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Assessing Behavior Stage Progression From Social Media Data

Authors:

Elissa R. Weitzman,

Rumi ChunaraAuthors Info & Claims

CSCW '17: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

Pages 1320 - 1333

https://doi.org/10.1145/2998181.2998336

Published: 25 February 2017 Publication History

Abstract

Important work rooted in psychological theory posits that health behavior change occurs through a series of discrete stages. Our work builds on the field of social computing by identifying how social media data can be used to resolve behavior stages at high resolution (e.g. hourly/daily) for key population subgroups and times. In essence this approach opens new opportunities to advance psychological theories and better understand how our health is shaped based on the real, dynamic, and rapid actions we make every day. To do so, we bring together domain knowledge and machine learning methods to form a hierarchical classification of Twitter data that resolves different stages of behavior. We identify and examine temporal patterns of the identified stages, with alcohol as a use case (planning or looking to drink, currently drinking, and reflecting on drinking). Known seasonal trends are compared with findings from our methods. We discuss the potential health policy implications of detecting high frequency behavior stages.

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Index Terms

Assessing Behavior Stage Progression From Social Media Data
1. Human-centered computing
2. Information systems
  1. Information retrieval

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cover image ACM Conferences

CSCW '17: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

February 2017

2556 pages

ISBN:9781450343350

DOI:10.1145/2998181

General Chairs:
Charlotte P. Lee
University of Washington
,
Steve Poltrock
Retired
,
Program Chairs:
Louise Barkhuus
Stockholm University and Cornell Tech
,
Marcos Borges
Universidade Federal do Rio de Janeiro
,
Wendy Kellogg
IBM T.J. Watson Research Center

Copyright © 2017 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 25 February 2017

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CSCW '17

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CSCW '17: Computer Supported Cooperative Work and Social Computing

February 25 - March 1, 2017

Oregon, Portland, USA

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CSCW '17 Paper Acceptance Rate 183 of 530 submissions, 35%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

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CSCW '25

Sponsor:
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Computer-Supported Cooperative Work and Social Computing

October 18 - 22, 2025

Bergen , Norway

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Chiu HSun CLiu YSue YYeh P(2024)Comparing four algorithms in predicting the risk of driving under the influence of alcohol among individuals with alcohol use disorderCurrent Psychology10.1007/s12144-024-06136-9Online publication date: 11-Jun-2024
https://doi.org/10.1007/s12144-024-06136-9
Monselise MYang C(2022)”I’m always in so much pain and no one will understand” - Detecting Patterns in Suicidal Ideation on RedditCompanion Proceedings of the Web Conference 202210.1145/3487553.3524700(686-691)Online publication date: 25-Apr-2022
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Chhetri BGoyal LMittal M(2022)Identifying Predictors for Substance Consumption Pattern Using Machine Learning TechniquesProceedings of International Conference on Intelligent Cyber-Physical Systems10.1007/978-981-16-7136-4_9(101-115)Online publication date: 24-Jan-2022
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