Predicting Human Activities from User-Generated Content

Steven Wilson; Rada Mihalcea

doi:10.18653/v1/P19-1245

Predicting Human Activities from User-Generated Content

Steven Wilson, Rada Mihalcea

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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 language	English
Title of host publication	Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Place of Publication	Florence, Italy
Publisher	Association for Computational Linguistics
Pages	2572-2582
Number of pages	11
ISBN (Electronic)	978-1-950737-48-2
DOIs	https://doi.org/10.18653/v1/P19-1245
Publication status	Published - 2 Aug 2019
Event	57th Annual Meeting of the Association for Computational Linguistics - Fortezza da Basso, Florence, Italy Duration: 28 Jul 2019 → 2 Aug 2019 Conference number: 57 http://www.acl2019.org/EN/index.xhtml

Conference

Conference	57th Annual Meeting of the Association for Computational Linguistics
Abbreviated title	ACL 2019
Country/Territory	Italy
City	Florence
Period	28/07/19 → 2/08/19
Internet address	http://www.acl2019.org/EN/index.xhtml

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10.18653/v1/P19-1245Licence: Creative Commons: Attribution (CC-BY)

Predicting Human Activities_WILSON_DOA13052019_VOR_CC-BYFinal published version, 377 KBLicence: Creative Commons: Attribution (CC-BY)

https://www.aclweb.org/anthology/P19-1245Licence: Creative Commons: Attribution (CC-BY)

Cite this

@inproceedings{9a12d25455e84d0aa70eaad280a16012,

title = "Predicting Human Activities from User-Generated Content",

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.",

author = "Steven Wilson and Rada Mihalcea",

year = "2019",

month = aug,

day = "2",

doi = "10.18653/v1/P19-1245",

language = "English",

pages = "2572--2582",

booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

note = "57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

url = "http://www.acl2019.org/EN/index.xhtml",

}

Wilson, S & Mihalcea, R 2019, Predicting Human Activities from User-Generated Content. in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, pp. 2572-2582, 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 28/07/19. https://doi.org/10.18653/v1/P19-1245

TY - GEN

T1 - Predicting Human Activities from User-Generated Content

AU - Wilson, Steven

AU - Mihalcea, Rada

N1 - Conference code: 57

PY - 2019/8/2

Y1 - 2019/8/2

N2 - 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.

AB - 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.

UR - https://github.com/steve-wilson/activity_prediction

U2 - 10.18653/v1/P19-1245

DO - 10.18653/v1/P19-1245

M3 - Conference contribution

SP - 2572

EP - 2582

BT - Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

PB - Association for Computational Linguistics

CY - Florence, Italy

T2 - 57th Annual Meeting of the Association for Computational Linguistics

Y2 - 28 July 2019 through 2 August 2019

ER -

Predicting Human Activities from User-Generated Content

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