Digital platforms enable the widespread dissemination of information, but their engagement-centric business models often promote the spread of ideologically homogeneous or controversial political content. These models can lead to the polarization of political opinions and impede the healthy functioning of democratic systems. The PhD will investigate innovative generative AI models devised for a deep understanding of political polarization and for countering its effects. It will mobilize several areas of AI: generative learning, frugal AI, continual learning, and multimedia learning. Advances will be associated with the following challenges:
-the modeling of political polarization, and the translation of the obtained domain model into actionable implementation requirements that will be used as inputs of AI algorithms;
-the curation of massive and diversified multimodal political data to ensure topical and temporal coverage, and to map these data to a common semantic representation space;
-the training of politics-oriented generative models to encode relevant knowledge effectively and efficiently and to generate labeled training data for downstream tasks;
-the specialization of the models for the specific tasks needed for a fine-grained understanding of polarization (topic detection, entity recognition, sentiment analysis);
-the continual update of the politics-oriented generative models and polarization-specific tasks to keep pace with the evolution of political events and news.