Young people's online communication and its association with mental well-being: results from the 2019 student health and well-being survey.

BACKGROUND: Online communication has become an integral aspect of daily life for young people internationally. Very little research has examined whether the association between social media use and well-being depends on who young people engage with (i.e. real, or virtual friendships). METHODS: Data were drawn from a subsample of students (N = 38,736) who took part in the School Health Research Network (SHRN) 2019 Student Health and Well-being (SHW) survey. A series of multivariable regression models were used to assess the association between who adolescents were communicating with online and well-being, controlling for confounders: passive social media use; friendship quality; and cyberbullying. We also tested whether these associations were modified based on gender. RESULTS: Students are highly engaged on social networking sites, and these sites are used to communicate with existing friendship groups and develop virtual friendships. Frequent online communication with best friends (b = .340, p < .001) and bigger friendship groups (b = .397; p < .001) was associated with higher levels of well-being. However, the frequency of online contact with virtual friends made online was negatively and significantly associated with well-being (b = -.760; p < .001), with a larger negative association for girls than boys. CONCLUSIONS: Online communication with virtual friendship networks were associated with lower mental well-being, with stronger associations for girls than boys; however, frequent online communication with 'real' friends was associated with better well-being. Our results indicate the importance of considering the nature of adolescent online communication, rather than just its quantity, in developing interventions to improve adolescent well-being.

Increases in Serious Psychological Distress among Ontario Students between 2013 and 2017: Assessing the Impact of Time Spent on Social Media.

OBJECTIVE: The objective of the current research was to examine the association between time spent on social media and serious psychological distress between 2013 and 2017, a period when the rates of both were trending upward. METHODS: The current study analyzed population-based data from 3 waves of the Ontario Student Drug Use and Health Survey (N = 15,398). Multivariate logistic regression models were used to examine the association between time spent on social media and serious psychological distress controlling for theoretically relevant covariates. Interactions were tested to assess whether the association changed over time. RESULTS: The prevalence of serious psychological distress increased from 10.9% in 2013 to 16.8% in 2017 concomitantly with substantial increases in social media usage, especially at the highest levels. In the multivariate context, we found a significant interaction between social media use and the survey year which indicates that the association between time spent on social media and psychological distress has decreased from 2013 to 2017. CONCLUSION: Although both social media use and psychological distress increased between 2013 and 2017, the interaction between these variables indicates that the strength of this association has decreased over time. This finding suggests that the higher rate of heavy social media use in 2017 compared to 2013 is not actually associated with the higher rate of serious psychological distress during the same time period. From a diffusion of innovation perspective, it is possible that more recent adopters of social media may be less prone to psychological distress. More research is needed to understand the complex and evolving association between social media use and psychological distress. Researchers attempting to isolate the factors associated with the recent increases in psychological distress could benefit from broadening their investigation to factors beyond time spent on social media.

Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users' Views, Online Context and Algorithmic Estimation.

New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist's data diet. In particular, some researchers see an advantage in the perceived 'public' nature of Twitter posts, representing them in publications without seeking informed consent. While such practice may not be at odds with Twitter's terms of service, we argue there is a need to interpret these through the lens of social science research methods that imply a more reflexive ethical approach than provided in 'legal' accounts of the permissible use of these data in research publications. To challenge some existing practice in Twitter-based research, this article brings to the fore: (1) views of Twitter users through analysis of online survey data; (2) the effect of context collapse and online disinhibition on the behaviours of users; and (3) the publication of identifiable sensitive classifications derived from algorithms.

Can't Count or Won't Count? Embedding Quantitative Methods in Substantive Sociology Curricula: A Quasi-Experiment.

This paper reports on a quasi-experiment in which quantitative methods (QM) are embedded within a substantive sociology module. Through measuring student attitudes before and after the intervention alongside control group comparisons, we illustrate the impact that embedding has on the student experience. Our findings are complex and even contradictory. Whilst the experimental group were less likely to be distrustful of statistics and appreciate how QM inform social research, they were also less confident about their statistical abilities, suggesting that through 'doing' quantitative sociology the experimental group are exposed to the intricacies of method and their optimism about their own abilities is challenged. We conclude that embedding QM in a single substantive module is not a 'magic bullet' and that a wider programme of content and assessment diversification across the curriculum is preferential.

Examining household effects on individual Twitter adoption: A multilevel analysis based on U.K. household survey data.

Previous studies mainly focused on individual-level factors that influence the adoption and usage of mobile technology and social networking sites, with little emphasis paid to the influences of household situations. Using multilevel modelling approach, this study merges household- (n1 = 1,455) and individual-level (n2 = 2,570) data in the U.K. context to investigate (a) whether a household economic capital (HEC) can affect its members' Twitter adoption, (b) whether the influences are mediated by the member's activity variety and self-reported efficacy with mobile technology, and (c) whether the members' traits, including educational level, gross income and residential area, moderate the relationship between HEC and Twitter adoption. Significant direct and indirect associations were discovered between HEC and its members' Twitter adoption. The educational level and gross income of household members moderated the influence of HEC on individuals' Twitter adoption.

Epicosm-a framework for linking online social media in epidemiological cohorts.

MOTIVATION: Social media represent an unrivalled opportunity for epidemiological cohorts to collect large amounts of high-resolution time course data on mental health. Equally, the high-quality data held by epidemiological cohorts could greatly benefit social media research as a source of ground truth for validating digital phenotyping algorithms. However, there is currently a lack of software for doing this in a secure and acceptable manner. We worked with cohort leaders and participants to co-design an open-source, robust and expandable software framework for gathering social media data in epidemiological cohorts. IMPLEMENTATION: Epicosm is implemented as a Python framework that is straightforward to deploy and run inside a cohort's data safe haven. GENERAL FEATURES: The software regularly gathers Tweets from a list of accounts and stores them in a database for linking to existing cohort data. AVAILABILITY: This open-source software is freely available at [https://dynamicgenetics.github.io/Epicosm/].

The mental health and well-being profile of young adults using social media.

The relationship between mental health and social media has received significant research and policy attention. However, there is little population representative data about who social media users are which limits understanding of confounding factors between mental health and social media. Here we profile users of Facebook, Twitter, Instagram, Snapchat and YouTube from the Avon Longitudinal Study of Parents and Children population cohort (N=4,083). We provide estimates of demographics and mental health and well-being outcomes by platform. We find that users of different platforms and frequencies are not homogeneous. User groups differ primarily by sex and YouTube users are the most likely to have poorer mental health outcomes. Instagram and Snapchat users tend to have higher well-being than the other social media sites considered. Relationships between use-frequency and well-being differ depending on the specific well-being construct measured. The reproducibility of future research may be improved by stratifying by sex and being specific about the well-being constructs used.

Linking Survey and Twitter Data: Informed Consent, Disclosure, Security, and Archiving.

Linked survey and Twitter data present an unprecedented opportunity for social scientific analysis, but the ethical implications for such work are complex-requiring a deeper understanding of the nature and composition of Twitter data to fully appreciate the risks of disclosure and harm to participants. In this article, we draw on our experience of three recent linked data studies, briefly discussing the background research on data linkage and the complications around ensuring informed consent. Particular attention is paid to the vast array of data available from Twitter and in what manner it might be disclosive. In light of this, the issues of maintaining security, minimizing risk, archiving, and reuse are applied to linked Twitter and survey data. In conclusion, we reflect on how our ability to collect and work with Twitter data has outpaced our technical understandings of how the data are constituted and observe that understanding one's data is an essential prerequisite for ensuring best ethical practice.

Views on social media and its linkage to longitudinal data from two generations of a UK cohort study.

Background: Cohort studies gather huge volumes of information about a range of phenotypes but new sources of information such as social media data are yet to be integrated. Participant's long-term engagement with cohort studies, as well as the potential for their social media data to be linked to other longitudinal data, could provide novel advances but may also give participants a unique perspective on the acceptability of this growing research area. Methods: Two focus groups explored participant views towards the acceptability and best practice for the collection of social media data for research purposes. Participants were drawn from the Avon Longitudinal Study of Parents and Children cohort; individuals from the index cohort of young people (N=9) and from the parent generation (N=5) took part in two separate 90-minute focus groups. The discussions were audio recorded and subjected to qualitative analysis. Results: Participants were generally supportive of the collection of social media data to facilitate health and social research. They felt that their trust in the cohort study would encourage them to do so. Concern was expressed about the collection of data from friends or connections who had not consented. In terms of best practice for collecting the data, participants generally preferred the use of anonymous data derived from social media to be shared with researchers. Conclusion: Cohort studies have trusting relationships with their participants; for this relationship to extend to linking their social media data with longitudinal information, procedural safeguards are needed. Participants understand the goals and potential of research integrating social media data into cohort studies, but further research is required on the acquisition of their friend's data. The views gathered from participants provide important guidance for future work seeking to integrate social media in cohort studies.

Who Tweets with Their Location? Understanding the Relationship between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter.

In this paper we take advantage of recent developments in identifying the demographic characteristics of Twitter users to explore the demographic differences between those who do and do not enable location services and those who do and do not geotag their tweets. We discuss the collation and processing of two datasets-one focusing on enabling geoservices and the other on tweet geotagging. We then investigate how opting in to either of these behaviours is associated with gender, age, class, the language in which tweets are written and the language in which users interact with the Twitter user interface. We find statistically significant differences for both behaviours for all demographic characteristics, although the magnitude of association differs substantially by factor. We conclude that there are significant demographic variations between those who opt in to geoservices and those who geotag their tweets. Not withstanding the limitations of the data, we suggest that Twitter users who publish geographical information are not representative of the wider Twitter population.

Who tweets? Deriving the demographic characteristics of age, occupation and social class from twitter user meta-data.

This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect "signatures" of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.