# Predicting Human Activities from User-Generated Content

arXiv: https://arxiv.org/abs/1907.08540 · HTML: https://mlsanity.com/paper/1907.08540/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Human Action Co-occurrence in Lifestyle Vlogs using Graph Link Prediction](https://mlsanity.com/paper/2309.06219/) (2023)

## Abstract (arXiv)

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.