64,336 research outputs found
Do narcissism and emotional intelligence win us friends? Modeling dynamics of peer popularity using inferential network analysis
This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study we examined the dynamics of popularity in 15 peer groups in two waves (N=273).We measured narcissism, ability EI, explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and three months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity
The happiness paradox: your friends are happier than you
Most individuals in social networks experience a so-called Friendship
Paradox: they are less popular than their friends on average. This effect may
explain recent findings that widespread social network media use leads to
reduced happiness. However the relation between popularity and happiness is
poorly understood. A Friendship paradox does not necessarily imply a Happiness
paradox where most individuals are less happy than their friends. Here we
report the first direct observation of a significant Happiness Paradox in a
large-scale online social network of Twitter users. Our results reveal
that popular individuals are indeed happier and that a majority of individuals
experience a significant Happiness paradox. The magnitude of the latter effect
is shaped by complex interactions between individual popularity, happiness, and
the fact that users cluster assortatively by level of happiness. Our results
indicate that the topology of online social networks and the distribution of
happiness in some populations can cause widespread psycho-social effects that
affect the well-being of billions of individuals.Comment: 15 pages, 3 figures, 2 table
A place-focused model for social networks in cities
The focused organization theory of social ties proposes that the structure of
human social networks can be arranged around extra-network foci, which can
include shared physical spaces such as homes, workplaces, restaurants, and so
on. Until now, this has been difficult to investigate on a large scale, but the
huge volume of data available from online location-based social services now
makes it possible to examine the friendships and mobility of many thousands of
people, and to investigate the relationship between meetings at places and the
structure of the social network. In this paper, we analyze a large dataset from
Foursquare, the most popular online location-based social network. We examine
the properties of city-based social networks, finding that they have common
structural properties, and that the category of place where two people meet has
very strong influence on the likelihood of their being friends. Inspired by
these observations in combination with the focused organization theory, we then
present a model to generate city-level social networks, and show that it
produces networks with the structural properties seen in empirical data.Comment: 13 pages, 7 figures. IEEE/ASE SocialCom 201
A Relational Hyperlink Analysis of an Online Social Movement
In this paper we propose relational hyperlink analysis (RHA) as a distinct approach for empirical social science research into hyperlink networks on the World Wide Web. We demonstrate this approach, which employs the ideas and techniques of social network analysis (in particular, exponential random graph modeling), in a study of the hyperlinking behaviors of Australian asylum advocacy groups. We show that compared with the commonly-used hyperlink counts regression approach, relational hyperlink analysis can lead to fundamentally different conclusions about the social processes underpinning hyperlinking behavior. In particular, in trying to understand why social ties are formed, counts regressions may over-estimate the role of actor attributes in the formation of hyperlinks when endogenous, purely structural network effects are not taken into account. Our analysis involves an innovative joint use of two software programs: VOSON, for the automated retrieval and processing of considerable quantities of hyperlink data, and LPNet, for the statistical modeling of social network data. Together, VOSON and LPNet enable new and unique research into social networks in the online world, and our paper highlights the importance of complementary research tools for social science research into the web
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