Predicting Human Activities from User-Generated Content

Steven Wilson, Rada Mihalcea


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.
Anthology ID:
P19-1245
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2572–2582
Language:
URL:
https://aclanthology.org/P19-1245
DOI:
10.18653/v1/P19-1245
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/P19-1245.pdf
Data
Event2Mind