Detection of Harassment on Web 2.0

By Dawei Yin, Zhenzhen Xue, Liangjie Hong, Brian D. Davison, Lynne Edwards. 2009

455 citations recorded by the source corpus.

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Citations in the corpus

Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, background): “…and Sikdar, 2017; Njagi et al., 2015; Schmidt and Wiegand, 2017; Vigna et al., 2017; Warner and Hirschberg, 2012), online harassment (Yin and Davison, 2009; Golbeck et al., 2017), abusive language (Mehdad and Tetreault, 2016; Park and Fung, 2017), cyberbullying (Dad-var et al., 2013;…”
Using Convolutional Neural Networks to Classify Hate-Speech (2017, background): “This is of particular importance for swift moderation of online chat rooms, as addressed by, e.g., Yin et al. (2009) and Papegnies et al. (2017), with the latter suggesting several types of features (at the morphological, syntactic and user behaviour levels) that can be used for identifying when…”
Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision (2018, unknown): “One of the earliest works (Yin et al., 2009) used hand engineered features and supervised learning to detect abusive language.”
Aggression Identification Using Deep Learning and Data Augmentation (2018, background): “For a slightly different task, harassment detection in the web, Yin et al. (2009) introduce contextual features, which consider a user’s previous and succeeding posts as a context.”
Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment (2017, background): “There is a large and growing literature on the automatic detection of online harassment (Yin et al. 2009; Chen et al. 2012).”
Neural Character-based Composition Models for Abuse Detection (2018, methodology): “m two different domains, namely Twitter and Wikipedia talk page. To the best of our knowledge, this is the ﬁrst work to use character-based word composition models for abuse detection. 2 Related Work Yin et al. (2009) were among the ﬁrst ones to apply supervised learning to the task of abuse detecti”
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content (2018, background): “Also, morphological, syntactic and user behavior level features have been shown to be useful in learning abusive behavior (Papegnies et al., 2017; Buckels et al., 2014; Yin et al., 2009; Chen et al., 2012).”
Abusive Language Detection in Online User Content (2016, background): “A summary of the guidelines and examples for each category is shown in Table 4.”
How Community Feedback Shapes User Behavior (2014, background): “…a growing concern (Heymann, Koutrika, and Garcia-Molina 2007), as emphasized by work on review spamming (Lim et al. 2010; Mukherjee, Liu, and Glance 2012; Ott, Cardie, and Hancock 2012), trolling (Shachaf and Hara 2010), social deviance (Shores et al. 2014) and online harassment (Yin et al. 2009).”
Tackling Online Abuse: A Survey of Automated Abuse Detection Methods (2019, methodology): “n the test set. Spertus noted some limitations of adopting a heuristic rule-based approach, e.g., the inability to deal with sarcasm, and vulnerability to errors in spelling, punctuation and grammar. Yin et al. (2009) developed a method for detecting online harassment. Working with the three DATA-HA”
Conversations Gone Awry: Detecting Early Signs of Conversational Failure (2018, background): “Crucially, given our focus on conversations that start seemingly civil, we do not expect overtly hostile language—such as insults (Yin et al., 2009)—to be informative.”
Detection and Fine-Grained Classification of Cyberbullying Events (2015, methodology): “By contrast, the number of studies that focus on the annotation and automatic detection of cyberbullying is limited.”
Cyberbullying Via Social Media (2015, methodology): “These modifiers are also consistent with tools in computer science that use personal pronouns and second person pronouns as indicators of cyberbullying (Bayzick et al., 2011; Yin et al., 2009).”
Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms (2018, background): “However, automatic detection of cyberbullying was addressed only since 2009 [19].”
Detecting Offensive Language in Social Media to Protect Adolescent Online Safety (2012, methodology): “F-score [13] represents the weighted harmonic mean of precision and recall, which is defined as:”
“Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses (2021, background): “Offensive Language Detection Ad hominems occur in many forms and are related to different types of offensive language, including abusive language (Yin et al., 2009; Chen et al., 2012; Nobata et al., 2016), hate speech (Warner and Hirschberg, 2012; Kwok and Wang, 2013; Djuric et al., 2015),…”