Trust and Social Networks in a Changing Europe.

This project set out to investigate the effects of the recent massive social transitions in Eastern Europe on the everyday social lives of the inhabitants of three very different nations: Georgia, Russia and Hungary. It focused in particular on the availability and nature of the support networks available to three different segments of each of the societies (manual workers, students and entrepreneurs) and the impact of network participation on psychological and physical well-being. The group set four specific questions to investigate: the part played by individual psychological beliefs in the formation and maintenance of social networks and the consequent formation of trusting relations; the implication of the size and quality of these networks for mental health; the nature of the social groups inhabited by the respondents and the implication of their work schedule and daily routines on the maintenance of a social and family life; and an analysis of how cultures vary in their social networks and intimacy. Three different methods were used to examine social support and its implications: structured questionnaires, semi-structured short interviews and a media analysis of newspaper materials. The questionnaires were administered to 150 participants in each country, equally divided between students studying full time, manual workers employed in factories, and business people (small kiosk owners, whose work and life style differs considerably from that of the manual workers). The questionnaires investigated various predictors of social support including the locus of control, relationship beliefs, individualism-collectivism and egalitarianism, demographic variables (age, gender and occupation), social support, both in general and in relation to significant events that have occurred since the transition from communism. Those with an internal locus of control were more likely to report a higher level of social support, as were collectivists, while age too was a significant predictor, with younger respondents enjoying higher levels of support, regardless of the measures of support employed. Respondents across the cultures referred to a decline of social support and the group also found a direct correlation between social support and mental health outcomes. All 450 respondents were interviewed on their general responses to changes in their lives since the fall of communism and the effects of their work lives on their social lives and the home environment. The interviews revealed considerable variations in the way in which work-life offered opportunities for a broader social life and also provided a hindrance to the development of fulfilling relationships. Many of the work experiences discussed were culture specific, with work having a particularly negative impact on the social life of Russian entrepreneurs but being seen much more positively in Georgia. This may reflect the nature of support offered in a society as overall support levels were lowest in Russia, meaning that social support may be of particular importance there. The way in cultural values and norms about personal relationships are transmitted in a culture is a critical issue for social psychologists and the group examined newspaper articles in those newspapers read by the respondents in each of the three countries. These revealed a number of different themes. The concept of a divided society and its implications for personal relationships was clearest in Russian and Hungary, where widely-read newspapers dwelt on the contrast between "new Russians/Hungarians" and the older, poorer ones and extended considerable sympathy to those suffering from neglect in institutions. Magyar Nemzet, a paper widely read by Hungarian students reflects the generally more pessimistic tone about personal relationships in Russia and Hungary and gave a particularly detailed analysis of the implications this holds for human relations in a modern society. In Georgia, however, the tone of the newspapers is more positive, stressing greater social cohesion. Part of this cohesion is framed in the context of religion, with the church appealing to a broader egalitarianism, whereas in less egalitarian Hungary appeals by the Church are centred more on the nuclear family and its need for expansion in both size and influence. The division between the sexes was another prominent issue in Hungary and Russia, while the theme of generational conflict also emerged in Hungarian and Georgian papers, although with some understanding of "young people today". The team's original expectation that the different newspapers read by the different groups of respondents would present differing images of personal relationships was not fulfilled, as despite variations in style, they found little clear "ideological targeting" of any particular readership. They conclude that the vast majority of respondents recognised that the social transition from communism has had a significant impact on the well-being of social relationships and that this is a pertinent issue for all segments of society. While the group see the data collected as a source to be worked on for some time in the future, their initial impressions include the following. Social support is clearly an important concern across all three countries. All respondents (including the students) lament the time taken up by their heavy work schedules and value their social networks and family ties in particular. The level of social support differs across the countries investigated, with Georgian apparently enjoying significantly higher levels of social support. The analysis produced an image of a relatively cohesive and egalitarian society in which even the group most often seen as distant from the general population, business people, is supported by a strong social network. In contrast, the support networks available to the Russian respondents seem particularly weak and reflect a general sense of division and alienation within the culture as a whole. The implications of low levels of social support may vary across countries. While Russians reported the lowest level of mental health problems, the link between social support and mental health may be strongest in that country. In contrast, in Hungary it is the link between fatalism and mental health problems which is particularly strong, while in Georgia the strongest correlation was between mental health and marital quality, emphasising the significance of the marital relationship in that country.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

Social Networks Analysis

This morning Dr. Risser will introduce you to the basic ideas of social network analysis. You will learn some history behind the study of social networks. Dr. Risser will introduce you to mathematical measures of social networks including centrality measures and measures of spread and cohesion. You will also learn how to use a computer program to analyze social network data

Social Networking: How to Build Social Networks

This poster illustrates variables and connections in social networking.

Online social networks: why we disclose

On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of industry players and policymakers worldwide. Despite the impressive scope of this phenomenon, very little is understood about what motivates users to disclose personal information. Integrating focus group results into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

Privacy concerns and identity in online social networks

Driven by privacy-related fears, users of Online Social Networks may start to reduce their network activities. This trend can have a negative impact on network sustainability and its business value. Nevertheless, very little is understood about the privacy-related concerns of users and the impact of those concerns on identity performance. To close this gap, we take a systematic view of user privacy concerns on such platforms. Based on insights from focus groups and an empirical study with 210 subjects, we find that (i) Organizational Threats and (ii) Social Threats stemming from the user environment constitute two underlying dimensions of the construct “Privacy Concerns in Online Social Networks”. Using a Structural Equation Model, we examine the impact of the identified dimensions of concern on the Amount, Honesty, and Conscious Control of individual self-disclosure on these sites. We find that users tend to reduce the Amount of information disclosed as a response to their concerns regarding Organizational Threats. Additionally, users become more conscious about the information they reveal as a result of Social Threats. Network providers may want to develop specific mechanisms to alleviate identified user concerns and thereby ensure network sustainability.

Leveraging Trust and Privacy Concerns in Online Social Networks: an Empirical Study

Unprecedented success of Online Social Networks, such as Facebook, has been recently overshadowed by the privacy risks they imply. Weary of privacy concerns and unable to construct their identity in the desired way, users may restrict or even terminate their platform activities. Even though this means a considerable business risk for these platforms, so far there have been no studies on how to enable social network providers to address these problems. This study fills this gap by adopting a fairness perspective to analyze related measures at the disposal of the provider. In a Structural Equation Model with 237 subjects we find that ensuring interactional and procedural justice are two important strategies to support user participation on the platform.

Investigating the value of privacy on online social networks: conjoint analysis

Popularity of Online Social Networks has been recently overshadowed by the privacy problems they pose. Users are getting increasingly vigilant concerning information they disclose and are strongly opposing the use of their information for commercial purposes. Nevertheless, as long as the network is offered to users for free, providers have little choice but to generate revenue through personalized advertising to remain financially viable. Our study empirically investigates the ways out of this deadlock. Using conjoint analysis we find that privacy is indeed important for users. We identify three groups of users with different utility patterns: Unconcerned Socializers, Control-conscious Socializers and Privacy-concerned. Our results provide relevant insights into how network providers can capitalize on different user preferences by specifically addressing the needs of distinct groups in the form of various premium accounts. Overall, our study is the first attempt to assess the value of privacy in monetary terms in this context.

"It won't happen to me!": self-disclosure in online social networks

Despite the considerable amount of self-disclosure in Online Social Networks (OSN), the motivation behind this phenomenon is still little understood. Building on the Privacy Calculus theory, this study fills this gap by taking a closer look at the factors behind individual self-disclosure decisions. In a Structural Equation Model with 237 subjects we find Perceived Enjoyment and Privacy Concerns to be significant determinants of information revelation. We confirm that the privacy concerns of OSN users are primarily determined by the perceived likelihood of a privacy violation and much less by the expected damage. These insights provide a solid basis for OSN providers and policy-makers in their effort to ensure healthy disclosure levels that are based on objective rationale rather than subjective misconceptions.