Showing papers on "Social network published in 2008"

Sampling Knowledge: The Hermeneutics of Snowball Sampling in Qualitative Research

Abstract: During the past two decades we have witnessed a rather impressive growth of theoretical innovations and conceptual revisions of epistemological and methodological approaches within constructivist‐qualitative quarters of the social sciences. Methodological discussions have commonly addressed a variety of methods for collecting and analyzing empirical material, yet the critical grounds upon which these were reformulated have rarely been extended to embrace sampling concepts and procedures. The latter have been overlooked, qualifying only as a ‘technical’ research stage. This article attends to snowball sampling via constructivist and feminist hermeneutics, suggesting that when viewed critically, this popular sampling method can generate a unique type of social knowledge—knowledge which is emergent, political and interactional. The article reflects upon researches about backpacker tourists and marginalized men, where snowball sampling was successfully employed in investigating these groups' organic social ne...

Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study

Abstract: Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks. Design Longitudinal social network analysis. Setting Framingham Heart Study social network. Participants 4739 individuals followed from 1983 to 2003. Main outcome measures Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties. ResultsClustersofhappyand unhappypeoplearevisible in the network, and the relationship between people ’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends). People whoaresurroundedbymanyhappypeopleandthosewho arecentralinthenetworkaremorelikelytobecomehappy in the future. Longitudinal statistical models suggest that clustersofhappinessresultfromthespreadofhappiness and not just a tendency for people to associate with similarindividuals.Afriendwholiveswithinamile(about 1.6km)andwhobecomeshappyincreasestheprobability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The

Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

Abstract: A large body of work has been devoted to defining and identifying clusters or communities in social and information networks. We explore from a novel perspective several questions related to identifying meaningful communities in large social and information networks, and we come to several striking conclusions. We employ approximation algorithms for the graph partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities. In particular, we define the network community profile plot, which characterizes the "best" possible community--according to the conductance measure--over a wide range of size scales. We study over 100 large real-world social and information networks. Our results suggest a significantly more refined picture of community structure in large networks than has been appreciated previously. In particular, we observe tight communities that are barely connected to the rest of the network at very small size scales; and communities of larger size scales gradually "blend into" the expander-like core of the network and thus become less "community-like." This behavior is not explained, even at a qualitative level, by any of the commonly-used network generation models. Moreover, it is exactly the opposite of what one would expect based on intuition from expander graphs, low-dimensional or manifold-like graphs, and from small social networks that have served as testbeds of community detection algorithms. We have found that a generative graph model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community profile plot similar to what we observe in our network datasets.

鄂Looking at 鈂, 鄂Looking up 鈂 or 鄂Keeping up with 鈂 People? Motives and Uses of Facebook

Abstract: This paper investigates the uses of social networking site Facebook, and the gratifications users derive from those uses. In the first study, 137 users generated words or phrases to describe how they used Facebook, and what they enjoyed about their use. These phrases were coded into 46 items which were completed by 241 Facebook users in Study 2. Factor analysis identified seven unique uses and gratifications: social connection, shared identities, content, social investigation, social network surfing and status updating. User demographics, site visit patterns and the use of privacy settings were associated with different uses and gratifications.

SoRec: social recommendation using probabilistic matrix factorization

Abstract: Data sparsity, scalability and prediction quality have been recognized as the three most crucial challenges that every collaborative filtering algorithm or recommender system confronts. Many existing approaches to recommender systems can neither handle very large datasets nor easily deal with users who have made very few ratings or even none at all. Moreover, traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the social interactions or connections among users. In view of the exponential growth of information generated by online social networks, social network analysis is becoming important for many Web applications. Following the intuition that a person's social network will affect personal behaviors on the Web, this paper proposes a factor analysis approach based on probabilistic matrix factorization to solve the data sparsity and poor prediction accuracy problems by employing both users' social network information and rating records. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results shows that our method performs much better than the state-of-the-art approaches, especially in the circumstance that users have made few or no ratings.

Social Networks and Health

Abstract: People are interconnected, and so their health is interconnected. In recognition of this social fact, there has been growing conceptual and empirical attention over the past decade to the impact of social networks on health. This article reviews prominent findings from this literature. After drawing a distinction between social network studies and social support studies, we explore current research on dyadic and supradyadic network influences on health, highlighting findings from both egocentric and sociocentric analyses. We then discuss the policy implications of this body of work, as well as future research directions. We conclude that the existence of social networks means that people's health is interdependent and that health and health care can transcend the individual in ways that patients, doctors, policy makers, and researchers should care about.

Looking at, looking up or keeping up with people?: motives and use of facebook

Abstract: This paper investigates the uses of social networking site Facebook, and the gratifications users derive from those uses. In the first study, 137 users generated words or phrases to describe how they used Facebook, and what they enjoyed about their use. These phrases were coded into 46 items which were completed by 241 Facebook users in Study 2. Factor analysis identified seven unique uses and gratifications: social connection, shared identities, content, social investigation, social network surfing and status updating. User demographics, site visit patterns and the use of privacy settings were associated with different uses and gratifications.

Statistical properties of community structure in large social and information networks

Abstract: A large body of work has been devoted to identifying community structure in networks. A community is often though of as a set of nodes that has more connections between its members than to the remainder of the network. In this paper, we characterize as a function of size the statistical and structural properties of such sets of nodes. We define the network community profile plot, which characterizes the "best" possible community - according to the conductance measure - over a wide range of size scales, and we study over 70 large sparse real-world networks taken from a wide range of application domains. Our results suggest a significantly more refined picture of community structure in large real-world networks than has been appreciated previously.Our most striking finding is that in nearly every network dataset we examined, we observe tight but almost trivial communities at very small scales, and at larger size scales, the best possible communities gradually "blend in" with the rest of the network and thus become less "community-like." This behavior is not explained, even at a qualitative level, by any of the commonly-used network generation models. Moreover, this behavior is exactly the opposite of what one would expect based on experience with and intuition from expander graphs, from graphs that are well-embeddable in a low-dimensional structure, and from small social networks that have served as testbeds of community detection algorithms. We have found, however, that a generative model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community structure similar to our observations.

Identifying and employing social network relationships

Abstract: Methods and apparatus are described for detecting social relationships across multiple networks and/or communication channels. These social relationships may then be utilized in a wide variety of ways to support and enhance a broad range of user services.

Can You See Me Now? Audience and Disclosure Regulation in Online Social Network Sites

Abstract: The prevailing paradigm in Internet privacy literature, treating privacy within a context merely of rights and violations, is inadequate for studying the Internet as a social realm. Following Goffm...

Statistics and Social Network of YouTube Videos

Abstract: YouTube has become the most successful Internet website providing a new generation of short video sharing service since its establishment in early 2005. YouTube has a great impact on Internet traffic nowadays, yet itself is suffering from a severe problem of scalability. Therefore, understanding the characteristics of YouTube and similar sites is essential to network traffic engineering and to their sustainable development. To this end, we have crawled the YouTube site for four months, collecting more than 3 million YouTube videos' data. In this paper, we present a systematic and in-depth measurement study on the statistics of YouTube videos. We have found that YouTube videos have noticeably different statistics compared to traditional streaming videos, ranging from length and access pattern, to their growth trend and active life span. We investigate the social networking in YouTube videos, as this is a key driving force toward its success. In particular, we find that the links to related videos generated by uploaders' choices have clear small-world characteristics. This indicates that the videos have strong correlations with each other, and creates opportunities for developing novel techniques to enhance the service quality.

Preserving Privacy in Social Networks Against Neighborhood Attacks

Abstract: Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative towards preserving privacy in social network data. We identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim's identity is preserved using the conventional anonymization techniques. We show that the problem is challenging, and present a practical solution to battle neighborhood attacks. The empirical study indicates that anonymized social networks generated by our method can still be used to answer aggregate network queries with high accuracy.

The Social Connectedness of Older Adults: A National Profile*

Abstract: For decades, scholars have wrestled with the notion that old age is characterized by social isolation. However, there has been no systematic, nationally representative evaluation of this possibility in terms of social network connectedness. In this paper, the authors develop a profile of older adults' social integration with respect to nine dimensions of connectedness to interpersonal networks and voluntary associations. The authors use new data from the National Social Life, Health, and Aging Project (NSHAP), a population-based study of non-institutionalized older Americans aged 57-85 conducted in 2005-2006. Findings suggest that among older adults, age is negatively related to network size, closeness to network members, and number of non-primary-group ties. On the other hand, age is positively related to frequency of socializing with neighbors, religious participation, and volunteering. In addition, it has a U-shaped relationship with volume of contact with network members. These findings are inconsistent with the notion that old age has a universal negative influence on social connectedness. Instead, life course factors have divergent consequences for different forms of social connectedness. Some later life transitions, like retirement and bereavement, may prompt greater connectedness. The authors close by urging increased dialogue between social gerontological and social network research.

Influence and correlation in social networks

Abstract: In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.This is a difficult task in general, since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network. Distinguishing influence from these is essentially the problem of distinguishing correlation from causality, a notoriously hard statistical problem.In this paper we study this problem systematically. We define fairly general models that replicate the aforementioned sources of social correlation. We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available.We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation. We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network (from Flickr) according to one of several models. Simulation results confirm that our test performs well on these data. Finally, we apply them to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.

Feedback effects between similarity and social influence in online communities

Abstract: A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties. People are similar to their neighbors in a social network for two distinct reasons: first, they grow to resemble their current friends due to social influence; and second, they tend to form new links to others who are already like them, a process often termed selection by sociologists. While both factors are present in everyday social processes, they are in tension: social influence can push systems toward uniformity of behavior, while selection can lead to fragmentation. As such, it is important to understand the relative effects of these forces, and this has been a challenge due to the difficulty of isolating and quantifying them in real settings.We develop techniques for identifying and modeling the interactions between social influence and selection, using data from online communities where both social interaction and changes in behavior over time can be measured. We find clear feedback effects between the two factors, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -- but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions. We also consider the relative value of similarity and social influence in modeling future behavior. For instance, to predict the activities that an individual is likely to do next, is it more useful to know the current activities of their friends, or of the people most similar to them?

The Taste for Privacy: An Analysis of College Student Privacy Settings in an Online Social Network

Abstract: The rapid growth of contemporary social network sites (SNSs) has coincided with an increasing concern over personal privacy. College students and adolescents routinely provide personal information on profiles that can be viewed by large numbers of unknown people and potentially used in harmful ways. SNSs like Facebook and MySpace allow users to control the privacy level of their profile, thus limiting access to this information. In this paper, we take the preference for privacy itself as our unit of analysis, and analyze the factors that are predictive of a student having a private versus public profile. Drawing upon a new social network dataset based on Facebook, we argue that privacy behavior is an upshot of both social influences and personal incentives. Students are more likely to have a private profile if their friends and roommates have them; women are more likely to have private profiles than are men; and having a private profile is associated with a higher level of online activity. Finally, students who have private versus public profiles are characterized by au nique set ofcultural preferences—of which the ‘‘taste for privacy’’ may be only as mall but integral part.

Social Capital Production in a Virtual P3 Community

Abstract: The purpose of this study is to examine the relational norms that determine social capital—an intangible resource embedded in and accumulated through a specific social structure. The social structure examined in this study is a virtual community created through text‐based conversations oriented toward peer‐to‐peer problem solving (P3). Empirical results support the conceptualization of social capital as an index composed of the normative influences of voluntarism, reciprocity, and social trust. Membership length was found to moderate the virtual P3 community experience. Qualitative analysis of the community dialogue provides additional support for the characterization of virtual P3 activity as community based.

Honest Signals: How They Shape Our World

Abstract: How can you know when someone is bluffing? Paying attention? Genuinely interested? The answer, writes Sandy Pentland in Honest Signals, is that subtle patterns in how we interact with other people reveal our attitudes toward them. These unconscious social signals are not just a back channel or a complement to our conscious language; they form a separate communication network. Biologically based "honest signaling," evolved from ancient primate signaling mechanisms, offers an unmatched window into our intentions, goals, and values. If we understand this ancient channel of communication, Pentland claims, we can accurately predict the outcomes of situations ranging from job interviews to first dates. Pentland, an MIT professor, has used a specially designed digital sensor worn like an ID badgea "sociometer"to monitor and analyze the back-and-forth patterns of signaling among groups of people. He and his researchers found that this second channel of communication, revolving not around words but around social relations, profoundly influences major decisions in our liveseven though we are largely unaware of it. Pentland presents the scientific background necessary for understanding this form of communication, applies it to examples of group behavior in real organizations, and shows how by "reading" our social networks we can become more successful at pitching an idea, getting a job, or closing a deal. Using this "network intelligence" theory of social signaling, Pentland describes how we can harness the intelligence of our social network to become better managers, workers, and communicators.

From Social Structural Factors to Perceptions of Relationship Quality and Loneliness: The Chicago Health, Aging, and Social Relations Study

Abstract: Objectives The objective of this study was to test a conceptual model of loneliness in which social structural factors are posited to operate through proximal factors to influence perceptions of relationship quality and loneliness Methods We used a population-based sample of 225 White, Black, and Hispanic men and women aged 50 through 68 from the Chicago Health, Aging, and Social Relations Study to examine the extent to which associations between sociodemographic factors and loneliness were explained by socioeconomic status, physical health, social roles, stress exposure, and, ultimately, by network size and subjective relationship quality Results Education and income were negatively associated with loneliness and explained racial/ethnic differences in loneliness Being married largely explained the association between income and loneliness, with positive marital relationships offering the greatest degree of protection against loneliness Independent risk factors for loneliness included male gender, physical health symptoms, chronic work and/or social stress, small social network, lack of a spousal confidant, and poor-quality social relationships Discussion Longitudinal research is needed to evaluate the causal role of social structural and proximal factors in explaining changes in loneliness

Resisting structural re-identification in anonymized social networks

Abstract: We identify privacy risks associated with releasing network data sets and provide an algorithm that mitigates those risks. A network consists of entities connected by links representing relations such as friendship, communication, or shared activity. Maintaining privacy when publishing networked data is uniquely challenging because an individual's network context can be used to identify them even if other identifying information is removed. In this paper, we quantify the privacy risks associated with three classes of attacks on the privacy of individuals in networks, based on the knowledge used by the adversary. We show that the risks of these attacks vary greatly based on network structure and size. We propose a novel approach to anonymizing network data that models aggregate network structure and then allows samples to be drawn from that model. The approach guarantees anonymity for network entities while preserving the ability to estimate a wide variety of network measures with relatively little bias.

Changes in use and perception of facebook

Abstract: As social computing systems persist over time, the user experiences and interactions they support may change. One type of social computing system, Social Network Sites (SNSs), are becoming more popular across broad segments of Internet users. Facebook, in particular, has very broad participation amongst college attendees, and has been growing in other populations as well. This paper looks at how use of Facebook has changed over time, as indicated by three consecutive years of survey data and interviews with a subset of survey respondents. Reported uses of the site remain relatively constant over time, but the perceived audience for user profiles and attitudes about the site show differences over the study period.

Grooming, gossip, Facebook and Myspace: What can we learn about these sites from those who won't assimilate?

Abstract: This paper explores the rapid adoption of online social network sites (also known as social networking sites) (SNSs) by students on a US college campus. Using quantitative (n = 713) and qualitative (n = 51) data based on a diverse sample of college students, demographic and other characteristics of SNS users and non-users are compared. Starting with the theoretical frameworks of Robin Dunbar and Erving Goffman, this paper situates SNS activity under two rubrics: (1) social grooming; and (2) presentation of the self. This study locates these sites within the emergence of social computing and makes a conceptual distinction between the expressive Internet, the Internet of social interactions, and the instrumental Internet, the Internet of airline tickets and weather forecasts. This paper compares and contrasts the user and non-user populations in terms of expressive and instrumental Internet use, social ties and attitudes toward social-grooming, privacy and efficiency. Two clusters are found to influence SNS...