20,004 research outputs found
Determination of Friendship Intensity between Online Social Network Users Based on Their Interaction
Online social networks (OSN) are one of the most popular forms of modern
communication and among the best known is Facebook. Information about the
connection between users on the OSN is often very scarce. It's only known if
users are connected, while the intensity of the connection is unknown. The aim
of the research described was to determine and quantify friendship intensity
between OSN users based on analysis of their interaction. We built a
mathematical model, which uses: supervised machine learning algorithm Random
Forest, experimentally determined importance of communication parameters and
coefficients for every interaction parameter based on answers of research
conducted through a survey. Taking user opinion into consideration while
designing a model for calculation of friendship intensity is a novel approach
in opposition to previous researches from literature. Accuracy of the proposed
model was verified on the example of determining a better friend in the offered
pair
Exploratory Analysis of Pairwise Interactions in Online Social Networks
In the last few decades sociologists were trying to explain human behaviour
by analysing social networks, which requires access to data about interpersonal
relationships. This represented a big obstacle in this research field until the
emergence of online social networks (OSNs), which vastly facilitated the
process of collecting such data. Nowadays, by crawling public profiles on OSNs,
it is possible to build a social graph where "friends" on OSN become
represented as connected nodes. OSN connection does not necessarily indicate a
close real-life relationship, but using OSN interaction records may reveal
real-life relationship intensities, a topic which inspired a number of recent
researches. Still, published research currently lacks an extensive exploratory
analysis of OSN interaction records, i.e. a comprehensive overview of users'
interaction via different ways of OSN interaction. In this paper we provide
such an overview by leveraging results of conducted extensive social experiment
which managed to collect records for over 3,200 Facebook users interacting with
over 1,400,000 of their friends. Our exploratory analysis focuses on extracting
population distributions and correlation parameters for 13 interaction
parameters, providing valuable insight in online social network interaction for
future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4
on pages 422 to 42
Risks of Friendships on Social Networks
In this paper, we explore the risks of friends in social networks caused by
their friendship patterns, by using real life social network data and starting
from a previously defined risk model. Particularly, we observe that risks of
friendships can be mined by analyzing users' attitude towards friends of
friends. This allows us to give new insights into friendship and risk dynamics
on social networks.Comment: 10 pages, 8 figures, 3 tables. To Appear in the 2012 IEEE
International Conference on Data Mining (ICDM
Exploring Image Virality in Google Plus
Reactions to posts in an online social network show different dynamics
depending on several textual features of the corresponding content. Do similar
dynamics exist when images are posted? Exploiting a novel dataset of posts,
gathered from the most popular Google+ users, we try to give an answer to such
a question. We describe several virality phenomena that emerge when taking into
account visual characteristics of images (such as orientation, mean saturation,
etc.). We also provide hypotheses and potential explanations for the dynamics
behind them, and include cases for which common-sense expectations do not hold
true in our experiments.Comment: 8 pages, 8 figures. IEEE/ASE SocialCom 201
The ethics of digital well-being: a multidisciplinary perspective
This chapter serves as an introduction to the edited collection of the same name, which includes chapters that explore digital well-being from a range of disciplinary perspectives, including philosophy, psychology, economics, health care, and education. The purpose of this introductory chapter is to provide a short primer on the different disciplinary approaches to the study of well-being. To supplement this primer, we also invited key experts from several disciplines—philosophy, psychology, public policy, and health care—to share their thoughts on what they believe are the most important open questions and ethical issues for the multi-disciplinary study of digital well-being. We also introduce and discuss several themes that we believe will be fundamental to the ongoing study of digital well-being: digital gratitude, automated interventions, and sustainable co-well-being
Élőlények kollektív viselkedésének statisztikus fizikája = Statistical physics of the collective behaviour of organisms
Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. Experiments: We have carried out quantitative experiments on the collective motion of cells as a function of their density. A sharp transition could be observed from the random motility in sparse cultures to the flocking of dense islands of cells. Using ultra light GPS devices developed by us, we have determined the existing hierarchical relations within a flock of 10 homing pigeons. Modelling: From the simulations of our new model of flocking we concluded that the information exchange between particles was maximal at the critical point, in which the interplay of such factors as the level of noise, the tendency to follow the direction and the acceleration of others results in large fluctuations. Analysis: We have proposed a novel link-density based approach to finding overlapping communities in large networks. The algorithm used for the implementation of this technique is very efficient for most real networks, and provides full statistics quickly. Correspondingly, we have developed a by now popular, user-friendly, freely downloadable software for finding overlapping communities. Extending our method to the time-dependent regime, we found that large groups in evolving networks persist for longer if they are capable of dynamically altering their membership, thus, an ability to change the group composition results in better adaptability. We also showed that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime
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