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

被引:1
作者
Aggarwal, Arnav [1 ]
Rai, Sunny [1 ]
Giorgi, Salvatore [1 ]
Havaldar, Shreya [1 ]
Sherman, Garrick [1 ]
Mittal, Juhi [1 ]
Chandra, Sharath [1 ]
机构
[1] Univ Penn, Philadelphia, PA 19104 USA
来源
COMPANION OF THE WORLD WIDE WEB CONFERENCE, WWW 2023 | 2023年
关键词
pain; social media; natural language processing; American communities; ETHNICITY; LANGUAGE; TWITTER; RACE;
D O I
10.1145/3543873.3587642
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.) 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.
引用
收藏
页码:1050 / 1058
页数:9
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