Showing papers on "Social network published in 2017"
12 Jul 2017
TL;DR: In this paper, a review of social capital as discussed in the literature, identifies controversies and debates, considers some critical issues, and provides conceptual and research strategies for building a theory.
Abstract: This chapter reviews social capital as discussed in the literature, identifies controversies and debates, considers some critical issues, and provides conceptual and research strategies for building a theory. It argues that such a theory and the research enterprise must be based on the fundamental understanding that social capital is captured from embedded resources in social networks. Such measurements can strength of tie network bridge, or intimacy, intensity, interaction and reciprocity be made relative to two frameworks: network resources and contact resources. There are many other measures, such as size, density, cohesion, and closeness of social networks which are candidates as measures for social capital. Network locations are necessary conditions of embedded resources. By considering social capital as assets in networks, the chapter discusses some issues in conceptualization, measurement, and causal mechanism. A proposed model identifies the exogenous factors leading to the acquisition (or the lack) of social capital as well as the expected returns of social capital.
3,733 citations
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13 Jul 2017
TL;DR: In this article, the authors present the first systematic and comprehensive collection of current theories and empirical research on the informal connections that individuals have for support, help, and information from other people.
Abstract: Leading scholars in the field of social networks from diverse disciplines present the first systematic and comprehensive collection of current theories and empirical research on the informal connections that individuals have for support, help, and information from other people. Expanding on concepts originally formulated by Pierre Bourdieu and James Coleman, this seminal work will find an essential place with educators and students in the fields of social networks, rational choice theory, institutions, and the socioeconomics of poverty, labor markets, social psychology, and race. The volume is divided into three parts. The first segment clarifies social capital as a concept and explores its theoretical and operational bases. Additional segments provide brief accounts that place the development of social capital in the context of the family of capital theorists, and identify some critical but controversial perspectives and statements regarding social capital in the literature. The editors then make the argument for the network perspective, why and how such a perspective can clarify controversies and advance our understanding of a whole range of instrumental and expressive outcomes. Social Capital further provides a forum for ongoing research programs initiated by social scientists working at the crossroads of formal theory and new methods. These scholars and programs share certain understandings and approaches in their analyses of social capital. They argue that social networks are the foundation of social capital. Social networks simultaneously capture individuals and social structure, thus serving as a vital conceptual link between actions and structural constraints, between micro- and macro-level analyses, and between relational and collective dynamic processes. They are further cognizant of the dual significance of the "structural" features of the social networks and the "resources" embedded in the networks as defining elements of social capital.
1,319 citations
TL;DR: In this paper, the authors conceptualize four phases of the journey of an idea, from conception to completion: idea generation, idea elaboration, idea championing, and idea implementation, and propose that a creator has distinct primary needs in each phase: cognitive flexibility, support, influence, and shared vision.
Abstract: Interest has burgeoned, in recent years, in how social networks influence individual creativity and innovation. From both the theoretical and empirical points of view, this increased attention has generated many inconsistencies. In this article we propose that a conceptualization of the idea journey encompassing phases that the literature has so far overlooked can help solve existing tensions. We conceptualize four phases of the journey of an idea, from conception to completion: idea generation, idea elaboration, idea championing, and idea implementation. We propose that a creator has distinct primary needs in each phase: cognitive flexibility, support, influence, and shared vision, respectively. Individual creators successfully move through a phase when the relational and structural elements of their networks match the distinct needs of the phase. The relational and structural elements that are beneficial for one phase, however, are detrimental for another. We propose that in order to solve this seeming ...
596 citations
TL;DR: In this paper, the consequences of interacting with social network sites for subjective well-being are discussed, i.e., how people feel moment-to-moment and how satisfied they are with their lives.
Abstract: Social network sites are ubiquitous and now constitute a common tool people use to interact with one another in daily life. Here we review the consequences of interacting with social network sites for subjective well-being—that is, how people feel moment-to-moment and how satisfied they are with their lives. We begin by clarifying the constructs that we focus on in this review: social network sites and subjective well-being. Next, we review the literature that explains how these constructs are related. This research reveals: (a) negative relationships between passively using social network sites and subjective well-being, and (b) positive relationships between actively using social network sites and subjective well-being, with the former relationship being more robust than the latter. Specifically, passively using social network sites provokes social comparisons and envy, which have negative downstream consequences for subjective well-being. In contrast, when active usage of social network sites predicts subjective well-being, it seems to do so by creating social capital and stimulating feelings of social connectedness. We conclude by discussing the policy implications of this work.
539 citations
TL;DR: It is shown that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process the authors call “moral contagion” and which offers insights into how moral ideas spread within networks during real political discussion.
Abstract: Political debate concerning moralized issues is increasingly common in online social networks. However, moral psychology has yet to incorporate the study of social networks to investigate processes by which some moral ideas spread more rapidly or broadly than others. Here, we show that the expression of moral emotion is key for the spread of moral and political ideas in online social networks, a process we call "moral contagion." Using a large sample of social media communications about three polarizing moral/political issues (n = 563,312), we observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them. Our results highlight the importance of emotion in the social transmission of moral ideas and also demonstrate the utility of social network methods for studying morality. These findings offer insights into how people are exposed to moral and political ideas through social networks, thus expanding models of social influence and group polarization as people become increasingly immersed in social media networks.
502 citations
TL;DR: The aim of this tutorial is to highlight a novel chapter of control theory, dealing with applications to social systems, to the attention of the broad research community.
382 citations
01 Jul 2017
TL;DR: This work builds predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news – satire, hoaxes, clickbait and propaganda, and shows that neural network models trained on tweet content and social network interactions outperform lexical models.
Abstract: Pew research polls report 62 percent of U.S. adults get news on social media (Gottfried and Shearer, 2016). In a December poll, 64 percent of U.S. adults said that “made-up news” has caused a “great deal of confusion” about the facts of current events (Barthel et al., 2016). Fabricated stories in social media, ranging from deliberate propaganda to hoaxes and satire, contributes to this confusion in addition to having serious effects on global stability. In this work we build predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news – satire, hoaxes, clickbait and propaganda. We show that neural network models trained on tweet content and social network interactions outperform lexical models. Unlike previous work on deception detection, we find that adding syntax and grammar features to our models does not improve performance. Incorporating linguistic features improves classification results, however, social interaction features are most informative for finer-grained separation between four types of suspicious news posts.
290 citations
TL;DR: In this paper, the effect of the source of advertisements on credibility perception through the theoretical framework of Ducoffe's (1995) advertising value model has been investigated in Facebook social network, where three distinct sources were used to generate and introduce product promotional messages: an associative reference group, an aspirational reference group and marketers themselves.
282 citations
TL;DR: A two-phase recommendation process is proposed to utilize deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user’s trusted friendships.
Abstract: With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user’s trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users’ interests and their trusted friends’ interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.
261 citations
TL;DR: It is suggested that social desirability bias is associated with key health measures and that the associations are not primarily due to depressive symptoms, and methods are needed to reduce social desIRability bias.
253 citations
TL;DR: A novel controlled experiment is described that is performed on Twitter using ‘social bots’ deployed to carry out coordinated attempts at spreading information, proposing two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses.
Abstract: It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using ‘social bots’ deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.
TL;DR: In this article, the authors survey the literature on the economic consequences of the structure of social networks and develop a taxonomy of macro and micro characteristics of social-interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors.
Abstract: We survey the literature on the economic consequences of the structure of social networks. We develop a taxonomy of "macro" and "micro" characteristics of social-interaction networks and discuss both the theoretical and empirical findings concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also discuss the challenges of accounting for the endogeneity of networks in assessing the relationship between the patterns of interactions and behaviors.
TL;DR: The type and size of social networks have a role in the relationship between loneliness and depression and increasing social interaction may be more beneficial than strategies based on improving maladaptive social cognition in loneliness to reduce the prevalence of depression among Spanish older adults.
Abstract: Loneliness and depression are associated, in particular in older adults. Less is known about the role of social networks in this relationship. The present study analyzes the influence of social networks in the relationship between loneliness and depression in the older adult population in Spain. A population-representative sample of 3535 adults aged 50 years and over from Spain was analyzed. Loneliness was assessed by means of the three-item UCLA Loneliness Scale. Social network characteristics were measured using the Berkman–Syme Social Network Index. Major depression in the previous 12 months was assessed with the Composite International Diagnostic Interview (CIDI). Logistic regression models were used to analyze the survey data. Feelings of loneliness were more prevalent in women, those who were younger (50–65), single, separated, divorced or widowed, living in a rural setting, with a lower frequency of social interactions and smaller social network, and with major depression. Among people feeling lonely, those with depression were more frequently married and had a small social network. Among those not feeling lonely, depression was associated with being previously married. In depressed people, feelings of loneliness were associated with having a small social network; while among those without depression, feelings of loneliness were associated with being married. The type and size of social networks have a role in the relationship between loneliness and depression. Increasing social interaction may be more beneficial than strategies based on improving maladaptive social cognition in loneliness to reduce the prevalence of depression among Spanish older adults.
TL;DR: The negative associations of Facebook use were comparable to or greater in magnitude than the positive impact of offline interactions, which suggests a possible tradeoff between offline and online relationships.
Abstract: Face-to-face social interactions enhance well-being. With the ubiquity of social media, important questions have arisen about the impact of online social interactions. In the present study, we assessed the associations of both online and offline social networks with several subjective measures of well-being. We used 3 waves (2013, 2014, and 2015) of data from 5,208 subjects in the nationally representative Gallup Panel Social Network Study survey, including social network measures, in combination with objective measures of Facebook use. We investigated the associations of Facebook activity and real-world social network activity with self-reported physical health, self-reported mental health, self-reported life satisfaction, and body mass index. Our results showed that overall, the use of Facebook was negatively associated with well-being. For example, a 1-standard-deviation increase in "likes clicked" (clicking "like" on someone else's content), "links clicked" (clicking a link to another site or article), or "status updates" (updating one's own Facebook status) was associated with a decrease of 5%-8% of a standard deviation in self-reported mental health. These associations were robust to multivariate cross-sectional analyses, as well as to 2-wave prospective analyses. The negative associations of Facebook use were comparable to or greater in magnitude than the positive impact of offline interactions, which suggests a possible tradeoff between offline and online relationships.
TL;DR: This work presents theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange and shows that the dynamics of group accuracy change with network structure.
Abstract: A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton’s discovery of the “wisdom of crowds” [Galton F (1907) Nature 75:450–451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals’ estimates became more similar when subjects observed each other’s beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020–9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.
TL;DR: Key achievements of user identity linkage across online social networks including state-of- the-art algorithms, evaluation metrics, and representative datasets are reviewed.
Abstract: The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.
TL;DR: Findings are reported from 506 UK based Facebook users who responded to an extensive online survey about their SNS behaviours and online vulnerability and the implications of social networking on an individual's online vulnerability are considered.
TL;DR: In the context of treatment, these adolescents shifted their social media use patterns from what they perceived as negative to more positive use, and Implications for clinicians counseling depressed adolescents on social mediause are discussed.
07 Aug 2017
TL;DR: This work presents a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs.
Abstract: Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as bridge users. In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before. Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method.
TL;DR: This meta-analysis examines the relationship between time spent on social networking sites and psychological well-being factors, namely self-esteem, life satisfaction, loneliness, and depression, and found the correlations were close to 0 and the effects of publication outlet, site on which users spent time, scale of time spent, and participant age and gender were not significant.
Abstract: This meta-analysis examines the relationship between time spent on social networking sites and psychological well-being factors, namely self-esteem, life satisfaction, loneliness, and depression. Sixty-one studies consisting of 67 independent samples involving 19,652 participants were identified. The mean correlation between time spent on social networking sites and psychological well-being was low at r = −0.07. The correlations between time spent on social networking sites and positive indicators (self-esteem and life satisfaction) were close to 0, whereas those between time spent on social networking sites and negative indicators (depression and loneliness) were weak. The effects of publication outlet, site on which users spent time, scale of time spent, and participant age and gender were not significant. As most included studies used student samples, future research should be conducted to examine this relationship for adults.
TL;DR: Evidence is examined that individual differences in social ability, partly determined by genetic influences on brain structure and function, impact the quality and quantity of social ties during adolescence and that the structure of one's social network exerts complex yet profound influences on individual behavior and mental health.
TL;DR: In this paper, the authors developed a concept of shareworthiness based on news value theory and integrating theories about online identities and self-representation, with which they seek to understand how the number of shares an article receives on such sites can be predicted.
Abstract: People increasingly visit online news sites not directly, but by following links on social network sites. Drawing on news value theory and integrating theories about online identities and self-representation, we develop a concept of shareworthiness, with which we seek to understand how the number of shares an article receives on such sites can be predicted. Findings suggest that traditional criteria of newsworthiness indeed play a role in predicting the number of shares, and that further development of a theory of shareworthiness based on the foundations of newsworthiness can offer fruitful insights in news dissemination processes.
TL;DR: The scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research are explored.
Abstract: Background: Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective: The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods: We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results: The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions: Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. [J Med Internet Res 2017;19(6):e228]
TL;DR: This paper surveys the educational research literature to examine: How such technologies are perceived and used by K-12 learners and teachers with what impacts on pedagogy or students' learning.
Abstract: The increasingly widespread use of social network sites to expand and deepen one's social connections is a relatively new but potentially important phenomenon that has implications for teaching and learning and teacher education in the 21st century. This paper surveys the educational research literature to examine: How such technologies are perceived and used by K-12 learners and teachers with what impacts on pedagogy or students' learning. Selected studies were summarized and categorized according to the four types introduced by Roblyer (2005) as studies most needed to move the educational technology field forward. These include studies that establish the technology's effectiveness at improving student learning; investigate implementation strategies; monitor social impact; and report on common uses to shape the direction of the field. We found the most prevalent type of study conducted related to our focal topic was research on common uses. The least common type of study conducted was research that established the technology's effectiveness at improving student learning. Implications for the design of future research and teacher education initiatives are discussed.
TL;DR: A conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures, is proposed, which suggests six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks.
Abstract: As users interact via social media spaces, like Twitter, they form connections that emerge into complex social network structures. These connections are indicators of content sharing, and network structures reflect patterns of information flow. This article proposes a conceptual and practical model for the classification of topical Twitter networks, based on their network-level structures. As current literature focuses on the classification of users to key positions, this study utilizes the overall network structures in order to classify Twitter conversation based on their patterns of information flow. Four network-level metrics, which have established as indicators of information flow characteristics—density, modularity, centralization, and the fraction of isolated users—are utilized in a three-step classification model. This process led us to suggest six structures of information flow: divided, unified, fragmented, clustered, in and out hub-and-spoke networks. We demonstrate the value of these network s...
TL;DR: In this paper, the authors consider the potential significance of clean energy communities (CECs) in the transformation of the present socio-technical regimes underlying our centralized energy systems, towards a more distributed and decentralized future.
Abstract: This paper considers the potential significance of ‘clean energy communities’ (CECs) in the transformation of the present socio-technical regimes underlying our centralized energy systems, towards a more distributed and decentralized future. It explains the centralized, distributed, and decentralized sub-structures, embedded in current energy systems and energy markets. We analyze long-term dynamics and possible pathways of this transition, and the co-evolution of energy systems and communities, using an exploratory structure, drawing on insights from transition theories, innovation studies, and social network concepts. This includes analysis of the various forms of CECs – including virtual power plants, peer-to-peer trading, microgrids, and community-scale energy projects – emerging in a number of developed and developing jurisdictions, including Australia. This analysis suggests that low-carbon transition pathways will be varied, driven by social, technological, and organizational contexts, and shaped by institutional change processes, and interaction with the existing regime and incumbent actors. Social and technological entrepreneurs, and utilities, operating within an environment increasingly defined by energy consumers, industry groups, and policy actors, will need to be adaptable and innovative in choosing strategic directions, associated investment decisions, establishing appropriate alliances, and acquiring resources, to meet their goals in this low-carbon energy transition.
TL;DR: This paper presents a comprehensive survey of different security and privacy threats that target every user of social networking sites, and separately focuses on various threats that arise due to the sharing of multimedia content within a social networking site.
TL;DR: In this paper, the authors developed a dataset of daily protests across 16 countries in the Middle East and North Africa over 14 months from 2010 through 2011, and measured the number of connections of each person.
Abstract: Who is responsible for protest mobilization? Models of disease and information diffusion suggest that those central to a social network (the core) should have a greater ability to mobilize others than those who are less well-connected. To the contrary, this article argues that those not central to a network (the periphery) can generate collective action, especially in the context of large-scale protests in authoritarian regimes. To show that those in the core of a social network have no effect on levels of protest, this article develops a dataset of daily protests across 16 countries in the Middle East and North Africa over 14 months from 2010 through 2011. It combines that dataset with geocoded, individual-level communication from the same period and measures the number of connections of each person. Those on the periphery are shown to be responsible for changing levels of protest, with some evidence suggesting that the core’s mobilization efforts lead to fewer protests. These results have implications for a wide range of social choices that rely on interdependent decision making.
TL;DR: An extended model by considering the substitution of price value with privacy concerns is developed and shows that there are three main drivers of users' intentions to use social network sites to publish content about their experiences: performance expectancy, hedonic motivation, and habit.
TL;DR: This study collected studies on social-network-related topics that were published between January 1996 and December 2014, assembling a total of 2565 articles and 81,316 citations to elucidate the core topics relevant to social networks.