Predicting Human Activities from User-Generated Content

Steven Wilson, Rada Mihalcea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Place of PublicationFlorence, Italy
PublisherAssociation for Computational Linguistics
Pages2572-2582
Number of pages11
ISBN (Electronic)978-1-950737-48-2
DOIs
Publication statusPublished - 2 Aug 2019
Event57th Annual Meeting of the Association for Computational Linguistics - Fortezza da Basso, Florence, Italy
Duration: 28 Jul 20192 Aug 2019
Conference number: 57
http://www.acl2019.org/EN/index.xhtml

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2019
Country/TerritoryItaly
CityFlorence
Period28/07/192/08/19
Internet address

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