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NLP research has attained high performances in abusive language detection as a supervised classification task.
Tackling online abuse: A survey of automated abuse detection methods
Pushkar Mishra, Helen Yannakoudakis, and Ekaterina Shutova. 2019 · 1908
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Reinforced training data selection for domain adaptation
Miaofeng Liu, Yan Song, Hongbin Zou, and Tong Zhang. 2019 · 1968
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Latent Dirichlet allocation
David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003 · 2003
Earlier work this paper cites.
Directions in abusive language training data: Garbage in, garbage out
Bertie Vidgen and Leon Derczynski. 2020 · 2004
Earlier work this paper cites.
Online learning for latent Dirichlet allocation
Matthew Hoffman, Francis R. Bach, and David M. Blei. 2010 · 2010
Earlier work this paper cites.
Offensive language detection using multi-level classification
Amir H Razavi, Diana Inkpen, Sasha Uritsky, and Stan Matwin. 2010 · 2010
Earlier work this paper cites.
Software Framework for Topic Modelling with Large Corpora
Radim Řehůřek and Petr Sojka. 2010 · 2010
Earlier work this paper cites.
Analyzing labeled cyberbullying incidents on the Instagram social network
Homa Hosseinmardi, Sabrina Arredondo Mattson, Rahat Ibn Rafiq, Richard Han, Qin Lv, and Shivakant Mishra. 2015 · 2015
Earlier work this paper cites.
Exploring the space of topic coherence measures
Michael Röder, Andreas Both, and Alexander Hinneburg. 2015 · 2015
Earlier work this paper cites.
A survey of predictive modeling on imbalanced domains
Paula Branco, Luís Torgo, and Rita P. Ribeiro. 2016 · 2016
Earlier work this paper cites.
Abusive language detection in online user content
Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. 2016 · 2016
Cited alongside, same era.
Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter
Zeerak Waseem and Dirk Hovy. 2016 · 2016
Cited alongside, same era.
Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017 · 2017
Cited alongside, same era.
Learning from class-imbalanced data: Review of methods and applications
Guo Haixiang, Li Yijing, Jennifer Shang, Gu Mingyun, Huang Yuanyue, and Gong Bing. 2017 · 2017
Cited alongside, same era.
Deceiving Google’s Perspective API built for detecting toxic comments
Hossein Hosseini, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017 · 2017
Cited alongside, same era.
The Internet’s hidden rules: An empirical study of Reddit norm violations at micro, meso, and macro scales
Eshwar Chandrasekharan, Mattia Samory, Shagun Jhaver, Hunter Charvat, Amy Bruckman, Cliff Lampe, Jacob Eisenstein, and Eric Gilbert. 2018 · 2018
Later among the works it cites.
Large scale crowdsourcing and characterization of Twitter abusive behavior
Antigoni Maria Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. 2018 · 2018
Later among the works it cites.
Inducing a lexicon of abusive words – a feature-based approach
Michael Wiegand, Josef Ruppenhofer, Anna Schmidt, and Clayton Greenberg. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Survey on deep learning with class imbalance
Justin M. Johnson and Taghi M. Khoshgoftaar. 2019 · 2019
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Learning to select data for transfer learning with Bayesian optimization
Sebastian Ruder and Barbara Plank. 2017 · 2017
Cited alongside, same era.
A survey on hate speech detection using natural language processing
Anna Schmidt and Michael Wiegand. 2017 · 2017
Cited alongside, same era.
Understanding abuse: A typology of abusive language detection subtasks
Zeerak Waseem, Thomas Davidson, Dana Warmsley, and Ingmar Weber. 2017 · 2017
Cited alongside, same era.
Ex machina: Personal attacks seen at scale
Ellery Wulczyn, Nithum Thain, and Lucas Dixon. 2017 · 2017
Cited alongside, same era.
Challenges for toxic comment classification: An in-depth error analysis
Betty van Aken, Julian Risch, Ralf Krestel, and Alexander Löser. 2018 · 2018
Cited alongside, same era.
Challenges and frontiers in abusive content detection
Bertie Vidgen, Alex Harris, Dong Nguyen, Rebekah Tromble, Scott Hale, and Helen Margetts. 2019a
Cited in the paper.
How much online abuse is there?
Bertie Vidgen, Helen Margetts, and Alex Harris. 2019b
Cited in the paper.
A just and comprehensive strategy for using NLP to address online abuse
David Jurgens, Libby Hemphill, and Eshwar Chandrasekharan. 2019 · 2019
Later among the works it cites.
Detection of Abusive Language: the Problem of Biased Datasets
Michael Wiegand, Josef Ruppenhofer, and Thomas Kleinbauer. 2019 · 2019
Later among the works it cites.
Semeval-2019 Task 6: Identifying and categorizing offensive language in social media (OffensEval)
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019 · 2019
Later among the works it cites.
Toxic, hateful, offensive or abusive? what are we really classifying? an empirical analysis of hate speech datasets
Paula Fortuna, Juan Soler, and Leo Wanner. 2020 · 2020
Closest in time.
Predictive biases in natural language processing models: A conceptual framework and overview
Deven Santosh Shah, H. Andrew Schwartz, and Dirk Hovy. 2020 · 2020
Closest in time.