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[ecrea] postdoctoral fellowship open position university of lorraine
Sun Oct 28 07:36:07 GMT 2018
POSTDOCTORAL FELLOWSHIP OPEN POSITION UNIVERSITY OF LORRAINE
Online Hate Speech Against Migrants
Research coordinator: Crem, University of Lorraine (Pr. Angeliki Monnier)
Co-coordinator: Loria, University of Lorraine (Dr. Irina Illina MCF HDR,
Dr. Dominique Fohr CNRS)
TERMS AND VENUE
This one-year position will be based at Crem (Metz/Nancy) and Loria
(Nancy), France (University of Lorraine).
The target start date for the position is April 1st, 2019, with some
flexibility on the exact start date.
PhD in Information and Communication Sciences, or Language Science.
Research experience in social media and language data analysis.
Very good mastery of the French language.
Very good written and oral expression skills, in French and in English.
Autonomy, teamwork skills.
HOW TO APPLY
Applicants are requested to submit the following materials:
• A cover letter applying for the position
• Full CV and list of publications
• Statement of Research (summary of research achievements and
perspectives for upcoming researches)
• Academic transcripts (diplomas, courses statements, grades, etc.)
(unofficial versions are fine) • Recommendation letters are not
obligatory but are strongly recommended.
Deadline for application is December 10th, 2018.
Applications are only accepted through email. All documents must be sent
to (angeliki.monnier /at/ univ-lorraine.fr)
<mailto:(angeliki.monnier /at/ univ-lorraine.fr)>
Applicants will be interviewed by an Ad Hoc Commission on January 15th,
2019. DETAILED DESCRIPTION OF THE RESEARCH PROJECT
This position is open as part of the Open Language and Knowledge project
for Citizens (OLKi), carried out within the IMPACT/Lorraine University
of Excellence (LUE) framework. Under the direction of
Pr. Angeliki Monnier (Crem), Dr. Irina Illina and Dr. Dominique Fohr
(Loria), the post-doctoral fellow will work on online hate speech
According to the 2017 International Migration Report, the number of
migrants worldwide has increased rapidly in recent years. This
development is causing great public concern around the world,
particularly in Europe. The economic crisis affecting some countries of
the Old Continent also feeds feelings of insecurity, encouraging the
development of anti-immigrant movements. The media are often pointed out
for their tendency to depict refugees and migrants negatively,
consolidating fears. A recent EU project has revealed a significant
increase in hate speech against immigrants and minorities, who are often
accused of being the cause of current economic and social problems.
Participatory web and social media seem to amplify the intensity and
scope of hate speech. The fight against racism and hatred on the
Internet is currently one of the priorities of the French government. On
September 20th, 2018, a report commissioned on this topic was given to
the Prime Minister, containing twenty proposals to combat hate on the
Scientific context and scope
The objective of this postdoctoral contract will be to study the context
of the appearance of hateful contents (circumstances of emergence,
locutors, dissemination processes, etc.), and to analyze the latter as
linguistic productions (narrative approaches, speech acts, enunciation,
etc.) in the light of the creation of a lexicon of hate speech in French.
Crem and Loria are already involved in this project. Owing to the
technology of “neural networks” (deep learning), their collaboration
aims to collect and shape a corpus of hateful expressions against
migrants, but also to develop an app that could automatically detect
hate speech in comments posted on the Internet, especially in media
The objective of the Crem-Loria collaboration in this postdoctoral
contract is to refine these initial results through qualitative analyses
of online hate speech against migrants. The aim is to achieve a better
understanding of the social phenomenon of hatred, as well as to improve
the development of algorithms used to qualify language.
For this reason, the collaboration between Crem and Loria will take an
iterative form, between the qualitative analysis of restricted corpora
and the work with algorithms. It will cover both the constitution of the
corpus (online data collection, search for expressions of hate) and the
analysis of this corpus (creation and organization of the lexicon).
The research will focus on user-generated content (social networks,
comments on media websites, etc.). Part of the analyzed data will come
from the OLKi platform, which will also serve as a support for the
evaluation of the algorithms developed.
* Collect online data (hate speech against migrants), using technical
solutions proposed by Loria.
* Analyze these discourses using Humanities and Social Sciences (SHS)
approaches: socio- pragmatic contexts of hateful comments,
linguistic analysis of speeches (speech acts, narratives,
enunciation, etc.). Depending on the candidate’s profile, image
analysis can be
included in the research project, as an additional component.
• Contribute to the development of a lexicon in French about online
hatred against migrants
(supervised by Crem and Loria).
* Write scientific articles based on research results, in
collaboration with the supervisors and co- signed with them, to be
published in scientific journals and / or to be presented at
national and / or international conferences.
* Attend regular meetings between the two teams.
* Provide regular reports on the project’s progress and a final report
at the end of it.
* The fellow is expected to regularly participate in the Crem and
Loria seminars and other
Crem, Center for Research on Mediations, comprises more than 230
researchers: approximately 80 tenured scholars, more than 90 doctoral
students, 45 associate members and 7 staff members. Its researchers
belong to 11 disciplines: nearly 90 % come from the Information and
Communication Sciences, Language sciences, French, Literature and Art
Sciences; about 10 % are specialists in English and Anglo-Saxon
languages, Arabic, Germanic and Romance languages, Anthropology,
Psychology and Sociology.
Pixel is one of the four Crem teams, with more than 40 members,
including 16 tenured scholars, with a specialization in the field of the
usages of information and communication technologies. Pixel researchers
implement different methodological approaches: surveys, content
analyzes, socio- technical analyzes, usage observations,
socio-historical analysis, etc. For years, Pixel has been developing a
sustained research activity around several thematic areas: digital
educational practices (online learning platforms, serious games), access
to online information (search engines, information websites, social
networks, micro-blogging, information monitoring), online collaboration
(participatory work environments, watch and curation tools, viral
dissemination of journalistic content) and creative industries (digital
games, expressive games, video games).
Loria, Laboratory of Research in Computer Science and its Applications
is a joint research unit (UMR 7503), common to several institutions: the
CNRS, the University of Lorraine and Inria. Since its creation in 1997,
Loria's mission is to enhance and promote fundamental and applied
research in Computer Sciences. The scientific work is carried out in 28
teams structured in 5 departments, 15 of which are shared with Inria,
representing a total of more than 400 persons. Loria is one of the
largest laboratories in Lorraine.
Multispeech is one of the 28 Loria-INRIA Grand Est teams. It comprises
12 tenured scholars. The Multispeech research project focuses on speech
processing, paying particular attention to multisource (source
separation, robust speech recognition), multilingual (foreign language
learning) and multimodal (audiovisual synthesis) aspects. Its research
program is structured in 3 axes:
- the explicit modeling of speech, which exploits its physical dimension;
- the statistical modeling of speech, which relies on techniques of
automatic learning such as the Bayesian models (HMM-GMM) and networks of
deep neurons (DNN);
- the uncertainties related to the high variability of the speech signal
and the imperfection of the models.
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