Flaunting it on Facebook: Young adults, drinking cultures and the cult of celebrity

Copyright © Antonia Lyons; Tim McCreanor; Fiona Hutton; Ian Goodwin; Helen Moewaka Barnes; Christine Griffin; Kerryellen Vroman; Acushla Dee O’Carroll; Patricia Niland; Lina Samu Print publication available from: http://www.drinkingcultures.info/Young adults in Aotearoa/New Zealand (NZ) regularly engage in heavy drinking episodes with groups of friends within a collective culture of intoxication to ‘have fun’ and ‘be sociable’. This population has also rapidly increased their use of new social networking technologies (e.g. mobile camera/ video phones; Facebook and YouTube) and are said to be obsessed with identity, image and celebrity. This research project explored the ways in which new technologies are being used by a range of young people (and others, including marketers) in drinking practices and drinking cultures in Aotearoa/NZ. It also explored how these technologies impact on young adults’ behaviours and identities, and how this varies across young adults of diverse ethnicities (Maori [indigenous people of NZ], Pasifika [people descended from the Pacific Islands] and Pakeha [people of European descent]), social classes and genders. We collected data from a large and diverse sample of young adults aged 18-25 years employing novel and innovative methodologies across three data collection stages. In total 141 participants took part in 34 friendship focus group discussions (12 Pakeha, 12 Maori and 10 Pasifika groups) while 23 young adults showed and discussed their Facebook pages during an individual interview that involved screencapture software and video recordings. Popular online material regarding drinking alcohol was also collected (via groups, interviews, and web searches), providing a database of 487 links to relevant material (including websites, apps, and games). Critical and in-depth qualitative analyses across these multimodal datasets were undertaken. Key findings demonstrated that social technologies play a crucial role in young adults’ drinking cultures and processes of identity construction. Consuming alcohol to a point of intoxication was a commonplace leisure-time activity for most of the young adult participants, and social network technologies were fully integrated into their drinking cultures. Facebook was employed by all participants and was used before, during and following drinking episodes. Uploading and sharing photos on Facebook was particularly central to young people’s drinking cultures and the ongoing creation of their identities. This involved a great deal of Facebook ‘work’ to ensure appropriate identity displays such as tagging (the addition of explanatory or identifying labels) and untagging photos. Being visible online was crucial for many young adults, and they put significant amounts of time and energy into updating and maintaining Facebook pages, particularly with material regarding drinking practices and events. However this was not consistent across the sample, and our findings revealed nuanced and complex ways in which people from different ethnicities, genders and social classes engaged with drinking cultures and new technologies in different ways, reflecting their positioning within the social structure. Pakeha shared their drinking practices online with relatively little reflection, while Pasifika and Maori participants were more likely to discuss avoiding online displays of drinking and demonstrated greater reflexive self-surveillance. Females spoke of being more aware of normative expectations around gender than males, and described particular forms of online identity displays (e.g. moderated intake, controlled selfdetermination). Participants from upper socio-economic groups expressed less concern than others about both drinking and posting material online. Celebrity culture was actively engaged with, in part at least, as a means of expressing what it is to be a young adult in contemporary society, and reinforcing the need for young people to engage in their own everyday practices of ‘celebritising’ themselves through drinking cultures online. Alcohol companies employed social media to market their products to young people in sophisticated ways that meant the campaigns and actions were rarely perceived as marketing. Online alcohol marketing initiatives were actively appropriated by young people and reproduced within their Facebook pages to present tastes and preferences, facilitate social interaction, construct identities, and more generally develop cultural capital. These commercial activities within the commercial platforms that constitute social networking systems contribute heavily to a general ‘culture of intoxication’ while simultaneously allowing young people to ‘create’ and ‘produce’ themselves online via the sharing of consumption ‘choices’, online interactions and activities

Persuasive system design does matter: a systematic review of adherence to web-based interventions

Background: Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. Objective: This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. Methods: We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. Results: We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Conclusions: Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adher

Detecting Mental Distresses Using Social Behavior Analysis in the Context of COVID-19: A Survey

Online social media provides a channel for monitoring people\u27s social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published since the COVID-19 outbreak. We analyze relevant research and its characteristics and propose new approaches to organizing the large amount of studies arising from this emerging research area, thus drawing new views, insights, and knowledge for interested communities. Specifically, we first classify the studies in terms of feature extraction types, language usage patterns, aesthetic preferences, and online behaviors. We then explored various methods (including machine learning and deep learning techniques) for detecting mental health problems. Building upon the in-depth review, we present our findings and discuss future research directions and niche areas in detecting mental health problems using social media data. We also elaborate on the challenges of this fast-growing research area, such as technical issues in deploying such systems at scale as well as privacy and ethical concerns

Large-Scale Sleep Condition Analysis Using Selfies from Social Media

Sleep condition is closely related to an individual's health. Poor sleep conditions such as sleep disorder and sleep deprivation affect one's daily performance, and may also cause many chronic diseases. Many efforts have been devoted to monitoring people's sleep conditions. However, traditional methodologies require sophisticated equipment and consume a significant amount of time. In this paper, we attempt to develop a novel way to predict individual's sleep condition via scrutinizing facial cues as doctors would. Rather than measuring the sleep condition directly, we measure the sleep-deprived fatigue which indirectly reflects the sleep condition. Our method can predict a sleep-deprived fatigue rate based on a selfie provided by a subject. This rate is used to indicate the sleep condition. To gain deeper insights of human sleep conditions, we collected around 100,000 faces from selfies posted on Twitter and Instagram, and identified their age, gender, and race using automatic algorithms. Next, we investigated the sleep condition distributions with respect to age, gender, and race. Our study suggests among the age groups, fatigue percentage of the 0-20 youth and adolescent group is the highest, implying that poor sleep condition is more prevalent in this age group. For gender, the fatigue percentage of females is higher than that of males, implying that more females are suffering from sleep issues than males. Among ethnic groups, the fatigue percentage in Caucasian is the highest followed by Asian and African American.Comment: 2017 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS'17

Investigating Bias in Facial Analysis Systems: A Systematic Review

© 2013 IEEE. Recent studies have demonstrated that most commercial facial analysis systems are biased against certain categories of race, ethnicity, culture, age and gender. The bias can be traced in some cases to the algorithms used and in other cases to insufficient training of algorithms, while in still other cases bias can be traced to insufficient databases. To date, no comprehensive literature review exists which systematically investigates bias and discrimination in the currently available facial analysis software. To address the gap, this study conducts a systematic literature review (SLR) in which the context of facial analysis system bias is investigated in detail. The review, involving 24 studies, additionally aims to identify (a) facial analysis databases that were created to alleviate bias, (b) the full range of bias in facial analysis software and (c) algorithms and techniques implemented to mitigate bias in facial analysis