Social Network Data Collection Research Articles

The Belfast Youth Development Study (BYDS): A prospective cohort study of the initiation, persistence and desistance of substance use from adolescence to adulthood in Northern Ireland.

BackgroundSubstance misuse persists as a major public health issue worldwide with significant costs for society. The development of interventions requires methodologically sound studies to explore substance misuse causes and consequences. This Cohort description paper outlines the design of the Belfast Youth Development (BYDS), one of the largest cohort studies of its kind in the UK. The study was established to address the need for a long-term prospective cohort study to investigate the initiation, persistence and desistance of substance use, alongside life course processes in adolescence and adulthood. The paper provides an overview of BYDS as a longitudinal data source for investigating substance misuse and outlines the study measures, sample retention and characteristics. We also outline how the BYDS data have been used to date and highlight areas ripe for future work by interested researchers.MethodsThe study began in 2000/1 when participants (n = 3,834) were pupils in their first year of post-primary education (age 10/11 years, school year 8) from over 40 schools in Northern Ireland. Children were followed during the school years: Year 9 (in 2002; aged 12; n = 4,343), Year 10 (in 2003; aged 13; n = 4,522), Year 11 (in 2004; aged 14; n = 3,965) and Year 12 (in 2005; aged 15; n = 3,830) and on two more occasions: 2006/07 (aged 16/17; n = 2,335) and 2010/11 (aged 20/21; n = 2,087). Data were collected on substance use, family, schools, neighbourhoods, offending behaviour and mental health. The most novel aspect of the study was the collection of detailed social network data via friendship nominations allowing the investigation of the spread of substance use via friendship networks. In 2004 (school year 11; respondents aged 14), a sub-sample of participants’ parents (n = 1,097) and siblings (n = 211) also completed measures on substance use and family dynamics.ResultsThe most recent wave (in 2010/2011; respondents aged 20/21 years) indicated lifetime use of alcohol, tobacco and cannabis among the cohort was 94, 70 and 45 per cent, respectively. The paper charts the development of drug use behaviour and some of the key results to date are presented. We have also identified a number of key areas ripe for analysis by interested researchers including sexual health and education.ConclusionsWe have established a cohort with detailed data from adolescence to young adulthood, supplemented with parent and sibling reports and peer network data. The dataset, allowing for investigation of trajectories of adolescent substance use, associated factors and subsequent long-term outcomes, constitutes an important resource for longitudinal substance misuse research. A planned further wave as the cohort enter their late twenties and potential to link to administrative data sources, will further enrich the datasets.

An Interactive, Mobile-Based Tool for Personal Social Network Data Collection and Visualization Among a Geographically Isolated and Socioeconomically Disadvantaged Population: Early-Stage Feasibility Study With Qualitative User Feedback.

BackgroundPersonal social networks have a profound impact on our health, yet collecting personal network data for use in health communication, behavior change, or translation and dissemination interventions has proved challenging. Recent advances in social network data collection software have reduced the burden of network studies on researchers and respondents alike, yet little testing has occurred to discover whether these methods are: (1) acceptable to a variety of target populations, including those who may have limited experience with technology or limited literacy; and (2) practical in the field, specifically in areas that are geographically and technologically disconnected, such as rural Appalachian Kentucky.ObjectiveWe explored the early-stage feasibility (Acceptability, Demand, Implementation, and Practicality) of using innovative, interactive, tablet-based network data collection and visualization software (OpenEddi) in field collection of personal network data in Appalachian Kentucky.MethodsA total of 168 rural Appalachian women who had previously participated in a study on the use of a self-collected vaginal swab (SCVS) for human papillomavirus testing were recruited by community-based nurse interviewers between September 2013 and August 2014. Participants completed egocentric network surveys via OpenEddi, which captured social and communication network influences on participation in, and recruitment to, the SCVS study. After study completion, we conducted a qualitative group interview with four nurse interviewers and two participants in the network study. Using this qualitative data, and quantitative data from the network study, we applied guidelines from Bowen et al to assess feasibility in four areas of early-stage development of OpenEddi: Acceptability, Demand, Implementation, and Practicality. Basic descriptive network statistics (size, edges, density) were analyzed using RStudio.ResultsOpenEddi was perceived as fun, novel, and superior to other data collection methods or tools. Respondents enjoyed the social network survey component, and visualizing social networks produced thoughtful responses from participants about leveraging or changing network content and structure for specific health-promoting purposes. Areas for improved literacy and functionality of the tool were identified. However, technical issues led to substantial (50%) data loss, limiting the success of its implementation from a researcher’s perspective, and hindering practicality in the field.ConclusionsOpenEddi is a promising data collection tool for use in geographically isolated and socioeconomically disadvantaged populations. Future development will mitigate technical problems, improve usability and literacy, and test new methods of data collection. These changes will support goals for use of this tool in the delivery of network-based health communication and social support interventions to socioeconomically disadvantaged populations.

Methods for Collection of Participant-aided Sociograms for the Study of Social, Sexual and Substance-using Networks Among Young Men Who Have Sex with Men.

In this study, we adapted and tested a participant-aided sociogram approach for the study of the social, sexual, and substance use networks of young men who have sex with men (YMSM); a population of increasing and disproportionate risk of HIV infection. We used a combination of two interviewer-administered procedures: completion of a pre-numbered list form to enumerate alters and to capture alter attributes; and a participant-aided sociogram to capture respondent report of interactions between alters on an erasable whiteboard. We followed the collection of alter interactions via the sociogram with a traditional matrix-based tie elicitation approach for a sub-sample of respondents for comparison purposes. Digital photographs of each network drawn on the whiteboard serve as the raw data for entry into a database in which group interactions are stored. Visual feedback of the network was created at the point of data entry, using NetDraw network visualization software for comparison to the network structure elicited via the sociogram. In a sample of 175 YMSM, we found this approach to be feasible and reliable, with high rates of participation among those eligible for the study and substantial agreement between the participant-aided sociogram in comparison to a traditional matrix-based approach. We believe that key strengths of this approach are the engagement and maintenance of participant attention and reduction of participant burden for alter tie elicitation. A key weakness is the challenge of entry of interview-based list form and sociogram data into the database. Our experience suggests that this approach to data collection is feasible and particularly appropriate for an adolescent and young adult population. This builds on and advances visualization-based approaches to social network data collection.

Epidemiology of Alcohol Abuse in Iran

Background: World Health Organization report in 2014 indicates a considerable range of alcohol consumption across the world which had a growing trend during last 5 years. A study on 5,231 students in 57 universities has shown that alcohol consumption became more common than other drugs, and alcohol is in the first rank for repeated usage. In another study in 2010, the prevalence of alcohol use was 15.1% among high school students in Tehran with predominant use among boys. This study was aimed to estimate the hidden population of alcohol consumer, commissioned by the Bureau of Mental and Social health and Addiction, Ministry of Health of Iran. Methods: In 2012, Iranian Ministry of Health has implemented a national program for estimation of the frequency of alcohol abusers from the hidden and high risk population by an indirect method. The method of network extension, social network measurement and data collection tool were done through questionnaire. Results: The consumption of alcohol is more than other substances in the country and it is estimated to involve 1,583,752 individuals. This was observed in all of the provinces and included more than 2,000 individuals per 100,000 population which consumed alcohol at least once in the past year. In young population, 1,063,557 people have consumed alcohol at least once in the previous year, which represents an incidence of 5%. Consumption in men aged 18 to 30 years is higher than the other age-groups. In terms of geographical separation, Isfahan, Fars, Tehran, Kurdistan, Zanjan, Yazd and Golestan provinces are at a very high risk. Conclusion: Alcohol is a serious health problem in our society. Based on higher rate of consumption in men and younger population, there must be higher attention. Adjusting systems for alcohol screening and treatment policies should be prioritized. In addition, studies to estimate the knowledge, attitude and practice of alcohol abusers are required.

Proximity data-loggers increase the quantity and quality of social network data

Social network analysis is an ideal quantitative tool for advancing our understanding of complex social behaviour. However, this approach is often limited by the challenges of accurately characterizing social structure and measuring network heterogeneity. Technological advances have facilitated the study of social networks, but to date, all such work has focused on large vertebrates. Here, we provide proof of concept for using proximity data-logging to quantify the frequency of social interactions, construct weighted networks and characterize variation in the social behaviour of a lek-breeding bird, the wire-tailed manakin, Pipra filicauda. Our results highlight how this approach can ameliorate the challenges of social network data collection and analysis by concurrently improving data quality and quantity.

Estimating network structure via random sampling: Cognitive social structures and the adaptive threshold method

This paper introduces and tests a novel methodology for measuring networks. Rather than collecting data to observe a network or several networks in full, which is typically costly or impossible, we randomly sample a portion of individuals in the network and estimate the network based on the sampled individuals’ perceptions on all possible ties. We find the methodology produces accurate estimates of social structure and network level indices in five different datasets. In order to illustrate the performance of our approach we compare its results with the traditional roster and ego network methods of data collection. Across all five datasets, our methodology outperforms these standard social network data collection methods. We offer ideas on applications of our methodology, and find it especially promising in cross-network settings.

Social networks: Evolving graphs with memory dependent edges

The plethora of digital communication technologies, and their mass take up, has resulted in a wealth of interest in social network data collection and analysis in recent years. Within many such networks the interactions are transient: thus those networks evolve over time. In this paper we introduce a class of models for such networks using evolving graphs with memory dependent edges, which may appear and disappear according to their recent history. We consider time discrete and time continuous variants of the model. We consider the long term asymptotic behaviour as a function of parameters controlling the memory dependence. In particular we show that such networks may continue evolving forever, or else may quench and become static (containing immortal and/or extinct edges). This depends on the existence or otherwise of certain infinite products and series involving age dependent model parameters. We show how to differentiate between the alternatives based on a finite set of observations. To test these ideas we show how model parameters may be calibrated based on limited samples of time dependent data, and we apply these concepts to three real networks: summary data on mobile phone use from a developing region; online social-business network data from China; and disaggregated mobile phone communications data from a reality mining experiment in the US. In each case we show that there is evidence for memory dependent dynamics, such as that embodied within the class of models proposed here.

A foundation for developing a methodology for social network sampling

Researchers are increasingly turning to network theory to understand the social nature of animal populations. We present a computational framework that is the first step in a series of works that will allow us to develop a quantitative methodology of social network sampling to aid ecologists in their social network data collection. To develop our methodology, we need to be able to generate networks from which to sample. Ideally, we need to perform a systematic study of sampling protocols on different known network structures, as network structure might affect the robustness of any particular sampling methodology. Thus, we present a computational tool for generating network structures that have user-defined distributions for network properties and for key measures of interest to ecologists. The user defines the values of these measures and the tool will generate appropriate network randomizations with those properties. This tool will be used as a framework for developing a sampling methodology, although we do not present a full methodology here. We describe the method used by the tool, demonstrate its effectiveness, and discuss how the tool can now be utilized. We provide a proof-of-concept example (using the assortativity measure) of how such networks can be used, along with a simulated egocentric sampling regime, to test the level of equivalence of the sampled network to the actual network.

To tell the truth: Measuring concordance in multiply reported network data

Social network data must accurately reflect actors’ relationships to properly estimate network features. Here, we examine multiple reports of sexual, drug-sharing and social tie data on high-risk networks in Colorado Springs. By comparing multiple reports on the same ties, we can evaluate the reliability of this study's network data. Our findings suggest that these data have a high level of reporting agreement. From these findings, we discuss implications for analysis of these and similar data and provide suggestions for future social network data collection efforts.

Social network influences on reproductive health behaviors in urban northern Thailand

Prevention approaches for reproductive health have evolved from an emphasis on individually focused models of behavior change to a recognition that risk reduction occurs within a context of social norms. Prevention programs can be improved by understanding how social structure influences sexual behavior and using that understanding to develop strategies for positive change. In a dynamic, urban context, communities are better conceptualized as informal networks of ties. These network structures may help to protect, or conversely, expose members to reproductive risk behaviors. Using data from a study of social and sexual networks conducted in northern Thailand, this article describes partner relations and social structure in the modern, urban context, and illustrates the links between individual, relational and structural properties and reproductive risk behaviors. Triangulation of ethnographic, survey and social network data collection and analytic tools provide an opportunity to interpret individual behaviors, meanings of relationships and structural properties of networks. Intervention approaches should build on existing networks, and address the complex meanings of romantic and sexual partnerships.