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Thesis : Mitigation strategies against Fake News diffusion in online social platforms

PhD school thesis
In this thesis we aim to study the principles that rule the diffusion of Fake News inside social platforms. In particular, we have the following two main objectives: (I) to characterise diffusion patterns and classify/predict FN from diffusion paths, and (II) to propose novel mitigation strategies by means of learning algorithms that can quickly spot FN from massive interactions traces.
Method and Novelty: In contradistinction to most literature devoted to the study of FN that heavily rely on natural language processing or sentiment analysis methods, in this work we propose to thoroughly study the diffusion dynamics of FN inside the social graph. Our hypothesis is that FN leave a signature that can be characterised by the structural and dynamical properties of interactions of the underlying social graph: namely, that FN will transit through specific diffusion paths or cause super-spreaders to be active at specific time intervals. Such structural signature has recently been incorporated into machine learning pipelines for FN detection [11, 12] with great success, yet a detailed study is still missing. The novelty of our approach will thus come from the use of original models of social platforms [6] as well as graph-related tools [9], both recently developed in LIP6, that can naturally find application in the analysis of diffusion and the proposal of mitigation control policies. Furthermore, the question how to fight against Fake News has not been sufficiently studied either, and there are no realistic policies suggested until now.

This PhD research project has been submitted for a funding request to “Ecole Doctorale d‘Informatique, Télécommunication et d‘Electronique (EDITE)”. The PhD candidate selected by the project leader will therefore participate in the project selection process (including a file and an interview) to obtain funding.

More details here

Contact :Anastasios Giovanidis, Vincent Gauthier