57,960 research outputs found
Metrics for privacy assessment when sharing information in online social networks
(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Privacy risk in Online Social Networks has become an important social concern. Users, with different perceptions of risk, share information without considering the audience that has access to the information disclosed or how far a publication will go. According to this, we propose two metrics (Audience and Reachability) based on information flows and friendship layers that indicate the privacy risk of sharing information, addressing the posts¿ scope and invisible audience. We assess these metrics through agent simulations in well-known models of networks. The findings show a strong relationship between metrics and structural centrality network properties. We also studied scenarios where there is no previous information about users activity or the information about the traces of the messages cannot be obtained. To deal with privacy assessment in these scenarios, we analyze the relationship between the proposed privacy metrics and local centrality properties as an estimation of privacy risk. The results showed that effectiveness centrality can be used as a suitable approximation of the proposed privacy measures.This work was supported in part by the Spanish Government project under Grant TIN2017-89156-R, and in part by the FPI under Grant BES-2015-074498.Alemany-Bordera, J.; Del Val Noguera, E.; Alberola Oltra, JM.; García-Fornes, A. (2019). Metrics for privacy assessment when sharing information in online social networks. IEEE Access. 7:143631-143645. https://doi.org/10.1109/ACCESS.2019.2944723S143631143645
Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks
Social networks existing among employees, customers or users of various IT
systems have become one of the research areas of growing importance. A social
network consists of nodes - social entities and edges linking pairs of nodes.
In regular, one-layered social networks, two nodes - i.e. people are connected
with a single edge whereas in the multi-layered social networks, there may be
many links of different types for a pair of nodes. Nowadays data about people
and their interactions, which exists in all social media, provides information
about many different types of relationships within one network. Analysing this
data one can obtain knowledge not only about the structure and characteristics
of the network but also gain understanding about semantic of human relations.
Are they direct or not? Do people tend to sustain single or multiple relations
with a given person? What types of communication is the most important for
them? Answers to these and more questions enable us to draw conclusions about
semantic of human interactions. Unfortunately, most of the methods used for
social network analysis (SNA) may be applied only to one-layered social
networks. Thus, some new structural measures for multi-layered social networks
are proposed in the paper, in particular: cross-layer clustering coefficient,
cross-layer degree centrality and various versions of multi-layered degree
centralities. Authors also investigated the dynamics of multi-layered
neighbourhood for five different layers within the social network. The
evaluation of the presented concepts on the real-world dataset is presented.
The measures proposed in the paper may directly be used to various methods for
collective classification, in which nodes are assigned to labels according to
their structural input features.Comment: 16 pages, International Journal of Computational Intelligence System
Modeling Paying Behavior in Game Social Networks
Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy
Does woman + a network = career progression?
Question: I am an ambitious and talented junior manager who has recently been hired by FAB plc, a large multinational company. I am also a woman and, as part of my induction pack, have received an invitation to join FABFemmes - the in-company women's network. I don't think my gender has been an obstacle to my success thus far and so I don't really feel the need to join. But on the other hand I don't want to turn my back on something that might offer me a useful source of contacts to help me advance up the career ladder. What would be the best thing to do? - Ms Ambitious, UK
Stay Awhile and Listen: User Interactions in a Crowdsourced Platform Offering Emotional Support
Internet and online-based social systems are rising as the dominant mode of
communication in society. However, the public or semi-private environment under
which most online communications operate under do not make them suitable
channels for speaking with others about personal or emotional problems. This
has led to the emergence of online platforms for emotional support offering
free, anonymous, and confidential conversations with live listeners. Yet very
little is known about the way these platforms are utilized, and if their
features and design foster strong user engagement. This paper explores the
utilization and the interaction features of hundreds of thousands of users on 7
Cups of Tea, a leading online platform offering online emotional support. It
dissects the level of activity of hundreds of thousands of users, the patterns
by which they engage in conversation with each other, and uses machine learning
methods to find factors promoting engagement. The study may be the first to
measure activities and interactions in a large-scale online social system that
fosters peer-to-peer emotional support
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