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
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Adolescents are developmentally sensitive to pathways that influence alcohol and other drug (AOD) use. In the absence of guidance, their routine engagement with social media may add a further layer of risk. There are several potential mechanisms for social media use to influence AOD risk, including exposure to peer portrayals of AOD use, socially amplified advertising, misinformation, and predatory marketing against a backdrop of lax regulatory systems and privacy controls. Here the authors summarize the influences of the social media world and suggest how pediatricians in everyday practice can alert youth and their parents to these risks to foster conversation, awareness, and harm reduction. Major depression constitutes a serious challenge in personal and public health. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. We explore the potential to use social media to detect and diagnose major depressive disorder in individuals. We first employ crowdsourcing to compile a set of Twitter users who report being diagnosed with clinical depression, based on a standard psychometric instrument. Through their social media postings over a year preceding the onset of depression, we measure behavioral attributes relating to social engagement, emotion, language and linguistic styles, ego network, and mentions of antidepressant medications. We leverage these behavioral cues, to build a statistical classifier that provides estimates of the risk of depression, before the reported onset. We find that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement. We believe our findings and methods may be useful in developing tools for identifying the onset of major depression, for use by healthcare agencies; or on behalf of individuals, enabling those suffering from depression to be more proactive about their mental health. 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. Self-disclosure is an important element facilitating improved psychological wellbeing in individuals with mental illness. As social media is increasingly adopted in health related discourse, we examine how these new platforms might be allowing honest and candid expression of thoughts, experiences and beliefs. Specifically, we seek to detect levels of self-disclosure manifested in posts shared on different mental health forums on Reddit. We develop a classifier for the purpose based on content features. The classifier is able to characterize a Reddit post to be of high, low, or no self-disclosure with 78 accuracy. Applying this classifier to general mental health discourse on Reddit, we find that bulk of such discourse is characterized by high self-disclosure, and that the community responds distinctively to posts that disclose less or more. We conclude with the potential of harnessing our proposed self-disclosure detection algorithm in psychological therapy via social media. We also discuss design considerations for improved community moderation and support in these vulnerable self-disclosing communities. Despite the well-established finding that people share negative emotions less openly than positive ones, a hashtag search for depression-related terms in Instagram yields millions of images. In this study, we examined depression-related images on Instagram along with their accompanying captions. We want to better understand the role of photo sharing in the lives of people who suffer from depression or who frame their experience as such; specifically, whether this practice engages support networks and how social computing systems can be designed to support such interactions. To lay the groundwork for further investigation, we report here on content analysis of depression-related posts. Abstract Purpose Disordered eating behavior—dieting, laxative use, fasting, binge eating—is common in college-aged women (11 –20 ). A documented increase in the number of young women experiencing eating psychopathology has been blamed on the rise of engagement with social media sites such as Facebook. We predicted that college-aged women's Facebook intensity (e.g., the amount of time spent on Facebook, number of Facebook friends, and integration of Facebook into daily life), online physical appearance comparison (i.e., comparing one's appearance to others' on social media), and online "fat talk" (i.e., talking negatively about one's body) would be positively associated with their disordered eating behavior. Methods In an online survey, 128 college-aged women (81.3 Caucasian, 6.7 Asian, 9.0 African-American, and 3.0 Other) completed items, which measured their disordered eating, Facebook intensity, online physical appearance comparison, online fat talk, body mass index, depression, anxiety, perfectionism, impulsivity, and self-efficacy. Results In regression analyses, Facebook intensity, online physical appearance comparison, and online fat talk were significantly and uniquely associated with disordered eating and explained a large percentage of the variance in disordered eating (60 ) in conjunction with covariates. However, greater Facebook intensity was associated with decreased disordered eating behavior, whereas both online physical appearance comparison and online fat talk were associated with greater disordered eating. Conclusions College-aged women who endorsed greater Facebook intensity were less likely to struggle with disordered eating when online physical appearance comparison was accounted for statistically. Facebook intensity may carry both risks and benefits for disordered eating. Research in computational epidemiology to date has concentrated on estimating summary statistics of populations and simulated scenarios of disease outbreaks. Detailed studies have been limited to small domains, as scaling the methods involved poses considerable challenges. By contrast, we model the associations of a large collection of social and environmental factors with the health of particular individuals. Instead of relying on surveys, we apply scalable machine learning techniques to noisy data mined from online social media and infer the health state of any given person in an automated way. We show that the learned patterns can be subsequently leveraged in descriptive as well as predictive fine-grained models of human health. Using a unified statistical model, we quantify the impact of social status, exposure to pollution, interpersonal interactions, and other important lifestyle factors on one's health. Our model explains more than 54 of the variance in people's health (as estimated from their online communication), and predicts the future health status of individuals with 91 accuracy. Our methods complement traditional studies in life sciences, as they enable us to perform large-scale and timely measurement, inference, and prediction of previously elusive factors that affect our everyday lives. Background: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. Methods: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, “can’t sleep”, Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. Results: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter ( P =.04), had fewer friends ( P <.001), and fewer followers ( P <.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets ( P <.001), indicating a possible relationship between sleep and pyschosocial issues. Conclusions: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered. [J Med Internet Res 2015;17(6):e140]