Abstract
The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human
activities from user-generated content. We
collect a dataset containing instances of social
media users writing about a range of everyday
activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make
predictions about which clusters contain activities that were performed by a given user based
on the text of their previous posts and selfdescription. Additionally, we explore the degree to which incorporating inferred user traits
into our model helps with this prediction task.