Hate Speech (HS) and harassment are particularly widespread in online
communication, especially due to users’ freedom and anonymity and the
lack of regulation provided by social media platforms. This phenomenon
has determined a growing interest in using artificial intelligence and
Natural Language Processing techniques to address social and ethical
issues. An extensive body of work has been proposed to automatically
detect HS relying on a variety of deep learning methods (Founta and
Nunes, 2018; Schmidt and Wiegand, 2017). Most research focus on HS as
expressed in texts without taking into account the contexts in which
they have been uttered. This PhD aims to bridge the gap by investigating
for the first time how HS are expressed and detected in multi-party
dialogues. We will propose new dialogue datasets for HS detection as
well as new context-based deep learning methods that leverage the
conversation thread to account for hateful contents and how they evolve
as the dialogue proceeds.

This project is part of the DesCartes
program,https://www.cnrsatcreate.cnrs.fr/descartes/ ” target=”_blank”>https://www.cnrsatcreate.cnrs.fr/descartes/
<https://www.cnrsatcreate.cnrs.fr/descartes/>, that aims to develop
disruptive hybrid AI to serve the smart city and to enable optimized
decision-making in complex situations, encountered for critical urban


Paula Fortuna, Sérgio Nunes:A Survey on Automatic Detection of Hate
Speech in Text. ACM Comput. Surv. 51(4): 85:1-85:30 (2018)

Anna Schmidt, Michael Wiegand: A Survey on Hate Speech Detection using
Natural Language Processing. SocialNLP@EACL 2017: 1-10




–> Degree in Computer science with solid background in NLP and/or
machine learning

–> A good experience in deep learning approaches for NLP

–> Good programming skills in Python

Educational level:

Master Degree

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