Explainable and Multi-Modal DL/ML Models for Extreme Narrative Detection in the Online Social Discourse.

When:
15/06/2023 all-day
2023-06-15T02:00:00+02:00
2023-06-15T02:00:00+02:00

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : ETIS (equipe REMI) / AGORA
Durée : 3 ans
Contact : michele.linardi@cyu.fr
Date limite de publication : 2023-06-15

Contexte :
Increased polarization triggered by social protest movements, the Covid-19 crisis and the war in Ukraine are historical events that have recently favored extremist narratives in public and online debates.
Extremist (a.k.a Extreme) narratives (EN) constitute counter-narratives in the sense that they challenge mainstream worldviews and social interpretations of major events in many kinds of public debate e.g., social media, parliamentary interventions, journals, books, and many others. This thesis offer is integrated in the context of the Horizon Europe ARENAS project (Grant agreement ID: 101094731), coordinated by CY Cergy Paris Université, and aims to contribute to Work Package 2 dedicated to the definition, identification and detection of extremist narratives. (https://cordis.europa.eu/project/id/101094731).

Sujet :
In this Ph.D. thesis, we want to study the EN characterization, modeling and automatic detection. Specifically, we note that the extremist narratives analysis should not only be seen from a radicalization/terrorism viewpoint, for which a rich Machine Learning (ML) literature already proposes multiple solutions. We observe that EN must be studied in a more general context that concerns different kind of values such as people democracy, citizenship, rights, etc., which do not necessarily assume a violent or hatred sentiments.
EN modeling cannot be only isolated to violent and extreme language features, but it must also consider a wider spectrum of narrative elements such as the beliefs, traits, practices of a collectivity, etc. that identifies a group of people sharing the same identity.

The principal thesis objective is to propose new (DL/ML) tools that characterize extremist narratives in corpora from different contexts (social media, political debates, transcripts, etc.).
We argue that EN modeling choice is not only restricted to text but must effectively consider other types of data, i.e., Graphs, Images, and Knowledge Base. In this case, we want to focus on multi-modal knowledge extraction, which is a challenging topic in Machine Learning.
The existing multi-view machine learning approaches usually are not adapted for multi-modal data or use the same similarity/distance measure for all the views.
A crucial objective of our research is to propose novel multi-modal knowledge extraction methods to detect extremism narratives and characterize them.

The successful candidate will work in close collaboration with language experts (from Heinrich Heine University of Düsseldorf and from Institute of Contemporary History-Ljubjana University) that will provide linguistic expertise and validation, along with labelled corpora from heterogeneous online (multi-modal) content. Interactions will be made with work already in progress at Cergy on forensic linguistics, the analysis of fake news, and digital discourse in a political context.

Profil du candidat :
Applicants should contact via email Michele Linardi (michele.linardi@cyu.fr), Julien Longhi (julien.longhi@cyu.fr) and Nistor Grozavu (nistor.grozavu@cyu.fr) with:

• A full curriculum vitae, including a summary of previous research experience.
• A transcript of higher education records.
• A one-page research statement discussing how the candidate’s background fits the proposed topic.
• Two support letters of persons that have worked with them.

The deadline of the application is: June 4th, 2023 (11h59 pm AoE).

Formation et compétences requises :
The candidate must fit the following requirements:

– Master’s degree in computer science or data science.
– Advanced programming skills in Python (C++/Java is a plus).
– Strong mathematical background, including Linear Algebra and Statistics.
– Research experience in Machine learning, Deep Learning and Data Mining.
– Fluency in written and spoken English is essential.

Adresse d’emploi :
CY Cergy Paris Université
33 bd Port, 95000 Cergy

Document attaché : 202305170940_ThesisProposal_full.pdf