research-article

A Cross-Modal Study of Pain Across Communities in the United States

Authors:

Arnav Aggarwal,

Salvatore Giorgi,

Shreya Havaldar,

Garrick Sherman,

Sharath Chandra GuntukuAuthors Info & Claims

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

Pages 1050 - 1058

https://doi.org/10.1145/3543873.3587642

Published: 30 April 2023 Publication History

Abstract

Pain is one of the most prevalent reasons for seeking medical attention in the United States. Understanding how different communities report and express pain can aid in directing medical efforts and in advancing precision pain management. Using a large-scale self-report survey data set on pain from Gallup (2.5 million surveys) and social media posts from Twitter (1.8 million tweets), we investigate a) if Twitter posts could predict community-level pain and b) how expressions of pain differ across communities in the United States. Beyond observing an improvement of over 9% (in Pearson r) when using Twitter language over demographics to predict community-level pain, our study reveals that the discourse on pain varied significantly across communities in the United States. Evangelical Hubs frequently post about God, lessons from struggle, and prayers when expressing pain, whereas Working Class Country posts about regret and extreme endurance. Academic stresses, injuries, painkillers, and surgeries were the most commonly discussed pain themes in College Towns; Graying America discussed therapy, used emotional language around empathy and anger, and posted about chronic pain treatment; the African American South posted about struggles, patience, and faith when talking about pain. Our study demonstrates the efficacy of using Twitter to predict survey-based self-reports of pain across communities and has implications in aiding community-focused pain management interventions.

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Cited By

Kaur RBhatia MKumar A(2024)Am I Hurt?: Evaluating Psychological Pain Detection in Hindi Text Using Transformer-based ModelsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/365020623:8(1-17)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1145/3650206

Index Terms

A Cross-Modal Study of Pain Across Communities in the United States
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
  2. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

April 2023

1567 pages

ISBN:9781450394192

DOI:10.1145/3543873

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Published: 30 April 2023

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National Institute on Minority Health and Health Disparities

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

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Cited By

Kaur RBhatia MKumar A(2024)Am I Hurt?: Evaluating Psychological Pain Detection in Hindi Text Using Transformer-based ModelsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/365020623:8(1-17)Online publication date: 5-Mar-2024
https://dl.acm.org/doi/10.1145/3650206

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