Understanding Online Social Networking Services
J.UCS Special Issue
Jason J. Jung
(Department of Computer Engineering
Yeungnam University, Gyeongsan, Korea
j2jung@intelligent.pe.kr)
Przemyslaw Kazienko
(Wroclaw University of Technology
Wroclaw, Poland
Przemyslaw.Kazienko@pwr.wroc.pl)
Since there have been a number of online social networking services
(SNS), we have been experiencing new social phenomena around us. Traditional
social network analysis methods [Gan and Zhu 2007, Jung 2010] might
have difficulties efficiently understanding the large-scale social
data from such SNS [Jung 2012]. Moreover, as the area of social
networks is a highly cross-disciplinary one, we aim to foster and
develop sustainable collaborations between Computer Science and
Informatics, Sociology, Cognitive Science and Psychology, Geographic
and Environmental Science, Biology, and Health and Social Sciences
[Jung 2009a]. This will provide an opportunity to push further the
discussion regarding the potential of social networks and their
applications across these communities. Due to their scale, complexity and
dynamics, these networks are extremely difficult to analyze in terms
of traditional social network analysis methods . On the other hand,
the data about human communication, common activities and
collaboration simultaneously provide new opportunities for new
applications [Jung 2008b, Jung 2008a].
Thus, this special issue is focused on analysis of these large-scale
social structures and what is more important to identify the areas
where social network analysis can be applied and provide the knowledge
that is not accessible for other types of analysis. Additionally,
applications of social networks analysis can be investigated either
from a static or dynamic perspective. We seek for business and
industrial applications of social network analysis that help to solve
real-world problems. The area of social networks analysis and its
applications bring together researchers and practitioners from
different fields and the main goal of this special issue is to provide
an the opportunity to share visions, research
achievements and solutions as well as to establish worldwide
cooperative research and development. At the same time, we want to
provide a platform for discussing research topics underlying the
concepts of social network analysis and its applications by inviting
members of different communities that share this common interest of
investigating social networks.
In the first paper, Wilas Chamlertwat et al. presents the Micro-blog
Sentiment Analysis System (MSAS) by using sentiment analysis to
automatically analyze customer opinions from the Twitter micro-blog
service. To evaluate the system, they conducted experiments on the
collected posts related to smartphones showed that the system could
help indicating the customers' sentiments towards the product
features.
The second paper by Mahalakshmi G.S. et al. proposes the evolution of
a knowledge network from the information available in digital
bibliographic repositories, e.g., DBLP. The most important
characteristic of this knowledge network would be the comprehension of
the proficiency of the scientist in the perspective of an area of
research. This is achieved by categorizing the research articles
published by an author into specific domains. The quality of the
research articles are ascertained by analyzing the abstracts within
the domain. This analysis is used to determine the quality of the
research article in terms of originality, relevancy and thereby, the
impact of the article with respect to a research area.
The third paper by Xuan Hau Pham et al. introduces a concept of social
pulse. They focus on a social tagging system where users can easily
exchange resources as well as their tags with other users. Given a
certain tag from a temporal folksonomy, the social pulse can be
established by counting the number of users (or
resources). Particularly, they discover meaningful relationship
between tags by computing inducibility. To conduct experimentation, a
tag search system has been implemented to collect a dataset from
Flickr.
In the fourth paper, Ting Ting Qin and Satoshi Fujita focus on an
unstructured p2p file sharing system. The aim of this work is to
improve the efficiency of file search in such networks. The proposed
scheme combines text clustering with a modified tag extraction
algorithm, and is executed in a fully distributed manner. The optimal
cluster number can also be fixed automatically through a distance cost
function. We have conducted experiments to evaluate the accuracy of
the proposed scheme.
In the fifth paper, Victor Ströele et al. assume that there are
several inter and intra connections between people in and outside
their organizations. They construct a multi-relational scientific
social network where researchers may have four different types of
relationships with each other. They adopt some criteria such as
relationship age in order to assign a weight to relationships and to
enable the modeling of a scientific social network as close as
possible to reality.
This special issue has been achieved by a number of fruitful
collaborations. We would like to thank the editor in chief of Journal
of Universal Computer Science (JUCS), Hermann Maurer, for his kind
support and help during the entire process of publication. This was
possible thanks to the work of the renowned researchers that provided their anonymous reviews.
Finally, we are most grateful to the authors for their valuable
contributions and for their willingness and efforts to improve their
papers in accordance with the reviewers suggestions and comments.
References
[Gan and Zhu 2007] Gan, Y. and Zhu, Z.: A
learning framework for knowledge building and collective wisdom
advancement in virtual learning communities. Educational Technology
& Society, 10(1):206-226, 2007.
[Jung 2008a] Jung, J.J.: Ontology-based
context synchronization for ad-hoc social
collaborations. Knowledge-Based Systems, 21(7):573-580, 2008.
[Jung 2008b] Jung, J.J.: Query transformation
based on semantic centrality in semantic social network. Journal of
Universal Computer Science, 14(7):1031-1047, 2008.
[Jung 2009a] Jung, J.J.: Contextualized query
sampling to discover semantic resource descriptions on the
web. Information Processing & Management, 45(2):283-290, 2009.
[Jung 2010] Jung, J.J.: Integrating Social Networks for Context Fusion
in Mobile Service Platforms. Journal of Universal Computer Science,
16(15):2099-2110, 2010.
[Jung 2012] Jung, J.J.: Evolutionary Approach for Semantic-based Query
Sampling in Large-scale Information Sources. Information Sciences,
182(1):30-39, 2012.
|