================[Apologies for any cross-posting]================
**Special issue of the journal Traitement Automatique des Langues (TAL)
Abusive Language Detection : Linguistic Resources, Methods and
Applications **
**Guest Editors**
Farah Benamara (IRIT-Toulouse University, IPAL Singapore), Delphine
Battistelli (MoDyCo, Paris Nanterre University) and Viviana Patti (Turin
University)
**Motivations**
Abusive language - or, in another very common terminology, hate speech -
and the propagation of harmful stereotypes have unfortunately become
commonplace occurrences on various social media platforms, partly due to
users’ freedom and anonymity and the lack of regulation provided by
these platforms. The sheer volume and often implicit nature of such
unwanted content make manual moderation of these user spaces a
formidable task. Various scientific communities interested in its at
least partial automation have taken up the problem over the past ten
years. In particular, Computational Social Science, Natural Language
Processing and Computational Linguistics have proposed numerous works to
create resources, datasets, and models aimed at automating the task of
abusive language detection (henceforth ALD). In fact, we see that ALD
has become a research theme in its own right in the field of Natural
Language Processing with an abundant literature.
Abusive language (umbrella term to refer to the various forms of harmful
language, such as toxic, offensive language, hate speech, and
stereotypes) is topically focused and each specific manifestation of
abusive language targets different vulnerable groups based on
characteristics such as gender (misogyny, sexism), ethnicity, race,
religion (xenophobia, racism, Islamophobia), sexual orientation
(homophobia), and so on. Most automatic ALD approaches cast the problem
into a binary classification task but important considerations should be
taken into account, in particular: (1) the topical focus or the
target-oriented nature of hate speech ; (2) the degree of engagement of
users in abusive content (e.g., denunciation, approbation, reporting,
neutral attitude) ; (3) the question of stereotypes and dominant
ideologies ; (4) the question of linguistic strategies more particularly
linked or born with social networks (e.g., emoticons, hashtags).
Furthermore, most of the work (resources, classifiers) is developed for
English.
**Topics**
Motivated by the interest of the community in the problem of ALD, we
invite papers from Natural Language Processing, Machine Learning and
Computational Social Sciences. We explicitly encourage interdisciplinary
submissions (resources, computational methods, and user applications at
the interface of linguistics/psychology/socio-linguistics/sociology) but
also position papers on the actual state of the art in the field
discussing the limitations of the current approaches and directions for
future work. The topics covered by the special issue include, but are
not limited to:
-- Linguistic resources and evaluation: annotation schemes, corpus
linguistics studies, new datasets, with a particular interest in French
language and/or multilingual resources. In the case of strictly lexical
resources: methods for constituting them and coverage, semantic
categories retained.
-- Formal/Conceptual approaches for ALD as inspired by models in
sociology, socio-linguistics and psychology.
-- Models and Methods: supervised and unsupervised approaches, including
LLMs.
-- Role of contextual phenomena, including discourses, extra-linguistic
contexts (e.g., cultural aspects).
-- Models for cross-lingual and multimodal detection.
-- New approaches beyond binary classification: target-oriented ALD,
degrees of user engagement, etc.
-- Dynamics of online AL in social media, propaganda propagation.
-- Bias detection and removal in resource creation, datasets and methods.
-- Application of ALD tools in education, social media content
moderation, etc.
-- Social, legal, and ethical implications of detecting, monitoring and
moderating AL.
**Important dates**
May 31th, 2024: Submission deadline
July 15th, 2024: Notification of acceptance after first rereading
End of September 2024: Revised version
Mid October 2024: Final decision
End of November 2024: Camera ready
January 2025: Publication of the special issue
**Submission**
Submissions can either be in French or English and should follow the
journal templates: https://tal-65-3.sciencesconf.org/
**About the journal**
Traitement Automatiques des Langues Journal (TAL) is the international
French journal of Natural Language Processing
(https://www.atala.org/revuetal) published by ATALA (French Association
for Natural Language Processing, http://www.atala.org) since 1959 with
the support of CNRS (National Centre for Scientific Research). It is
indexed by ACL Anthology as well as DBLP. It is also supported by the
Institute of Human and Social Sciences of the CNRS.
**Contact**
For any question, please contact tal-6...@sciencesconf.org
**External committee**
-- Cristina Bosco, University of Turin
-- Elena Cabrio, University of Côte d'Azur
-- Tommaso Caselli, Faculty of Arts, Rijksuniveristeit Groningen
-- Valentina Dragos, ONERA
-- Karën Fort, Sorbonne University
-- Claire Hugonnier, University of Grenoble Alpes
-- Irina Illina, University of Lorraine
-- Roy Ka-Wei Lee, Singapore University of Technology and Design
-- Véronique Moriceau, IRIT, University of Toulouse
-– Frédérique Segond, INRIA Paris
-- Mariona Taulé, University of Barcelona
-- Samuel Vernet, Aix-Marseille University
-- Mathieu Valette, Paris Sorbonne Nouvelle University
-- Marcos Zampieri, George Mason University
--
========================
Farah Benamara Zitoune
Professor in Computer Science, Université Paul Sabatier
IRIT-CNRS
118 Route de Narbonne, 31062, Toulouse.
Tel : +33 5 61 55 77 06
http://www.irit.fr/~Farah.Benamara
==================================
--
========================
Farah Benamara Zitoune
Professor in Computer Science, Université Paul Sabatier
IRIT-CNRS
118 Route de Narbonne, 31062, Toulouse.
Tel : +33 5 61 55 77 06
http://www.irit.fr/~Farah.Benamara
==================================
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