Citation

Bhogal AN, Berrocal V, Romero D, Willis M, Vydiswaran VV, Veinot TC. Am. J. Prev. Med. 2024; ePub(ePub): ePub.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.amepre.2024.01.002

PMID

38191003

Abstract

INTRODUCTION: Social media sites like Twitter (now X) are increasingly used to create health behavior metrics for public health surveillance. Yet little is known about social norms that may bias the content of posts about health behaviors. Social norms for posts about four health behaviors (smoking tobacco, drinking alcohol, physical activity, eating food) on Twitter/X were evaluated.

METHODS: This was a randomized experiment delivered via web-based survey to adult, English-speaking Twitter/X users in three Michigan, USA, counties from 2020-2022 (n=560). Each participant viewed 24 posts presenting experimental manipulations regarding four health behaviors and answered questions about each post's social acceptability. Principal component analysis was used to combine survey responses into one perceived social acceptability measure. Linear mixed models with the Benjamini-Hochberg correction were implemented to test seven study hypotheses in 2023.

RESULTS: Supporting six hypotheses, posts presenting healthier (CI:0.028,0.454), less stigmatized behaviors (CI 0.552,0.157) were more socially acceptable than posts regarding unhealthier, stigmatized behaviors. Unhealthy (CI:-0.268,-0.109) and stigmatized behavior (CI:-0.261,-0.103) posts were less acceptable for more educated participants. Posts about collocated activities (CI:0.410,0.573) and accompanied by expressions of liking (CI:0.906,1.11) were more acceptable than activities undertaken alone or disliked. Contrary to one hypothesis, posts reporting unusual activities were less acceptable than usual ones (CI:-0.472,0.312).

CONCLUSIONS: Perceived social acceptability may be associated with the frequency and content of health behavior posts. Users of Twitter/X and other social media platform posts to estimate health behavior prevalence should account for potential estimation biases from perceived social acceptability of posts.

Language: en