# Predicting Human Activities from User-Generated Content

## TL;DR

This paper presents a method for predicting human activities from social media content by leveraging sentence embeddings, clustering, and user trait inference, demonstrating improved activity prediction accuracy.

## Contribution

It introduces a novel approach combining sentence embeddings, activity clustering, and user trait inference to predict human activities from user-generated content.

## Key findings

- Effective clustering of activities based on semantic embeddings
- Improved activity prediction when incorporating user traits
- Demonstrated potential for personalized activity inference

## 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 self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.