1,425 research outputs found
Suicide ideation of individuals in online social networks
Suicide explains the largest number of death tolls among Japanese adolescents
in their twenties and thirties. Suicide is also a major cause of death for
adolescents in many other countries. Although social isolation has been
implicated to influence the tendency to suicidal behavior, the impact of social
isolation on suicide in the context of explicit social networks of individuals
is scarcely explored. To address this question, we examined a large data set
obtained from a social networking service dominant in Japan. The social network
is composed of a set of friendship ties between pairs of users created by
mutual endorsement. We carried out the logistic regression to identify users'
characteristics, both related and unrelated to social networks, which
contribute to suicide ideation. We defined suicide ideation of a user as the
membership to at least one active user-defined community related to suicide. We
found that the number of communities to which a user belongs to, the
intransitivity (i.e., paucity of triangles including the user), and the
fraction of suicidal neighbors in the social network, contributed the most to
suicide ideation in this order. Other characteristics including the age and
gender contributed little to suicide ideation. We also found qualitatively the
same results for depressive symptoms.Comment: 4 figures, 9 table
A Trust Management Framework for Decision Support Systems
In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources
Measuring Social Value Orientation
Narrow self-interest is often used as a simplifying assumption when studying people making decisions in social contexts. Nonetheless, people exhibit a wide range of different motivations when choosing unilaterally among interdependent outcomes. Measuring the magnitude of the concern people have for others, sometimes called Social Value Orientation (SVO), has been an interest of many social scientists for decades and several different measurement methods have been developed so far. Here we introduce a new measure of SVO that has several advantages over existent methods. A detailed description of the new measurement method is presented, along with norming data that provide evidence of its solid psychometric properties. We conclude with a brief discussion of the research streams that would benefit from a more sensitive and higher resolution measure of SVO, and extend an invitation to others to use this new measure which is freely availabl
Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS
The features of collaboration patterns are often considered to be different
from discipline to discipline. Meanwhile, collaborating among disciplines is an
obvious feature emerged in modern scientific research, which incubates several
interdisciplines. The features of collaborations in and among the disciplines
of biological, physical and social sciences are analyzed based on 52,803 papers
published in a multidisciplinary journal PNAS during 1999 to 2013. From those
data, we found similar transitivity and assortativity of collaboration patterns
as well as the identical distribution type of collaborators per author and that
of papers per author, namely a mixture of generalized Poisson and power-law
distributions. In addition, we found that interdisciplinary research is
undertaken by a considerable fraction of authors, not just those with many
collaborators or those with many papers. This case study provides a window for
understanding aspects of multidisciplinary and interdisciplinary collaboration
patterns
A Dynamic Additive and Multiplicative Effects Model with Application to the United Nations Voting Behaviors
We introduce a regression model for a series of networks that are correlated
over time. Our model is a dynamic extension of the additive and multiplicative
effects network model (AMEN) of Hoff (2019) In addition to incorporating a
temporal structure, the model accommodates two types of missing data thus
allows the size of the network to vary over time. We demonstrate via
simulations the necessity of various components of the model. We apply the
model to the United Nations General Assembly voting data from 1983 to 2014
(Voeten (2013)) to answer interesting research questions regarding to
international voting behaviors. In addition to finding important factors that
could explain the voting behaviors, the model-estimated additive effects,
multiplicative effects, and their movements reveal meaningful foreign policy
positions and alliances of various countries
Comparing Community Structure to Characteristics in Online Collegiate Social Networks
We study the structure of social networks of students by examining the graphs
of Facebook "friendships" at five American universities at a single point in
time. We investigate each single-institution network's community structure and
employ graphical and quantitative tools, including standardized pair-counting
methods, to measure the correlations between the network communities and a set
of self-identified user characteristics (residence, class year, major, and high
school). We review the basic properties and statistics of the pair-counting
indices employed and recall, in simplified notation, a useful analytical
formula for the z-score of the Rand coefficient. Our study illustrates how to
examine different instances of social networks constructed in similar
environments, emphasizes the array of social forces that combine to form
"communities," and leads to comparative observations about online social lives
that can be used to infer comparisons about offline social structures. In our
illustration of this methodology, we calculate the relative contributions of
different characteristics to the community structure of individual universities
and subsequently compare these relative contributions at different
universities, measuring for example the importance of common high school
affiliation to large state universities and the varying degrees of influence
common major can have on the social structure at different universities. The
heterogeneity of communities that we observe indicates that these networks
typically have multiple organizing factors rather than a single dominant one.Comment: Version 3 (17 pages, 5 multi-part figures), accepted in SIAM Revie
On methods to assess the significance of community structure in networks of financial time series
We consider the problem of determining whether the community structure found by a clustering algorithm applied to financial time series is statistically significant, when no other information than the observed values and a similarity measure among time series is available. We propose two raw-data based methods for assessing robustness of clustering algorithms on time-dependent data linked by a relation of similarity: One based on community scoring functions that quantify some topological property that characterizes ground-truth communities, the other based on random perturbations and quantification of the variation in the community structure. These methodologies are well-established in the realm of unweighted networks; our contribution are versions adapted to complete weighted networks. We reinforce our assessment of the accuracy of the clustering algorithm by testing its performance on synthetic ground-truth communities of time series built through Monte Carlo simulations of VARMA processes
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