Detecting offensive tweets via topical feature discovery over a large scale twitter corpus

By Guang Xiang, Bin Fan, Ling Wang, Jason I. Hong, Carolyn Penstein Rosé. 2012

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Communication

In Information Systems

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): “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.”
Ex Machina: Personal Attacks Seen at Scale (2016, background): “This included removing annotations where the same worker labeled a comment as both an attack and not an attack and removing comments that most workers flagged as not being English.”
A Survey on Hate Speech Detection using Natural Language Processing (2017, methodology): “The data sources that are used include: Twitter (Xiang et al., 2012; Xu et al., 2012; Burnap et al., 2013; Burnap et al., 2014; Burnap and Williams, 2015; Silva et al., 2016), Instagram (Hosseinmardi et al., 2015; Zhong et al., 2016), Yahoo!”
Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018, background): “…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; Dinakar et al., 2012; Hee et al., 2015; Zhong et al., 2016) and offensive language (Chen et al., 2012; Xiang et al., 2012).”
Using Convolutional Neural Networks to Classify Hate-Speech (2017, methodology): “Xiang et al. (2012) created offensivelanguage topic clusters using Logistic Regression over a set of 860,071 tweets automatically annotated using a boot-strapping technique and supplemented with a dictionary of 339 offensive words.”
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer (2018, methodology): “Most previous work that address the problem of offensive language on social media has focused on text classification using different machine learning methods (Xiang et al., 2012; Warner and Hirschberg, 2012; Kwok and Wang, 2013; Wang et al., 2014; Burnap and Williams, 2015; Nobata et al., 2016; Davi”
Explaining Toxic Text via Knowledge Enhanced Text Generation (2022, methodology): “Early approaches include using n-grams (Waseem and Hovy, 2016; Sood et al., 2012; Perera and Fer-nando, 2021) as well as word clustering (Xiang et al., 2012; Zhong et al., 2016).”
Cyber Hate Classification: 'Othering' Language And Paragraph Embedding (2018, background): “Othering is an established construct in rhetorical narrative surrounding hate speech [25], and the ’we-they’ dichotomy has always been identified in racist discourse [48].”
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).”
Distantly Supervised Lifelong Learning for Large-Scale Social Media Sentiment Analysis (2017, methodology): “[19] build the sentiment classifier by introducing rich microblog features in SVM, and then proposed a semi-supervised learning framework combining LDA and sentiment classification.”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, background): “We ﬁrst collected a small sample of nat-ural/real hate speech from Twitter using relevant keywords (such as “stop Islam”) and manually selected those that were effectively hate speeches.”
A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content (2018, methodology): “, 2016) and linear regressions models (Davidson et al., 2017; Xiang et al., 2012) have also been used to effectively detect abusive online language.”
Comparative Studies of Detecting Abusive Language on Twitter (2018, methodology): “:1808.10245v1 [cs.CL] 30 Aug 2018 et al. (2010) applied Na¨ıve Bayes, and Warner and Hirschberg (2012) used Support Vector Machine (SVM), both with word-level features to classify offensive language. Xiang et al. (2012) generated topic distributions with Latent Dirichlet Allocation (Blei et al.,2003”
Do Characters Abuse More Than Words? (2016, methodology): “Hand-crafted regular expressions and blacklists also feature prominently in (Yin et al., 2009; Sood et al., 2012; Xiang et al., 2012).”
Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020, background): “…(Waseem and Hovy, 2016; Waseem, 2016; Ross et al., 2017), Facebook (Kumar et al., 2018), WhatsApp (Sprug-noli et al., 2018), and forums (de Gibert et al., 2018), in order to perform hate speech classiﬁcation (Xiang et al., 2012; Silva et al., 2016; Del Vi-gna et al., 2017; Mathew et al., 2018).”
Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network (2018, methodology): “) in messages as their presence can be predictive features [4,11,17,27].”