Detection and Fine-Grained Classification of Cyberbullying Events

By Cynthia Van Hee, Els Lefever, Ben Verhoeven, Julie Mennes, Bart Desmet, Guy De Pauw, Walter Daelemans, Veronique Hoste. 2015

192 citations recorded by the source corpus.

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Citations in the corpus

Understanding Abuse: A Typology of Abusive Language Detection Subtasks (2017, background): “…language subtasks can be synthesized in a two-fold typology that con-Explicit Implicit D i r ec t e d “Go kill yourself”, “You’re a sad little f*ck” (Van Hee et al., 2015a) , “@User shut yo beaner ass up sp*c and hop your f*ggot ass back across the border little n*gga” (Davidson et al., 2017) ,…”
Studying Generalisability across Abusive Language Detection Datasets (2019, background): “For the most part, word-level n-grams have been highly predictive, with other linguistic features such as part-of-speech tags (Xu et al., 2012; Davidson et al., 2017) and sentiment score (Van Hee et al., 2015; Davidson et al., 2017) providing slight improvements.”
Social Chemistry 101: Learning to Reason about Social and Moral Norms (2020, background): “Our work adds to the growing literature concerned with distilling reactions to situations (Vu et al., 2014; Ding and Riloff, 2016) as well as so- 10We use the MediaBias/FactCheck ratings: https:// mediabiasfactcheck.com. cial and moral dynamics in language (Van Hee et al., 2015).”
A Dutch Dataset for Cross-lingual Multilabel Toxicity Detection (2021, methodology): “Compare Toxicity and Cyberbullying As a precursor to the main experiments, and to align the new annotation with Van Hee et al., we tested how cyberbullying acts as a naive predictor of toxicity using the combined labels for each class and F1 Score (Van Hee et al., 2015b; Emmery et al., 2019).”
Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media (2018, background): “Other work has lever-aged sentiment markers, speciﬁcally negative polarity and sentiment strength in preprocessing (Dinakar et al. 2012; Sood, Churchill, and Antin 2012; Gitari et al. 2015) and as features for hate speech classiﬁcation (Van Hee et al. 2015; Burnap et al. 2015).”
Benchmarking Aggression Identification in Social Media (2018, background): “, 2015) , cyberbullying (Dinakar et al., 2012; Nitta et al., 2013; Dadvar et al., 2013; Dadvar et al., 2014; Hee et al., 2015), flaming / insults (Sax, 2016; Nitin et al.”
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods (2019, methodology): “Van Hee et al. (2015) gathered 85 , 485 Dutch posts from ask.fm to form a dataset on cyber-bullying ( DATA - BULLY ; 6 .”
Aggression Identification Using Deep Learning and Data Augmentation (2018, background): “Besides this positive recent development of data sharing, fine-grained labeling becomes a topic of broader interest, for example in the work of Van Hee et al. (2015).”
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages (2020, background): “…and Hovy, 2016; Waseem, 2016), toxicity (Kolhatkar et al., 2019), hatefulness (Gao and Huang, 2017), aggression (Kumar et al., 2018), attack (Wulczyn et al., 2017), cyberbullying (Van Hee et al., 2015; Sprugnoli et al., 2018), misogyny (Fersini et al., 2018), obscenity, threats, and insults.”
Offensive Language and Hate Speech Detection for Danish (2019, background): “[9] focus on classifying cyberbullying events in Dutch.”
Detecting Online Hate Speech Using Context Aware Models (2017, methodology): “First, logistic regression models have been used in several prior hate speech detection studies Chen et al. (2012); Burnap and Williams (2014); Van Hee et al. (2015); Hosseinmardi et al. (2015); Burnap and Williams (2015); Waseem and Hovy (2016); Wulczyn et al. (2016); Nobata et al. (2016) and…”