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Updated: Sep 9, 2025

Validation of a Psychosocial Intervention on Body Image in Older People: An Experimental Design
Published on: May 31, 2021
Seasonal body image dissatisfaction: a bi-hemispheric panel analysis of social media users across 4 years
Justin Thomas1,2, Timothy Regan3,4, Rana Samad3
1Global Digital Wellbeing Program (Sync), King Abdulaziz Center for World Culture (Ithra), Dhahran, Saudi Arabia. justin.thomas@liverpool.ac.uk.
Purpose:
Seasonal body image refers to within-person variations in body image satisfaction that correspond with climatic seasonality (winter, spring, summer, and autumn). Previous cross-sectional research involving male participants from northern (UK, USA, and Canada) and southern hemisphere (Australia) nations reports a peak in body image dissatisfaction during the summertime, with a decrease in the wintertime. Big Data extracted from social media platforms provides a novel means of further exploring the seasonal body image hypothesis in a larger and more diverse sample across several years.
Methods:
This study utilised panel data drawn from X/Twitter, a social media platform, to investigate the posts (N = 12,017,766) of users/authors (N = 1534) between 2020 and 2023. The panel consisted of authors from countries in both the northern and southern hemispheres. A template-driven search algorithm identified expressions of body image dissatisfaction (BID) in users' posts.
Results:
The rate of BID (relative to the overall number of posts) was calculated for each calendar month. A statistically significant summer spike was observed in the Northern hemisphere, while the data were non-significant but directionally supportive of a similar summer spike in the Southern hemisphere.
Conclusions:
This study partially supports the seasonal body image hypothesis, adding nuance to the current understanding of seasonality. This research has implications for the timing of public health initiatives aimed at preventing body image issues and eating disorders.
Level Of Evidence Iv:
Evidence obtained from multiple time series without intervention.
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