# Social Media Image Analysis for Public Health

arXiv: https://arxiv.org/abs/1512.04476 · HTML: https://mlsanity.com/paper/1512.04476/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2015.

## Built on by (most cited first, 5 of 5)

- [MoodCapture: Depression Detection Using In-the-Wild Smartphone Images](https://mlsanity.com/paper/2402.16182/) (2024)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [Othering and low status framing of immigrant cuisines in US restaurant reviews and large language models](https://mlsanity.com/paper/2307.07645/) (2023)
- [Discovering Image Usage Online: A Case Study With "Flatten the Curve''](https://mlsanity.com/paper/2307.06458/) (2023)
- [Refinement of an Epilepsy Dictionary through Human Annotation of Health-related posts on Instagram](https://mlsanity.com/paper/2405.08784/) (2024)

## Abstract (arXiv)

Several projects have shown the feasibility to use textual social media data to track public health concerns, such as temporal influenza patterns or geographical obesity patterns. In this paper, we look at whether geo-tagged images from Instagram also provide a viable data source. Especially for "lifestyle" diseases, such as obesity, drinking or smoking, images of social gatherings could provide information that is not necessarily shared in, say, tweets. In this study, we explore whether (i) tags provided by the users and (ii) annotations obtained via automatic image tagging are indeed valuable for studying public health. We find that both user-provided and machine-generated tags provide information that can be used to infer a county's health statistics. Whereas for most statistics user-provided tags are better features, for predicting excessive drinking machine-generated tags such as "liquid" and "glass" yield better models. This hints at the potential of using machine-generated tags to study substance abuse.