, background): “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).”
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].”
All You Need is "Love": Evading Hate Speech Detection (2018, methodology): “9 Sentiment analysis can be incorporated to the process either as a prior filter [9], or as a feature used directly for hate speech classification.”
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods (2019, methodology): “• First to propose a lexicon-based method for abuse detection whereby they constructed an insulting and abusing language dictionary of words and phrases, with each entry having an associated weight indicating its abusive impact; Njagi et al. (2015) also adopted such a lexicon-based approach later on”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, background): “…often 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…”
Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection (2018, unknown): “[15] extracted sentences from some major ‘‘hate sites’’ in United States.”
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network (2018, background): “) in messages as their presence can be predictive features [4,11,17,27].”
Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020, methodology): “…(Warner and Hirschberg, 2012; Silva et al., 2016; Schmidt and Wiegand, 2017; Fortuna and Nunes, 2018) employing features such as lexical resources (Gitari et al., 2015; Burnap and Williams, 2016), sentiment polarity (Burnap and Williams, 2015) and multi-modal information (Hosseinmardi et al.,…”
A Survey on Automatic Detection of Hate Speech in Text (2018, methodology): “Bearing in mind that hate speech has a negative polarity, authors have been computing the sentiment as a feature for hate speech detection [3, 19, 22, 31, 52, 53].”
Detecting the Hate Code on Social Media (2017, unknown): “( Gitari et al. 2015).”
A Measurement Study of Hate Speech in Social Media (2017, background): “Multiple researchers try to solve this problem using manual inspection or a mix of crowdsource labeling and machine learning techniques [1, 2, 12, 26, 29, 31].”
Joint Modelling of Emotion and Abusive Language Detection (2020, background): “Early approaches experimented with lexicon-based features (Gitari et al., 2015), bagof-words (BOW) or n-gram features (Sood et al., 2012; Dinakar et al., 2011), and user-specific features, such as age (Dadvar et al., 2013) and gender (Waseem and Hovy, 2016).”
Benchmarking Aggression Identification in Social Media (2018, background): “…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 Wie-gand, 2017), radicalization (Agarwal and Sureka, 2015;…”
Towards Ethics by Design in Online Abusive Content Detection (2020, background): “Several lexicons of abusive expressions have been built manually, automatically, and semi-automatically (Razavi et al., 2010; Gitari et al., 2015; Wiegand et al., 2018).”
FuzzE: Fuzzy Fairness Evaluation of Offensive Language Classifiers on African-American English (2020, background): “Likewise, offensive language and hate speech lexicons have been curated to facilitate offensive language detection (Gitari et al., 2015; Davidson et al., 2017; Wiegand et al., 2018).”
Automated Hate Speech Detection and the Problem of Offensive Language (2017, background): “…to better identify the targets and intensity of 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…”