Modelling Familiarity for Intelligent Personalized Social Mobilization

No. 1: Thirty-First AAAI Conference On Artificial Intelligence

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 31

Doctoral Consortium

With the rise of the Internet and social media, social mobilization - large-scale mobilization manpower for scientific, social, and political activities through crowdsourcing - has become a widespread practice. Despite the success, social mobilization is not without its limitations. Local trapping of diffusion and the dependence on highly connected individuals to mobilize people in distance locations affect the effectiveness of social mobilization. Furthermore, as empirical studies on people's responses to various social mobilization approaches are lacking, it is a significant challenge for artificial intelligence (AI) researchers to design effective and efficient decision support mechanisms to help manage this emerging phenomenon. In my thesis, I conduct large-scale empirical studies to help the AI research community establish baseline personal variabilities in different people's response patterns to social mobilization approaches. Based on the collected dataset, I will further propose computational algorithmic crowdsourcing mechanisms which leverage the empirical evidence to improve the effectiveness and efficiency of social mobilization, towards achieving superlinear productivity. Throughout this process, I will also incorporate human factors into the computational models to benefit social mobilization efforts.

10.1609/aaai.v31i1.10521