Improving Cyberbullying Detection with User Context

By Maral Dadvar, Dolf Trieschnigg, R. Ordelman, Franciska de Jong. 2013

396 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Social Psychology, Sociology and Political Science

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Citations in the corpus

Understanding Abuse: A Typology of Abusive Language Detection Subtasks (2017, background): “In the task of annotating documents that contain bullying, it appears that there is a common understanding of what cyberbullying entails: an intentionally harmful electronic attack by an individual or group against a victim, usually repetitive in nature (Dadvar et al., 2013).”
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) (2019, methodology): “(2012) used sentiment analysis and topic models to identify relevant topics, and Dadvar et al. (2013) used user-related features such as the”
Predicting the Type and Target of Offensive Posts in Social Media (2019, background): “, 2018), (cyber-)bullying (Xu et al., 2012; Dadvar et al., 2013), toxic comments, hate speech (Kwok and Wang, 2013; Djuric et al.”
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods (2019, background): “This is illustrated in the works of Dadvar et al. (2013) and Waseem and Hovy ( 2016): Dadvar et al. observed an inter-annotator agreement of 93% on their cyber-bullying dataset.”
Do Characters Abuse More Than Words? (2016, background): “…has been rather diffuse as researchers have focused on different aspects ranging from profanity detection (Sood et al., 2012) to hate speech detection (Warner and Hirschberg, 2012) to cyberbullying (Dadvar et al., 2013) and to abusive language in general (Chen et al., 2012; Djuric et al., 2015b).”
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective (2020, background): “For example, in research on cyberbullying, where abusive language is directed towards specific individuals, more consensus in task definition and annotation instructions can be found, and higher inter-annotator agreement rates are often observed (Dadvar et al., 2013).”
Challenges in discriminating profanity from hate speech (2017, background): “Dadvar et al. (2013) improve the detection of bullying by utilizing user features and the author’s frequency of profanity use in previous messages.”
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (2018, background): “Closely related to our work is research that leverages user attributes in the classiﬁcation process such as history of participation in hate speech and usage of profanity (Xiang et al., 2012; Dadvar et al., 2013).”
Cross-Domain Detection of Abusive Language Online (2018, background): “, 2017; Burnap and Williams, 2015), and support vector machines (SVM) (Xu et al., 2012; Dadvar et al., 2013; Schofield and Davidson, 2017).”
Towards Ethics by Design in Online Abusive Content Detection (2020, background): “For example, in research on cyberbullying, where abusive language is directed towards speciﬁc individuals, more consensus in task deﬁnition and annotation instructions can be found, and higher inter-annotator agreement rates are often observed (Dadvar et al., 2013).”
A Measurement Study of Hate Speech in Social Media (2017, neutral): “Aside from leveraging text based features researchers also explored other features like leveraging user history [8] or even community detection [6]; These techniques can be used in addition to the text based features.”
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2020, background): “Dadvar et al. (2013) and Saﬁ Samghabadi et al. (2020) studied utility of the conversational context.”
Hate Me, Hate Me Not: Hate Speech Detection on Facebook (2017, background): “Using machine learning, it was possible to recognize those users that adopt troll profiles in cyberbullying practices [4, 13, 18].”
Toward Multimodal Cyberbullying Detection (2017, background): “…have worked on the effects of cy-berbullying on teenagers [25, 50] and also tried to identify automated methods for cyberbullying detection [13, 12, 40], such approaches are yet to consider the dramatically changed social media landscape that the teenagers are dealing with now compared to even…”
Benchmarking Aggression Identification in Social Media (2018, neutral): “…trolling (Cambria et al., 2010; Kumar et al., 2014; Mojica, 2016; Mihaylov et al., 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., 2012), abusive / offensive language (Chen et…”
Developing an online hate classifier for multiple social media platforms (2020, result): “At least the following works have experimented with SVM for online hate detection, with satisfactory results: Xu et al. [83], Dadvar et al. [84], Nobata et al. [23], and Salminen et al. [5]. computational complexity of SVM is lower compared to deep learning models, and it provides more…”