Learning from Bullying Traces in Social Media

By Jun-Ming Xu, Kwang-Sung Jun, Xiaojin Zhu, Amy Bellmore. 2012

413 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “…like lexicons (such as hatebase) (Xiang et al., 2012; Dadvar et al., 2012; Burnap and Williams, 2015; Liu and Forss, 2015), n-grams, capitalization/punctuation details (Chen et al., 2012; Waseem and Hovy, 2016; Nobata et al., 2016), among others (Xu et al., 2012; Burnap and Williams, 2016).”
A Survey on Hate Speech Detection using Natural Language Processing (2017, methodology): “More recently, many authors have shifted to employing the term cyberbullying (Xu et al., 2012; Hosseinmardi et al., 2015; Zhong et al., 2016; Van Hee et al., 2015; Dadvar et al., 2013; Dinakar et al., 2012).”
SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) (2019, background): “Xu et al. (2012) use topic models to to identify relevant topics in bullying.”
Predicting the Type and Target of Offensive Posts in Social Media (2019, background): “One of the best offensive keywords was tweets that were ﬂagged as not being safe by the Twitter ‘safe’ ﬁlter (the ‘-’ indicates ‘not safe’).”
Detection of Cyberbullying Incidents on the Instagram Social Network (2015, background): “…(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 2011),(Nahar et al. 2012) are more accurately…”
Comparative Studies of Detecting Abusive Language on Twitter (2018, background): “Abusive language is often reframed as, but not limited to, of-fensive language (Razavi et al., 2010), cyberbul-lying (Xu et al., 2012), othering language (Bur-nap and Williams, 2014), and hate speech (Djuric et al., 2015).”
Detecting Hate Speech in Social Media (2017, background): “One example is the work by Xu et al. (2012) who apply sentiment analysis to detect bullying in tweets and use Latent Dirichlet Allocation (LDA) topic models (Blei et al., 2003) to identify relevant topics in these texts.”
Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network (2016, background): “Although this social networking site has become an important, near real-time communication channel (Kavanaugh et al., 2012), a study determined that Twitter is turning into a “cyberbullying playground” (Xu et al., 2012).”
Detecting Nastiness in Social Media (2017, methodology): “This is analogous to the method used in data collection by Xu et al. (2012); they limited their Twitter data to tweets containing the words bully, bullied, bullying .”
Cross-Domain Detection of Abusive Language Online (2018, methodology): “We then train the SVM with the optimal hyperparameters on the union of training and development sets of X , and then use the model to label the test set of Y , obtaining the ﬁnal score.”
Toward Multimodal Cyberbullying Detection (2017, background): “While the importance of understanding multimodal content for cyberbullying detection has been widely acknowledged, the cyberbullying detection literature is still primarily focused on (sophisticated) text processing [13, 12, 40, 53], and their accuracy remains limited.”
Challenges in discriminating profanity from hate speech (2017, methodology): “Xu et al. (2012) apply sentiment analysis to detect bullying roles in tweets.”
Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context (2019, methodology): “Our simplest model is a linear support vector machine (SVM) on TF-IDF weighted unigrams and bigrams.”
Predictive Analysis on Twitter: Techniques and Applications (2018, background): “Prior work [92] has modeled harassment on social media to identify the harassing content which was a binary classification approach.”
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2020, methodology): “For cyberbullying detection, Xu et al. (2012) used sentiment analysis and topic models to identify relevant topics.”
Automatic detection of cyberbullying in social media text (2018, methodology): “:( bullying not cool’ (Xu et al., 2012, p. 658) For this research, English and Dutch social media data were annotated for diﬀerent forms of cyberbullying, based on the actors involved in a cyberbullying incident.”