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A PhD position is available at the LIA (computer science laboratory of the University of Avignon, France), in collaboration with the software company Nectar de Code (https://www.nectardecode.com), starting from October 2015 and cofunded by the administrative PACA region and Nectar de Code.
Title: User models for the automatic monitoring of social networks
Keywords: machine learning, modeling, natural language processing, networka analysis
Description: http://lia.univ-avignon.fr/fileadmin/documents//Users/Intranet/chercheurs/Labo_Commun/CD2015/Sujet3.pdf (in French, contact us for more details in English)
Laboratory: LIA (http://lia.univ-avignon.fr/)
Funding: Provence region (public structure), Nectar de Code (software company)
Conditions: the candidate must be less than 30 years old
Starting date: October 2015
Required files: send the following files to all three advisors: CV + topic-related motivation letter + grade transcripts for both Master years (or equivalent)
Application deadline: 9th of june 2015 (note the very short delay, beyond our control)
In the recent years, the interactive aspects of the Internet became increasingly important, transforming it into the main support for free communication between potentially very different persons. This led to considerable scientific and societal issues related to the supervision of Web-based social interactions. Indeed, in order to work correctly, open communication spaces need to be monitored, which is a difficult task. Without any moderation, such a service can be hijacked, or end up hosting negative behaviors decreasing the service quality, or even illegal activities. One could argue the absence of any control actually contributed to the development of such open spaces on the Web, however the absence of any moderation could also dramatically limit their interest. Human-operated moderation is often costly, and even economically unfeasible; creating automatic monitoring methods for such large-scale textual interactions is consequently of the highest interest. We propose to develop an approach focused on users, and to evaluate it on the specific case of a social networking service. Tackling this problem involves designing models of the users’ behavior, and defining methods to estimate these statistical models. The general framework of this PhD topic is social networks analysis.