Directions in Abusive Language Training Data: Garbage In, Garbage Out

Data-driven analysis and detection of abusive online content covers many different tasks, phenomena, contexts, and methodologies. This paper systematically reviews abusive language dataset creation and content in conjunction with an open website for cataloguing abusive language data. This collection of knowledge leads to a synthesis providing evidence-based recommendations for practitioners working with this complex and highly diverse data.

[1]  Lucas Dixon,et al.  Ex Machina: Personal Attacks Seen at Scale , 2016, WWW.

[2]  Kalina Bontcheva,et al.  Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines , 2014, LREC.

[3]  Ika Alfina,et al.  Hate speech detection in the Indonesian language: A dataset and preliminary study , 2017, 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[4]  Tomaž Erjavec,et al.  Datasets of Slovene and Croatian Moderated News Comments , 2018, ALW.

[5]  Kalina Bontcheva,et al.  Broad Twitter Corpus: A Diverse Named Entity Recognition Resource , 2016, COLING.

[6]  Mehmet Fatih Çömlekçi.  Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions that Shape Social Media , 2019 .

[7]  Ankur Taly,et al.  Counterfactual Fairness in Text Classification through Robustness , 2018, AIES.

[8]  Yarin Gal,et al.  BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning , 2019, NeurIPS.

[9]  Sylvie Delacroix,et al.  Bottom-Up Data Trusts: Disturbing the ‘One Size Fits All’ Approach to Data Governance , 2018, International Data Privacy Law.

[10]  Alexander van Deursen,et al.  The digital divide shifts to differences in usage , 2014, New Media Soc..

[11]  L. Lachenicht.  Aggravating language a study of abusive and insulting language , 1980 .

[12]  Ingmar Weber,et al.  Racial Bias in Hate Speech and Abusive Language Detection Datasets , 2019, Proceedings of the Third Workshop on Abusive Language Online.

[13]  Ritesh Kumar,et al.  Benchmarking Aggression Identification in Social Media , 2018, TRAC@COLING 2018.

[14]  Shervin Malmasi,et al.  Challenges in discriminating profanity from hate speech , 2017, J. Exp. Theor. Artif. Intell..

[15]  Zeerak Waseem,et al.  Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter , 2016, NLP+CSS@EMNLP.

[16]  Jill P Mesirov,et al.  Accessible Reproducible Research , 2010, Science.

[17]  Cody Buntain,et al.  A Large Labeled Corpus for Online Harassment Research , 2017, WebSci.

[18]  Amit P. Sheth,et al.  A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research , 2018, WebSci.

[19]  Indra Budi,et al.  A Dataset and Preliminaries Study for Abusive Language Detection in Indonesian Social Media , 2018 .

[20]  Jonathan Mellon,et al.  Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users , 2017 .

[21]  Vinay Singh,et al.  A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection , 2018, PEOPLES@NAACL-HTL.

[22]  John Pavlopoulos,et al.  Deeper Attention to Abusive User Content Moderation , 2017, EMNLP.

[23]  Michael Veale,et al.  Like Trainer, Like Bot? Inheritance of Bias in Algorithmic Content Moderation , 2017, SocInfo.

[24]  Gianluca Stringhini,et al.  Screenshot Classifier annotated images pHashes of non-screenshot annotated images Know Your Meme Generic Annotation Sites Meme Annotation Sites Generic Web Communities , 2018 .

[25]  Scott A. Hale,et al.  Political Turbulence: How Social Media Shape Collective Action , 2015 .

[26]  Wendy Hall,et al.  Growing the artificial intelligence industry in the UK , 2017 .

[27]  N. Strossen.  HATE: Why We Should Resist it With Free Speech, Not Censorship , 2018 .

[28]  M. Williams,et al.  Hatred behind the screens: A report on the rise of online hate speech , 2019 .

[29]  Kalina Bontcheva,et al.  The GATE Crowdsourcing Plugin: Crowdsourcing Annotated Corpora Made Easy , 2014, EACL.

[30]  Ingmar Weber,et al.  Automated Hate Speech Detection and the Problem of Offensive Language , 2017, ICWSM.

[31]  Virgílio A. F. Almeida,et al.  Characterizing and Detecting Hateful Users on Twitter , 2018, ICWSM.

[32]  Gianluca Stringhini,et al.  Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web , 2016, ICWSM.

[33]  Richard Wilson,et al.  HATE: Why We Should Resist it with Free Speech, Not Censorship by Nadine Strossen (review) , 2019, Human Rights Quarterly.

[34]  Cristina Bosco,et al.  An Impossible Dialogue! Nominal Utterances and Populist Rhetoric in an Italian Twitter Corpus of Hate Speech against Immigrants , 2018, LREC.

[35]  Hugo Jair Escalante,et al.  Overview of MEX-A3T at IberLEF 2019: Authorship and Aggressiveness Analysis in Mexican Spanish Tweets , 2018, IberLEF@SEPLN.

[36]  Justin Reich,et al.  Privacy, anonymity, and big data in the social sciences , 2014, Commun. ACM.

[37]  Pete Burnap,et al.  Us and them: identifying cyber hate on Twitter across multiple protected characteristics , 2016, EPJ Data Science.

[38]  Adam Tauman Kalai,et al.  Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings , 2016, NIPS.

[39]  Ingmar Weber,et al.  Understanding Abuse: A Typology of Abusive Language Detection Subtasks , 2017, ALW@ACL.

[40]  David Jurgens,et al.  A Just and Comprehensive Strategy for Using NLP to Address Online Abuse , 2019, ACL.

[41]  Joel R. Tetreault,et al.  Abusive Language Detection in Online User Content , 2016, WWW.

[42]  Franz J. Király,et al.  Design choices for productive, secure, data-intensive research at scale in the cloud , 2019, ArXiv.

[43]  Justin Grimmer,et al.  Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.

[44]  Michael Wiegand,et al.  Detection of Abusive Language: the Problem of Biased Datasets , 2019, NAACL.

[45]  Ralf Peters,et al.  Detecting Cyberbullying in Online Communities , 2016, ECIS.

[46]  Rahul Goel,et al.  Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision , 2018, ArXiv.

[47]  Nabiha Aziz.  Dog Whistles and Discriminatory Intent: Proving Intent Through Campaign Speech in Voting Rights Litigation , 2019 .

[48]  Rogers Prates de Pelle,et al.  Offensive Comments in the Brazilian Web: a dataset and baseline results , 2017 .

[49]  Preslav Nakov,et al.  SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval) , 2019, *SEMEVAL.

[50]  Leon Derczynski,et al.  Offensive Language and Hate Speech Detection for Danish , 2019, LREC.

[51]  Gianluca Stringhini,et al.  Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior , 2018, ICWSM.

[52]  Gianluca Stringhini,et al.  What is Gab: A Bastion of Free Speech or an Alt-Right Echo Chamber , 2018, WWW.

[53]  Michael C. Frank,et al.  Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal Cognition , 2018, Royal Society Open Science.

[54]  Stan Matwin,et al.  Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs , 2018, ALW.

[55]  Ona de Gibert,et al.  Hate Speech Dataset from a White Supremacy Forum , 2018, ALW.

[56]  Nikola S. Nikolov,et al.  Dataset Construction for the Detection of Anti-Social Behaviour in Online Communication in Arabic , 2018, ACLING.

[57]  K. Bretonnel Cohen,et al.  Last Words: Amazon Mechanical Turk: Gold Mine or Coal Mine? , 2011, CL.

[58]  Gabriela Ferraro,et al.  Transfer learning for hate speech detection in social media , 2019, Journal of Computational Social Science.

[59]  Sérgio Nunes,et al.  A Survey on Automatic Detection of Hate Speech in Text , 2018, ACM Comput. Surv..

[60]  Bernard J. Jansen,et al.  Developing an online hate classifier for multiple social media platforms , 2020, Human-centric Computing and Information Sciences.

[61]  Hatem Haddad,et al.  L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language , 2019, Proceedings of the Third Workshop on Abusive Language Online.

[62]  Emily M. Bender,et al.  Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science , 2018, TACL.

[63]  James Pustejovsky,et al.  Natural Language Annotation for Machine Learning - a Guide to Corpus-Building for Applications , 2012 .

[64]  Lei Gao,et al.  Detecting Online Hate Speech Using Context Aware Models , 2017, RANLP.

[65]  Xiaochang Peng,et al.  Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification , 2019, Proceedings of the Third Workshop on Abusive Language Online.

[66]  James Goulding,et al.  Psychology of personal data donation , 2019, PloS one.

[67]  Media Sport,et al.  Online harms white paper , 2019 .

[68]  Radhika Mamidi,et al.  When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data , 2017, NLP+CSS@ACL.

[69]  Ralf Peters,et al.  Detecting Offensive Statements towards Foreigners in Social Media , 2017, HICSS.

[70]  Indra Budi,et al.  Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter , 2019, Proceedings of the Third Workshop on Abusive Language Online.

[71]  I. Shapiro.  Problems, Methods, and Theories in the Study of Politics, or What's Wrong with Political Science and What to Do About it , 2002 .

[72]  Sara Tonelli,et al.  Creating a WhatsApp Dataset to Study Pre-teen Cyberbullying , 2018, ALW.

[73]  Naganna Chetty,et al.  Hate speech review in the context of online social networks , 2018 .

[74]  David Reitter,et al.  Crowdsourcing the Measurement of Interstate Conflict , 2016, PloS one.

[75]  Shivakant Mishra,et al.  International Conference on Advances in Social Networks Analysis and Mining ( ASONAM ) Are They Our Brothers ? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere , 2018 .

[76]  D. Paulhus,et al.  Trolls just want to have fun , 2014 .

[77]  Scott A. Hale,et al.  Challenges and frontiers in abusive content detection , 2019, Proceedings of the Third Workshop on Abusive Language Online.

[78]  Michael Wiegand,et al.  Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language , 2018 .

[79]  Dirk Hovy,et al.  Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter , 2016, NAACL.

[80]  Jing Qian,et al.  A Benchmark Dataset for Learning to Intervene in Online Hate Speech , 2019, EMNLP.

[81]  M. Montgomery,et al.  Post-truth politics?: Authenticity, populism and the electoral discourses of Donald Trump , 2017 .

[82]  Ramit Sawhney,et al.  Did you offend me? Classification of Offensive Tweets in Hinglish Language , 2018, ALW.

[83]  M. Taddeo.  Data philanthropy and the design of the infraethics for information societies , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[84]  Grant Blank.  The Digital Divide Among Twitter Users and Its Implications for Social Research , 2017 .

[85]  Walid Magdy,et al.  Abusive Language Detection on Arabic Social Media , 2017, ALW@ACL.

[86]  M. Williams,et al.  Cyber-hate on social media in the aftermath of Woolwich , 2015 .

[87]  Diana Maynard,et al.  Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. , 2014, LREC.

[88]  S. Fiske,et al.  Hostile and Benevolent Sexism , 1997 .

[89]  Julia Hirschberg,et al.  Detecting Hate Speech on the World Wide Web , 2012 .

[90]  Lluis Gomez,et al.  Exploring Hate Speech Detection in Multimodal Publications , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[91]  Paolo Rosso,et al.  Overview of the Task on Automatic Misogyny Identification at IberEval 2018 , 2018, IberEval@SEPLN.

[92]  Subbarao Kambhampati,et al.  Dude, srsly?: The Surprisingly Formal Nature of Twitter's Language , 2013, ICWSM.

[93]  Michael Wiegand,et al.  A Survey on Hate Speech Detection using Natural Language Processing , 2017, SocialNLP@EACL.

[94]  Benjamin E. Lauderdale,et al.  Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data , 2016, American Political Science Review.

[95]  Miles Osborne,et al.  I Wish I Didn't Say That! Analyzing and Predicting Deleted Messages in Twitter , 2013, ArXiv.

[96]  Felice Dell'Orletta,et al.  Overview of the EVALITA 2018 Hate Speech Detection Task , 2018, EVALITA@CLiC-it.

[97]  Taha Yasseri,et al.  A Biased Review of Biases in Twitter Studies on Political Collective Action , 2016, Front. Phys..

[98]  A. Kenny.  Freewill and Responsibility (Routledge Revivals) , 2011 .

[99]  Animesh Mukherjee,et al.  Spread of Hate Speech in Online Social Media , 2018, WebSci.

[100]  Sérgio Nunes,et al.  A Hierarchically-Labeled Portuguese Hate Speech Dataset , 2019, Proceedings of the Third Workshop on Abusive Language Online.

[101]  Bernard J. Jansen,et al.  Online Hate Interpretation Varies by Country, But More by Individual: A Statistical Analysis Using Crowdsourced Ratings , 2018, 2018 Fifth International Conference on Social Networks Analysis, Management and Security (SNAMS).

[102]  Siân Brooke,et al.  "There are no girls on the Internet": Gender performances in Advice Animal memes , 2019, First Monday.

[103]  Manish Shrivastava,et al.  Degree based Classification of Harmful Speech using Twitter Data , 2018, TRAC@COLING 2018.

[104]  Marco Guerini,et al.  CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech , 2019, ACL.

[105]  John P. A. Ioannidis,et al.  A manifesto for reproducible science , 2017, Nature Human Behaviour.

[106]  Matthew K. O. Lee,et al.  Online social networks: Why do students use facebook? , 2011, Comput. Hum. Behav..

[107]  Paolo Rosso,et al.  SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter , 2019, *SEMEVAL.

[108]  M. Williams,et al.  Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation , 2017, Sociology.

[109]  Björn Ross,et al.  Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis , 2016, ArXiv.

[110]  Gianluca Stringhini,et al.  Mean Birds: Detecting Aggression and Bullying on Twitter , 2017, WebSci.

[111]  Raphaël Troncy,et al.  Analysis of named entity recognition and linking for tweets , 2014, Inf. Process. Manag..