Forecasting People’s Action via Social Media Data

In this paper, we explored predicting human activity based on users' content from social media. The activities we do are related to our interests, personalities, political preferences and our future decisions. The data set we collect contains examples of social media users related to many daily business activities. Then, we use the latest tailored sentence embedding framework to recognize the semantics of human activities and automatically cluster these activities. We proposed a neural network architecture to predict all activities performed by a specific user based on previous posts and self-describing text. Besides, we explore how adding inferred user characteristics into our model contributes to this forecasting task.