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Preface: Special issue on NLP approaches to offensive content online

Published online by Cambridge University Press:  06 December 2023

Marcos Zampieri Open the ORCID record for Marcos Zampieri [Opens in a new window] ,
Isabelle Augenstein ,
Siddharth Krishnan ,
Joshua Melton  and
Preslav Nakov
Marcos Zampieri*
Affiliation:
George Mason University, Fairfax, VA, USA
Isabelle Augenstein
Affiliation:
University of Copenhagen, Copenhagen, Denmark
Siddharth Krishnan
Affiliation:
University of North Carolina at Charlotte, Charlotte, North Carolina, USA
Joshua Melton
Affiliation:
University of North Carolina at Charlotte, Charlotte, North Carolina, USA
Preslav Nakov
Affiliation:
Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, UAE
*
Corresponding author: Marcos Zampieri; Email: mzampier@gmu.edu
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Extract

We are delighted to present the Special Issue on NLP Approaches to Offensive Content Online published in the Journal of Natural Language Engineering issue 29.6. We are happy to have received a total of 26 submissions to the special issue evidencing the interest of the NLP community in this topic. Our guest editorial board comprised of international experts in the field has worked hard to review all submissions over multiple rounds of peer review. Ultimately, we accepted nine articles to appear in this special issue.

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Article
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Natural Language Engineering , Volume 29 , Issue 6 , November 2023 , pp. 1415
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Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

We are delighted to present the Special Issue on NLP Approaches to Offensive Content Online published in the Journal of Natural Language Engineering issue 29.6. We are happy to have received a total of 26 submissions to the special issue evidencing the interest of the NLP community in this topic. Our guest editorial board comprised of international experts in the field has worked hard to review all submissions over multiple rounds of peer review. Ultimately, we accepted nine articles to appear in this special issue.

The papers in this special issue deal with the application of computational models to automatically identify and further understand offensive content in social media. A wide variety of topics are covered here such as the identification of offensive spans in text, the variability and intrinsic subjectivity of data annotation, machine translation of offensive content, to name a few. The majority of papers included in this special issue describe research on English, while some deal with other languages such as Arabic, German, Hindi, and Italian.

Offensive content is pervasive in social media, and it comes in various forms. This special issue contains papers dealing with abusive language, aggression, cyberbulling, hate speech, offensive language, and toxicity. While specific fine-grained definitions may differ, papers in this special issue show that there is often substantial overlap between these phenomena which allows researchers to treat multiple types of offensive content using similar computational models.

We would like to take this opportunity to thank our guest editorial board members, who worked hard to review the large number of submissions we received. The names of all guest editorial board members are listed on the special issue website.Footnote a We further thank the NLE editorial staff and the NLE executive editor Prof. Ruslan Mitkov, for the support provided during the editorial process.

Marcos Zampieri, Isabelle Augenstein, Siddharth Krishnan, Joshua Melton and Preslav Nakov

Guest Editors of the NLE Special Issue on NLP Approaches to Offensive Content Online