Sharing multimodal information (typically images, videos or text) in Social Network Sites (SNS) occupies a relevant part of our time. The particular way how users expose themselves in SNS can provide useful information to infer human behaviors. This paper proposes to use multimodal data gathered from Instagram accounts to predict the perceived prototypical needs described in Glasser's choice theory. The contribution is two-fold: (i) we provide a large multimodal database from Instagram public profiles (more than 30,000 images and text captions) annotated by expert Psychologists on each perceived behavior according to Glasser's theory, and (ii) we propose to automate the recognition of the (unconsciously) perceived needs by the users. Particularly, we propose a baseline using three different feature sets: visual descriptors based on pixel images (SURF and Visual Bag of Words), a high-level descriptor based on the automated scene description using Convolutional Neural Networks, and a text-based descriptor (Word2vec) obtained from processing the captions provided by the users. Finally, we propose a multimodal fusion of these descriptors obtaining promising results in the multi-label classification problem.
Different researchers have used SNS data to plan the path of disease occurrences @cite_11 @cite_45 . Predictive screening methods have also successfully found signs of mental health issues in social media data @cite_21 @cite_10 @cite_46 .
@cite_21 @cite_45 @cite_46 @cite_10 @cite_11
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Background: Social networking sites (SNSs) have the potential to increase the reach and efficiency of essential public health services, such as surveillance, research, and communication. Objective: The objective of this study was to conduct a systematic literature review to identify the use of SNSs for public health research and practice and to identify existing knowledge gaps. Methods: We performed a systematic literature review of articles related to public health and SNSs using PubMed, EMBASE, and CINAHL to search for peer-reviewed publications describing the use of SNSs for public health research and practice. We also conducted manual searches of relevant publications. Each publication was independently reviewed by 2 researchers for inclusion and extracted relevant study data. Results: A total of 73 articles met our inclusion criteria. Most articles (n=50) were published in the final 2 years covered by our search. In all, 58 articles were in the domain of public health research and 15 were in public health practice. Only 1 study was conducted in a low-income country. Most articles (63 73, 86 ) described observational studies involving users or usages of SNSs; only 5 studies involved randomized controlled trials. A large proportion (43 73, 59 ) of the identified studies included populations considered hard to reach, such as young individuals, adolescents, and individuals at risk of sexually transmitted diseases or alcohol and substance abuse. Few articles (2 73, 3 ) described using the multidirectional communication potential of SNSs to engage study populations. Conclusions: The number of publications about public health uses for SNSs has been steadily increasing in the past 5 years. With few exceptions, the literature largely consists of observational studies describing users and usages of SNSs regarding topics of public health interest. More studies that fully exploit the communication tools embedded in SNSs and study their potential to produce significant effects in the overall population’s health are needed. [J Med Internet Res 2014;16(3):e79] We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness. Abstract Introduction The authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a communicable disease that has not yet been formalized by national public health institutes and explicitly communicated to the general public. Most existing approaches to modeling infectious-disease-related knowledge discovery through social media networks are top-down approaches that are based on already known information, such as the names of diseases and their symptoms. In existing top-down approaches, necessary but unknown information, such as disease names and symptoms, is mostly unidentified in social media data until national public health institutes have formalized that disease. Most of the formalizing processes for latent infectious diseases are time consuming. Therefore, this study presents a bottom-up approach for latent infectious disease discovery in a given location without prior information, such as disease names and related symptoms. Methods Social media messages with user and temporal information are extracted during the data preprocessing stage. An unsupervised sentiment analysis model is then presented. Users’ expressions about symptoms, body parts, and pain locations are also identified from social media data. Then, symptom weighting vectors for each individual and time period are created, based on their sentiment and social media expressions. Finally, latent-infectious-disease-related information is retrieved from individuals’ symptom weighting vectors. Datasets and results Twitter data from August 2012 to May 2013 are used to validate this study. Real electronic medical records for 104 individuals, who were diagnosed with influenza in the same period, are used to serve as ground truth validation. The results are promising, with the highest precision, recall, and F 1 score values of 0.773, 0.680, and 0.724, respectively. Conclusion This work uses individuals’ social media messages to identify latent infectious diseases, without prior information, quicker than when the disease(s) is formalized by national public health institutes. In particular, the unsupervised machine learning model using user, textual, and temporal information in social media data, along with sentiment analysis, identifies latent infectious diseases in a given location. 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.