Predicting Human Activities from User-Generated Content
This work addresses the problem of activity prediction for social media analysis, but it appears incremental as it builds on existing methods without major breakthroughs.
The paper tackles predicting human activities from social media text by collecting a dataset of user posts about everyday activities, using a tailored sentence embedding framework for clustering, and training a neural network to predict activity clusters from user text and inferred traits. The result shows that incorporating user traits improves prediction accuracy, though specific numbers are not provided.
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 self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.