Confrontational and offensive behavior are pervasive in social media. Online communities, social media platforms, and tech companies are well aware of the problem and they have been investigating ways to cope with offensive language in social media. This has yielded an increased interest in the NLP community in automatically identifying offenses, aggression, and hate speech in user-generated content (Fortuna and Nunes, 2018; Nakov et al., 2021).
The interest of the community on this topic is evidenced by several recent studies on English (Yao et al. 2019; Ridenhour et al., 2020) and on many other languages like Danish (Sigurbergsson and Derczynski, 2020) and Greek (Pitenis et al., 2020). Multilingual models have also been explored to cope with data scarcity for some languages (Corazza et al., 2020; Ranasinghe and Zampieri, 2020, Nozza, 2021). Finally, well-attended competitions have been organized on the topic at SemEval (e.g. HatEval, OffensEval, and Toxic Spans Detection) and other venues such as TRAC and HASOC. These competitions provided participants with widely used benchmark datasets such as OLID (Zampieri et al., 2019) and SOLID (Rosenthal et al., 2021).
Motivated by the interest of the community in the problem of offensive content online, we are editing a special issue of the Cambridge University Press Journal Natural Language Engineering on this topic to be published in 2022.
We welcome papers dealing with one or more of the following topics:
NLP models for detecting impolite and offensive content online (e.g. hate speech, cyberbulling, aggression, etc.)
Application of NLP tools to analyze social media content and other large data sets for offensive language detection
NLP models for cross-lingual offensive language identification
Computational models for multi-modal abuse detection
Development of corpora and annotation guidelines and taxonomies for offensive language identification
Human-Computer Interaction (HCI) for abusive language detection systems
Deadline for Submissions: August 31, 2021
First decision: Starting November 30, 2021
Revised Version Submissions: March 1, 2022
Final Decisions: May 30, 2022
Submissions should be formatted according to the NLE guidelines available here and submitted through the manuscript submission system.
To have your manuscript considered for this special issue, when uploading your manuscript to the system you should choose NLP Approaches to Offensive Content Online in the field Special Issue Designation.
Isabelle Augenstein - University of Copenhagen, Denmark and CheckStep Ltd, UK
Siddharth Krishnan - University of North Carolina at Charlotte, USA
Joshua Melton - University of North Carolina at Charlotte, USA
Preslav Nakov - Qatar Computing Research Institute, Qatar
Marcos Zampieri - Rochester Institute of Technology, USA
Pepa Atanasova - University of Copenhagen, Denmark
Arunkumar Bagavathi - Oklahoma State University
Valerio Basile - University of Turin, Italy
Cristina Bosco - University of Turin, Italy
Tommaso Caselli - University of Groningen, The Netherlands
Bharathi Raja Chakravarthi - National University of Ireland, Galway, Ireland
Charalampos Chelmis - State University of New York at Albany, USA
Çağrı Çöltekin - University of Tübingen, Germany
Thomas Davidson - Rutgers University, USA
David Jurgens - University of Michigan, USA
Darja Fišer - University of Ljubljana, Slovenia
Paula Fortuna - Pompeu Fabra University, Spain
Ashique Khudabukhsh - Rochester Institute of Technology, USA
Ritesh Kumar - Dr. Bhimrao Ambedkar University, India
Nikola Ljubešić - Jožef Stefan Institute, Slovenia
Diana Maynard - University of Sheffield, UK
Jelena Mitrović - University of Passau, Germany
Tharindu Ranasinghe - University of Wolverhampton, UK
Paolo Rosso - Polytechnic University of Valencia, Spain
Indira Sen - GESIS, Germany
Bertie Vidgen - Turing Institute, UK
Seid Muhie Yimam - University of Hamburg, Germany
Arkaitz Zubiaga - Queen Mary University of London, UK
Michelle Corazza, Stefano Menini, Elena Cabrio, Sara Tonelli and Serena Villata. 2020. A multilingual Evaluation for Online Hate Speech Detection. ACM Transactions on Internet Technology (TOIT), 20(2), pp.1-22.
Paula Fortuna and Sergio Nunes. 2018. A Survey on Automatic Detection of Hate Speech in Text. ACM Computing Surveys (CSUR), 51(4):1–30.
Preslav Nakov, Vibha Nayak, Kyle Dent, Ameya Bhatawdekar, Sheikh Muhammad Sarwar, Momchil Hardalov, Yoan Dinkov, Dimitrina Zlatkova, Guillaume Bouchard, Isabelle Augenstein. 2021. Detecting Abusive Language on Online Platforms: A Critical Analysis. arXiv preprint arXiv:2103.00153.
Debora Nozza. 2021. Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection. Proceedings of ACL.
Zeses Pitenis, Marcos Zampieri, and Tharindu Ranasinghe. 2020. Offensive Language Identification in Greek. In Proceedings of LREC.
Tharindu Ranasinghe and Marcos Zampieri. 2020. Multilingual Offensive Language Identification with Cross-lingual Embeddings. In Proceedings of EMNLP.
Michael Ridenhour, Arunkumar Bagavathi, Elaheh Raisi, and Siddharth Krishnan. 2020. Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models. Proceedings of SBP-BRiMS.
Sara Rosenthal, Pepa Atanasova, Georgi Karadzhov, Marcos Zampieri, Preslav Nakov. 2021. SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification. Findings of the ACL.
Gudbjartur Ingi Sigurbergsson and Leon Derczynski. 2020. Offensive Language and Hate Speech Detection for Danish. In Proceedings of LREC.
Mengfan Yao, Charalampos Chelmis, and Daphney Stavroula Zois. 2019. Cyberbullying Ends Here: Towards Robust Detection of Cyberbullying in Social Media. In Proceedings of WWW.
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019. Predicting the Type and Target of Offensive Posts in Social Media. In Proceedings of NAACL.