360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360 Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.Comment: QCRI Technical Repor

New Media & Youth Identity. Issues and Research Pathways

Media have held a considerable and growing place in the social environment of industrial society in recent decades, transforming the perception that a people have of their place in the world and of their memberships and belonging, creating new paths for social relations, affecting lifestyles, socialization, and communication processes, and the construction of identity itself. The relationship between young people (especially teenagers and adolescents) and new media shows some peculiarities which are worth further reflection to understand the extent and outcomes of these social changes. This article aims to investigate the discourse on youth identity and new media in the social science literature, determining which are the key trends and exploring the more relevant research questions about this theme and the way these topics relate to one another. Titles and abstracts of articles published during the period 2004 \u2013 2013 were selected from the Scopus social sciences database and they were analysed using different content analysis techniques supported by the T-Lab software. The international literature on these topics presents a certain liveliness and heterogeneity in themes and its perspectives on theoretical and empirical research. Nevertheless, it has been possible to identify some key trends, focusing mainly on the idea of active identity construction by new media

Scraping social media photos posted in Kenya and elsewhere to detect and analyze food types

Monitoring population-level changes in diet could be useful for education and for implementing interventions to improve health. Research has shown that data from social media sources can be used for monitoring dietary behavior. We propose a scrape-by-location methodology to create food image datasets from Instagram posts. We used it to collect 3.56 million images over a period of 20 days in March 2019. We also propose a scrape-by-keywords methodology and used it to scrape ∼30,000 images and their captions of 38 Kenyan food types. We publish two datasets of 104,000 and 8,174 image/caption pairs, respectively. With the first dataset, Kenya104K, we train a Kenyan Food Classifier, called KenyanFC, to distinguish Kenyan food from non-food images posted in Kenya. We used the second dataset, KenyanFood13, to train a classifier KenyanFTR, short for Kenyan Food Type Recognizer, to recognize 13 popular food types in Kenya. The KenyanFTR is a multimodal deep neural network that can identify 13 types of Kenyan foods using both images and their corresponding captions. Experiments show that the average top-1 accuracy of KenyanFC is 99% over 10,400 tested Instagram images and of KenyanFTR is 81% over 8,174 tested data points. Ablation studies show that three of the 13 food types are particularly difficult to categorize based on image content only and that adding analysis of captions to the image analysis yields a classifier that is 9 percent points more accurate than a classifier that relies only on images. Our food trend analysis revealed that cakes and roasted meats were the most popular foods in photographs on Instagram in Kenya in March 2019.Accepted manuscrip

"Notjustgirls": Exploring Male-related Eating Disordered Content across Social Media Platforms

Eating disorders (EDs) are a worldwide public health concern that impact approximately 10% of the U.S. population. Our previous research characterized these behaviors across online spaces. These characterizations have used clinical terminology, and their lexical variants, to identify ED content online. However, previous HCI research on EDs (including our own) suffers from a lack of gender and cultural diversity. In this paper, we designed a follow-up study of online ED characterizations, extending our previous methodologies to focus specifically on male/masculine-related content. We highlight the similarities and differences found in the terminology utilized and media archetypes associated with the social media content. Finally, we discuss other considerations highlighted through our analysis of the male-related content that is missing from the previous research

Artistic Expressions of Vegan Women with Disturbed Eating Behavior and Body Image Distress

This research explores the experience of women who are vegan, and have disturbed eating behaviors (DEB) and body image distress (BID). Four participants completed a series of three art-making sessions. Participants were invited to visually explore their experience as a vegan woman with DEB/BID. They made a mixed media collage with an emphasis on layering in each session. They engaged in discussion about their process, and the final art piece’s meaning. Between sessions, researcher response art pieces were created for each participant piece, with accompanying journal reflections to engage with the ideas they explored. All participant sessions were video and audio-recorded. Edited individual review videos were created for each participant. Participants attended a fourth session, during which they discussed the research process, their art, corresponding response art, and the video of their sessions. A final research summary video was created, and a final summary art piece was created. Qualitative analysis revealed Six Essential Ideas that characterized the women’s experience: re-claiming space, defining female, navigating food choices, vegan in context, identification and relationships with other animals and the environment, and disability as a vegan woman. A functional model of these six ideas, in relation to femaleness, veganism, and DEB/BID is presented to make meaning of the results. A set of theoretical models of the mechanisms between femaleness, veganism, and DEB/BID is proposed in response to the research question

A Novel Site-Agnostic Multimodal Deep Learning Model to Identify Pro-Eating Disorder Content on Social Media

Over the last decade, there has been a vast increase in eating disorder diagnoses and eating disorder-attributed deaths, reaching their zenith during the Covid-19 pandemic. This immense growth derived in part from the stressors of the pandemic but also from increased exposure to social media, which is rife with content that promotes eating disorders. Such content can induce eating disorders in viewers. This study aimed to create a multimodal deep learning model capable of determining whether a given social media post promotes eating disorders based on a combination of visual and textual data. A labeled dataset of Tweets was collected from Twitter, upon which twelve deep learning models were trained and tested. Based on model performance, the most effective deep learning model was the multimodal fusion of the RoBERTa natural language processing model and the MaxViT image classification model, attaining accuracy and F1 scores of 95.9% and 0.959 respectively. The RoBERTa and MaxViT fusion model, deployed to classify an unlabeled dataset of posts from the social media sites Tumblr and Reddit, generated similar classifications as previous research studies that did not employ artificial intelligence, showing that artificial intelligence can develop insights congruent to those of researchers. Additionally, the model was used to conduct a time-series analysis of yet unseen Tweets from eight Twitter hashtags, uncovering that the relative abundance of pro-eating disorder content has decreased drastically. However, since approximately 2018, pro-eating disorder content has either stopped its decline or risen once more in ampleness

Sharing the Pain in Social Media: A Content Analysis of #thinspiration Images on Instagram

#Thinspiration is an online trend that depicts thin-ideal media content specifically found on the social medium Instagram. The images found under the #thinspiration intend to inspire weight loss while encouraging and/or glorifying dangerous behaviors that are usually attributed to eating disorders including anorexia nervosa (AN), bulimia nervosa (BN) and eating disorder not otherwise specified (EDNOS). This study provides a content analysis of thinspiration imagery on the popular social networking site Instagram. A set of 300 randomly selected images was coded. Images tended to objectify women and sexualize them with a focus on bony and extremely thin women. Results seemed to point to harmful effects and users is the #thinspiration community view and contribute sexually suggestive content that objectifies females. Please be cautious when reading this paper as it includes media that could possibly be a trigger to those dealing with body image or eating disorders