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Estimating county health statistics with twitter

Author:

Aron CulottaAuthors Info & Claims

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 1335 - 1344

https://doi.org/10.1145/2556288.2557139

Published: 26 April 2014 Publication History

Abstract

Understanding the relationships among environment, behavior, and health is a core concern of public health researchers. While a number of recent studies have investigated the use of social media to track infectious diseases such as influenza, little work has been done to determine if other health concerns can be inferred. In this paper, we present a large-scale study of 27 health-related statistics, including obesity, health insurance coverage, access to healthy foods, and teen birth rates. We perform a linguistic analysis of the Twitter activity in the top 100 most populous counties in the U.S., and find a significant correlation with 6 of the 27 health statistics. When compared to traditional models based on demographic variables alone, we find that augmenting models with Twitter-derived information improves predictive accuracy for 20 of 27 statistics, suggesting that this new methodology can complement existing approaches.

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Cited By

Xu XYao BDong YGabriel SYu HHendler JGhassemi MDey AWang D(2024)Mental-LLMProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435408:1(1-32)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643540
Saha KGupta PMark GKiciman EDe Choudhury M(2024)Observer Effect in Social Media UseProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642078(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642078
Mangalik SEichstaedt JGiorgi SMun JAhmed FGill GV. Ganesan ASubrahmanya SSoni NClouston SSchwartz H(2024)Robust language-based mental health assessments in time and space through social medianpj Digital Medicine10.1038/s41746-024-01100-07:1Online publication date: 2-May-2024
https://doi.org/10.1038/s41746-024-01100-0
Show More Cited By

Index Terms

Estimating county health statistics with twitter
1. Human-centered computing
  1. Collaborative and social computing
  2. Human computer interaction (HCI)
2. Information systems
  1. Information retrieval
  2. Information storage systems

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cover image ACM Conferences

CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2014

4206 pages

ISBN:9781450324731

DOI:10.1145/2556288

General Chairs:
Matt Jones
Swansea University, Wales, UK
,
Philippe Palanque
Université Paul Sabatier, France
,
Program Chairs:
Albrecht Schmidt
University of Stuttgart, Germany
,
Tovi Grossman
Autodesk Research, Canada

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 26 April 2014

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CHI '14: CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2014

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CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Citations

Cited By

Xu XYao BDong YGabriel SYu HHendler JGhassemi MDey AWang D(2024)Mental-LLMProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435408:1(1-32)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643540
Saha KGupta PMark GKiciman EDe Choudhury M(2024)Observer Effect in Social Media UseProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642078(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642078
Mangalik SEichstaedt JGiorgi SMun JAhmed FGill GV. Ganesan ASubrahmanya SSoni NClouston SSchwartz H(2024)Robust language-based mental health assessments in time and space through social medianpj Digital Medicine10.1038/s41746-024-01100-07:1Online publication date: 2-May-2024
https://doi.org/10.1038/s41746-024-01100-0
Küçük DArıcı N(2023)Deep Learning-Based Sentiment and Stance Analysis of Tweets About VaccinationInternational Journal on Semantic Web and Information Systems10.4018/IJSWIS.33386519:1(1-18)Online publication date: 21-Nov-2023
https://doi.org/10.4018/IJSWIS.333865
Sharma MPeng Y(2023)How Visual Aesthetics and Calorie Density Predict Food Image Popularity on Instagram: A Computer Vision AnalysisHealth Communication10.1080/10410236.2023.217563539:3(577-591)Online publication date: 9-Feb-2023
https://doi.org/10.1080/10410236.2023.2175635
Polyzos E(2023)Inflation and the war in Ukraine: Evidence using impulse response functions on economic indicators and Twitter sentimentResearch in International Business and Finance10.1016/j.ribaf.2023.10204466(102044)Online publication date: Oct-2023
https://doi.org/10.1016/j.ribaf.2023.102044
Giorgi SEichstaedt JPreotiuc-Pietro DGardner JAndrew Schwartz HUngar L(2023)Filling in the White Space: Spatial Interpolation with Gaussian Processes and Social Media DataCurrent Research in Ecological and Social Psychology10.1016/j.cresp.2023.100159(100159)Online publication date: Oct-2023
https://doi.org/10.1016/j.cresp.2023.100159
Sultan MScholz Cvan den Bos W(2023)Leaving traces behind: Using social media digital trace data to study adolescent wellbeingComputers in Human Behavior Reports10.1016/j.chbr.2023.10028110(100281)Online publication date: May-2023
https://doi.org/10.1016/j.chbr.2023.100281
Nguyen HLe H(2023)Predicting Community Health Through Heterogeneous Social NetworksSN Computer Science10.1007/s42979-023-01718-z4:3Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1007/s42979-023-01718-z
Gilal NAl-Thelaya KAl-Saeed JAbdallah MSchneider JShe JAwan JAgus M(2023)Evaluating machine learning technologies for food computing from a data set perspectiveMultimedia Tools and Applications10.1007/s11042-023-16513-483:11(32041-32068)Online publication date: 19-Sep-2023
https://doi.org/10.1007/s11042-023-16513-4
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