Social Media Image Analysis for Public Health

Venkata Garimella, Abdulrahman Alfayad, Ingmar Weber

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

25 Citations (Scopus)

Abstract

Several projects have shown the feasibility to use emph{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 emph{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.
Original languageEnglish
Title of host publicationCHI '16 Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
PublisherACM
Pages5543-5547
ISBN (Print)978-1-4503-3362-7
DOIs
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Colorado Convention Center, Denver, United States
Duration: 6 May 201711 May 2017
Conference number: 35
https://chi2017.acm.org/

Conference

ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
CountryUnited States
CityDenver
Period06/05/201711/05/2017
Internet address

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