Improved cyberbullying detection using gender information

By Maral Dadvar, Franciska de Jong, R. Ordelman, Dolf Trieschnigg. 2012

205 citations recorded by the source corpus.

Subfields: Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “Application Level Interventions Feature-based Detection Toxicity Lexical features (Xiang et al., 2012; Dadvar et al., 2012; Burnap and Williams, 2015; Liu and Forss, 2015); n-gram features (Chen et al.”
A Survey on Hate Speech Detection using Natural Language Processing (2017, background): “Knowing the gender of the user may also help (Dadvar et al., 2012; Waseem and Hovy, 2016).”
Detection of Cyberbullying Incidents on the Instagram Social Network (2015, background): “Prior works that investigated cyberbullying (Ptaszynski et al. 2010),(Dadvar et al. 2012),(K. Reynolds and Edwards 2011),(Dinakar et al. 2012),(H. Sanchez 2012),(Kontostathis et al. 2013), (Xu et al. 2012),(Nahar et al. 2014),(Na-har, Li, and Pang 2013),(Dinakar, Reichart, and Lieberman…”
Improving Cyberbullying Detection with User Context (2013, background): “In this study we show that taking user context, such as a user’s comments history and user characteristics [4], into account can improve the performance of detection tools for cyberbullying incidents considerably.”
Computational Sociolinguistics: A Survey (2016, background): “Dadvar et al. (2012) trained genderspecific classifiers to detect instances of cyberbullying, noticing that language used by harassers varies by gender.”
Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment (2014, background): “, 2013) and cyberbullying detection (Dadvar et al., 2012).”
Explaining Toxic Text via Knowledge Enhanced Text Generation (2022, methodology): “The use of meta-information, such as information about the user (Dadvar et al., 2012), has proven to be useful, depending on the type of information used.”
Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network (2016, methodology): “Moreover, a study determined that accurate cyberbullying detection in online social networks was improved by using gender-related information as a feature (Dadvar et al., 2012).”
A Survey on Automatic Detection of Hate Speech in Text (2018, methodology): “…(2002) [64] Classifying racist texts using a support vector machine (2004) [33] Improved Cyberbullying Detection Using Gender Information (2012) [16] Detecting Hate Speech on the World Wide Web (2012) [74] The found documents analyse datasets with messages that were collected from social…”