Social Connectedness: Measurement, Determinants, and Effects

In the social media era, the ever-increasing utility of Online Social Networks (OSN) services provide a variety of benefits to users, organizations, and service providers. However, OSN services also introduce new threats and privacy issues regarding the data they are dealing with. For instance, in a reliable OSN service, a user should be able to set up his desired level of information sharing and securely manage sensitive data. Currently, few approaches exist that can model OSNs for the purpose, let alone a model the effects that attackers can have on these networks. In this work a novel OSN modeling approach is presented to fill the gap. This model is based on an innovative game-theoretic approach and it is analyzed both from a theoretical and simulation-oriented view. The game-theoretic model is implemented to analyze several attack scenarios. Honeytokens, which are an information security tool based upon deception, are defined and identified as a security tool that could help in OSN security. As the results show, there are several scenarios where OSN services are very vulnerable and hence more protection mechanisms should be provided to secure the data contained across these networks, including the use of honeytokens. In this work we introduce a novel OSN modeling approach for optimal data sharing based on innovative game theories, considering the states/optimal policies of data sharing on OSNs and possible confrontations between the attacker and the user. After we develop the theoretical framework, we conduct experiments, integrating our ideas with honeytokens in several attack scenarios. Finally, we analyze our experimental results and discuss recommendations based on the results.

With the widespread adoption of cloud computing, it is increasingly popular for online social network (OSN) service providers to leverage the public cloud as a back-end to manage their services for the cloud’s well-understood benefits. However, the cloud is also notoriously subject to a wide attack surface, making it an imperative need to embed security in the cloud-backed OSN service from the very beginning. In light of this, in this paper, we design, implement, and evaluate PeGraph, the first system simultaneously allowing private, efficient, and rich queries over encrypted social graphs. PeGraph is aimed at safeguarding the confidentiality of the social graph at the cloud, while preserving the functionality of social search, a key enabler for quality OSN services like friend discovery and user targeting. PeGraph is built from a delicate synergy of insights from social graph modelling and lightweight cryptography such as searchable encryption and additive secret sharing, supporting rich social search queries like exact queries, fuzzy queries, and mixed queries. PeGraph also allows the cloud to obliviously render the encrypted social search results in a ranked order according to their importance, as per users’ preferences. Extensive experiments demonstrate that PeGraph can securely process a wide range of practical social search queries within 1 second, over a real-world social graph consisting of millions of entities.

ABSTRACTTwitter Sentiment Geographical Index (TSGI) has been proposed to complement traditional surveys to measure subjective well‐being (SWB) at the US county level. Our study aims to investigate determinants of TSGI‐measured SWB in rural and urban US counties. Using the Classification and Regression Tree, we identified phenotypes or county‐level characteristics associated with high SWB. Counties with newer homes were the top characteristic of high SWB in both urban and rural areas. Counties of the identical phenotypes tend to concentrate geographically, with the most favorable phenotypes clustered in the South. Random Forest analysis identified additional characteristics of high SWB, including higher population density in rural areas and lower real estate tax ratio in urban areas. Our results yield a comprehensive understanding of determinants of SWB at the local level, guiding evidence‐based policy decisions and community initiatives to improve well‐being in target populations.

ABSTRACTDuring the period of the Great Recession, previous research has found that women-owned firms were less likely to lay-off workers than were firms owned by men. Given that the individual firm behaviour has a cumulative effect on regional economic performance, we expect greater stability across those regions with a larger share of women-owned and managed businesses. We test this hypothesis using US county data during the period from 2007 to 2013 at the US county level. Consistent with the findings of Matsa and Miller, our results suggest that regional economic stability increases with the share of women-owned and managed establishments.

BackgroundWorldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contacttracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States.ObjectiveThe purpose of this study was to create a granular NPI data set at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases.MethodsUsing a standardized crowdsourcing methodology, we collected time-series data on 7 key NPIs for 1320 US counties.ResultsThis open-source data set is the largest and most comprehensive collection of county NPI policy data and meets the need for higher-resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (P<.001). We identified a correlation between workplace closures and lower growth rates of COVID-19 cases (P=.004). We found weak correlations between shelter-in-place enforcement and measures of Democratic local voter proportion (R=0.21) and elected leadership (R=0.22).ConclusionsThis study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.

Social networks and geographic structures of communities are important predictors of infectious disease transmission. To examine their joint effects on diarrheal disease and how these effects might develop, the authors analyzed social network and geographic data from northern coastal Ecuador and examined associations with diarrhea prevalence. Between July 2003 and May 2005, 113 cases of diarrhea were identified in nine communities. Concurrently, sociometric surveys were conducted, and households were mapped with geographic information systems. Spatial distribution metrics of households within communities and of communities with respect to roads were developed that predict social network degree in casual contact ("contact") and food-sharing ("food") networks. The mean degree is 25-40% lower in communities with versus without road access and 66-94% lower in communities with lowest versus highest housing density. Associations with diarrheal disease were found for housing density (comparing dense with dispersed communities: risk ratio = 3.3, 95% confidence interval (CI): 1.1, 10.0) and social connectedness (comparing lowest with highest degree communities: risk ratio = 3.4, 95% CI: 1.1, 10.1 in the contact network and risk ratio = 4.9, 95% CI: 1.1, 21.9 in the food network). Some of these differences may be related to more new residents, lower housing density, and less social connectedness in road communities.

Beyond the intrinsic pleasure that people derive from spending time with others, social connections have positive spill-over effects for individual and societal well-being. People with extensive and supportive networks have better health, tend to live longer, and are more likely to be employed. At a society-wide level, social connections can generate shared values – such as trust in others and norms of reciprocity – which influence a range of outcomes, including economic growth, democratic participation and crime. The indicators used in this chapter to measure different aspects of social connections refer to social network support and to the frequency of social contact. Overall, personal social networks are relatively strong in OECD countries, with most people seeing friends and/or relatives on a regular basis and reporting that they have someone to count on in times of need. However, there are significant differences between different socio-economic and demographic groups, with the old, the poor and the less-educated having weaker social support networks. There are also wide crosscountry differences in levels of interpersonal trust – one key indicator of the outcomes of social connections. Measuring social connections remains challenging, however, and more work is needed to develop comparable measures in this field.

Social support networks have been become one of the mostly influential metaphors in many areas around the world, especially in terms of mitigating disaster consequences and revivifying disaster affected livelihoods. More importantly, reciprocal supports ties are more powerful in healing disaster wounds of communities. Especially social capital legacies enrich by reciprocal support networks by the ways in which making may hopes among disaster victims. In this context, the objective of this study is to investigate the social support network behaviors in supporting 16 flood-affected households in the mass flooding event occurred in 2017. Particularly, the study researched social support network behaviors at different flood inundation phases such as before, during, and after. This study collected primary data (mainly social network data) using household survey and filed observations. The study used the Social Network Analysis (SNA) method for the network data analysis. This article shows that flood affected households have received social network supports in different magnitudes at before, during, and after flood inundation phases. More importantly, provision of foods, water and basic needs, sheltering, clearing contaminated households, and emotional supports have mobilized and reciprocated among victims in reviving their livelihoods. The social support network legacies have evolved at different flood inundation phases. This study fills the gabs in the flood disaster discourse on Sri Lankan context.

This paper examines the intergenerational mobility trajectory (class) effects on social connection and, through this, on subjective well-being in contemporary UK society. Drawing on data from the United Kingdom Household Longitudinal Study, we measured four types of formal and informal social network (civic engagement, neighbourhood cohesion, diversity and size of social networks) and used three indictors for well-being. We find that social network does play a significant role on well-being but the impact is much smaller than that of class. We also find that class is more closely related to the formal than the informal domains of social network. Demographic attributes show some influences but social network, employment, and prior levels of well-being in particular, have more salient effects on well-being. Over and above all this, class as an indicator of cumulative advantages and disadvantages has a persistent and systematic influence. Overall, the analysis shows that while enriching social connection would contribute to well-being, reducing class-based inequality is of greater importance.

ABSTRACTThis study explores the interplay between time pressures at home and at work, social connectedness, and well-being as reported by Australian women. Specifically, taking advantage of longitudinal data (from the Household, Income, and Labour Dynamics in Australia survey for the years 2001 to 2013) and employing the newly developed “blow up and cluster” estimation technique, this study finds there has been a marginal decline in the life satisfaction of Australian women. After accounting for changes in sociodemographic characteristics, a more pervasive negative trend in life satisfaction appears to be reported by both men and women, and both genders report higher levels of life satisfaction for greater levels of almost all measures of social connectedness. This study adds to a growing body of evidence pointing toward the importance of frequent and meaningful social connections to societal well-being, as well as the need to refocus attention on well-being in public-policy spheres.

IntroductionHigher levels of social connectedness are associated with better physical and mental health outcomes, but measures of connectedness are often study specific. Prior research has distinguished between perceived and received (quantifiable) measures of social connectedness, with differing impacts on health, sometimes mediated by place of residence. This analysis investigated the relationship between perceptions of social support/connection and quantifiable measures of social encounters, by neighbourhood, to inform understanding of place-based differences in connectedness and health outcomes.MethodsNegative binomial regression models were used to determine associations between perceptions of social connectedness (perceived community connections and social involvement) and the number of recorded daily social encounters as a proxy for received support/connectedness. Analyses were undertaken across two Local Government Areas (LGAs) in Melbourne with disparate socio-economic profiles to examine potential modification of social connectedness measures by neighbourhood of residence.ResultsTwo measures of perceived connectedness had a clear relationship with recorded daily social encounters–feeling a sense of community belonging (RR 1.20 (1.04, 1.37), p = 0.010) and having family or friends close by (RR 1.30 (1.10,1.54), p = 0.002 “neither” compared to “disagree”, (RR 1.15 (1.04, 1.26), p = 0.006 “agree” compared to “disagree”). Involvement in a local church, sporting or social club was associated with a greater number of daily social encounters for respondents who participated a few times a year (RR 1.17 (1.05,1.32), p = 0.006) or often (RR 1.23 (1.12,1.36), p<0.001) compared to never. In the less affluent LGA, active contributions to neighbours and community through assistance and volunteering were a frequent driver of social connection. Differences in patterns between the two areas were found with some measures of perception showing stronger relationships with recorded daily encounters in one area but not the other.ConclusionsThese results indicate substantial complexity in the relationship between perceptions of social connectedness and recorded daily social encounters/received connectedness, meaning that one cannot be reliably extrapolated from the other. Drivers of individuals’ social connections also varied by area of residence. These findings offer new insights into potential mediators of the association between connectedness and wellbeing.

Adolescent/young adult cancer survivors (AYACS) are diagnosed with cancer between 15 and 39 years of age. Improving AYACS' survivorship quality is crucial-including improving social connectedness, a construct describing the quality, structure and function of social relationships. With better understanding of AYACS' social connectedness, network-based interventions can be developed to foster social health. This study explored how a personal cancer diagnosis impacts AYACS' social connectedness among 35 AYACS 15-25 years old. Three themes emerged through thematic analysis: (1) AYACS experience substantial heterogeneity related to social support needs; (2) AYACS leverage multiple relationships and resources when seeking support after a personal cancer diagnosis; (3) AYACS' individual experiences were unique in that some noted positive changes, whereas others noted negative changes in relationships within social networks, specifically with peers. These findings create a foundation to develop social programming, foster peer relationships, and incorporate social science methods to aid intervention development to strengthen AYACS' social connectedness.

2. INTRODUCTION Since the inception of online social networks, the amount of social data that is being published on a daily basis has been increasing at an unprecedented rate. Smart, GPS-enabled, always-connected personal devices have taken the data generation to a new level by making it tremendously easy to generate and share social content like check-in information, likes, microblogs (e.g., Twitter), multimedia data, and so on. There is an enormous value in reasoning about such streaming data and deriving meaningful insights from it in real-time. Examples of potential applications include advertising, sentiment analysis, detecting natural disasters, social recommendations, personalized trends, spam detection, to name a few. There is thus an increasing need to build scalable systems to support such applications. Complex nature of social networks and their rapid evolution, coupled with the huge volume of streaming social data and the need for real-time processing, raise many computational challenges that have not been addressed in prior work. Social network data comprises of two major components. First, there is a network (linkage) component that captures the underlying interconnection structure among the entities in the social network. Second, there is content data that is typically associated with the nodes and the edges in the social network. The social network data stream contains updates to both these components. The structure of the network may itself change rapidly in many cases, especially when things like webpages and user tags (e.g., Twitter hashtags) are treated as nodes of the network. However, most of the social network data stream consists of updates to the data associated with the nodes and the edges, e.g., status updates and other content uploaded by the users, communication among the users, and so on. There is interest in performing a wide variety of queries and analytics over such data streams in real time. The queries can range from simple publish-subscribe queries, where a user is interested in being notified when something happens in his friend circle, to complex anomaly detection queries, where the goal is to identify anomalous behavior as early as possible. In this paper, we present an introduction to this new research area of stream querying and reasoning over social data. This area combines aspects from several well-studied research areas, chief among them, social network analysis, graph databases, and data streams. We provide a formal definition of the problem, survey the related prior work, and discuss some of the key research challenges that need to be addressed (and some of the solutions that have been proposed). We note that we use the term stream reasoning in this paper to encompass a broad range of tasks including various types of analytics, probabilistic reasoning, statistical inference, and logical reasoning. We contrast our use of this term with the recent work by Valle et al. [104, 105] who define this term more specifically to refer to integration of logical reasoning systems with data streams in the context of the Semantic Web. Given the vast amount of work on this and related topics, it is not our intention to be comprehensive in this brief overview. Rather we aim to cover some of the key ideas and representative work.

While the Japanese balanced die is a nutritional-related effort recommended in Japan’s health promotion policies, evidence about its association with frailty is scarce. It’s also worth noting the role of social context, given its wide associations with dietary behaviors and frailty. The present study examined the association between an unbalanced diet and frailty, and whether social connections modify this association in older Japanese adults. Data were collected by a cross-sectional survey of 15,302 community-dwelling adults aged ≥ 65 in Osaka, Japan, 2022. Frailty was measured using the validated Kihon Checklist, with a score of ≥ 8 classified as frailty. A balanced meal was defined as a combination of “staple food, main dishes, and vegetables”, in line with Japan’s health promotion policies. Participants without at least one such meal per day were classified as having an “unbalanced diet.” Social connections included social participation, social contact, and social support. Social participation was defined as participation in any of the organizations or groups. Social contact was defined as regular contact with family or friends. Social support was the presence of a supportive social network. Sex-stratified adjusted logistic models were employed to examine the associations between an unbalanced diet and frailty. We further tested interactions between an unbalanced diet and each social connection variable on both multiplicative and additive scales. The overall prevalence of frailty was 23.1%. Men have a higher proportion of unbalanced diet (29.6%) than women (15.1%). We found an unbalanced diet was associated with frailty in men (OR 1.70, 95% CI 1.38–2.11) and women (OR 1.77, 95% CI 1.38–2.26). In men, significant multiplicative and negative additive interactions were observed between an unbalanced diet and social participation, social contact, and social support (all p < 0.05). No significant interactions were found in women. We identified significant associations between an unbalanced diet and frailty in older Japanese adults. This association varied by social connections status specifically in men. Nutrition-related efforts for frailty may need to consider social context in older men. However, our study was limited by its cross-sectional design.

Purpose – In today’s aging world online communication is often viewed as a means to enhance social connectivity, and therefore well-being, of older adults. However, previous research on the influence of online communication on social connectivity largely disregards older adults, yields conflicting results and fails to assess the – debatable − causal direction of relationship. The purpose of this paper is to overcome these issues by developing four hypotheses related to who uses what, how, with whom. Design/methodology/approach – The authors use a panel data study to test the hypotheses, including 302 older adults. Response rates are between 62 and 75 percent. Findings – The authors find, first, that older adults differentiate between social connectivity with other village members, i.e., village connectivity, and connectivity with friends. Second, the impact of online communication varies among these two types of social connectivity. Where e-mail use has a negative impact on village connectivity, it does not affect connectivity with friends. Facebook use on the other hand has a negative impact on connectivity with friends, but not on village connectivity. The negative effects were not found among those older adults that were already well-connected on forehand, indicating a buffer effect. Practical/implications – Policy makers’ implementing online communication tools to strengthen social connectivity of older adults, may want to carefully select tools based on the type of connectivity they aim to enhance. Impact needs to be monitored. Originality/value – The authors contribute by analyzing how characteristics of online communication tools, i.e., information richness and privacy protection, as well as social connectivity, i.e., geographical proximity and emotional closeness jointly shape older adults’ social connectivity.