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.
Social media has also enabled large-scale studies linking lifestyle and health data at an @math level. For example, @cite_5 , build a classifier to identify the health of a user based on their Twitter usage. Many more hidden'', yet important conditions such as depression @cite_7 @cite_0 @cite_23 , sleep problems @cite_17 , eating disorders @cite_2 , and substance use @cite_18 have been studied using social media data. Our study is different from the ones discussed above in that we propose the use of image data to study public health. Abdullah, et al @cite_3 use smile recognition from images posted on social media to study and quantify the overall societ al happiness. @cite_23 study depression related images on Instagram and establish[ed] the importance of visual imagery as a vehicle for expressing aspects of depression". In our work, we study if the use of image recognition techniques helps in understanding a broader range of health related issues.
@cite_18 @cite_7 @cite_3 @cite_0 @cite_23 @cite_2 @cite_5 @cite_17
2041003138 2513928994 2104925568 2078120282
The increasing adoption of social media provides unprecedented opportunities to gain insight into human nature at vastly broader scales. Regarding the study of population-wide sentiment, prior research commonly focuses on text-based analyses and ignores a treasure trove of sentiment-laden content: images. In this paper, we make methodological and computational contributions by introducing the Smile Index as a formalized measure of societ al happiness. Detecting smiles in 9 million geo-located tweets over 16 months, we validate our Smile Index against both text-based techniques and self-reported happiness. We further make observational contributions by applying our metric to explore temporal trends in sentiment, relate public mood to societ al events, and predict economic indicators. Reflecting upon the innate, language-independent aspects of facial expressions, we recommend future improvements and applications to enable robust, global-level analyses. We conclude with implications for researchers studying and facilitating the expression of collective emotion through socio-technical systems. Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners’ average unassisted diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These results suggest new avenues for early screening and detection of mental illness. Understanding the relationships among environment, behavior, and health is a core concern of public health researchers. While a number of recent studies have investigated the use of social media to track infectious diseases such as influenza, little work has been done to determine if other health concerns can be inferred. In this paper, we present a large-scale study of 27 health-related statistics, including obesity, health insurance coverage, access to healthy foods, and teen birth rates. We perform a linguistic analysis of the Twitter activity in the top 100 most populous counties in the U.S., and find a significant correlation with 6 of the 27 health statistics. When compared to traditional models based on demographic variables alone, we find that augmenting models with Twitter-derived information improves predictive accuracy for 20 of 27 statistics, suggesting that this new methodology can complement existing approaches. Social media sites have been used to track the expression of emotion words across nations. A question left unexplored is whether, at a similar scale, these sites can be used to collect reliable well-being data. To tackle this question, we collect Satisfaction With Life (SWL) test results from a Facebook application and show that aggregate country-level results significantly vary across twelve rich countries and strongly correlate with official well-being scores. To then show that collecting data on Facebook offers an informative look at the sociology of well-being, we study the impact of (un)happiness on the twelve countries by relating the test results on Facebook to reputable international indicators of social and health problems. We find that countries where happiness is lower have increased problems across the board: decreasing well-being is associated with increasing homicide, obesity, drug use, mental illness, and anxiety. In addition to offering these findings, this work hints at the conditions under which social media could be used for data-driven social science research.