Guest editorial
| DOI | https://doi.org/10.1108/IDD-08-2017-0067 |
| Published date | 21 August 2017 |
| Pages | 109-109 |
| Date | 21 August 2017 |
| Author | J. Jenny Li,I-Hsien Ting,Charles Perez |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
Guest editorial
Digital information supply chain for social
media data
Social media and social network are undoubtedly the most
popular current topics, not only in information technology
and computer science but also in other disciplines such as
business and politics. Social media data aggregates rapidly,
and the resources and formats vary widely. Social media data
are therefore claimed as the best instance of “Big Data”. Thus,
how to manage delivery process and analyze the data from
social media is a challenging problem.
Most of existing studies in this area focus on how to analyze
social media data without worrying about difficulties in
managing Big Data. This special issue aims to fill this gap by
covering the topic of Big Data management and its
accompanying technologies and tools. This special issue has
been designed to focus on the topic of information supply
chain for social media data, which has been proved to be
probably the best way to deal with such data. This special issue
includes a total of five papers: from the 7th Workshop in
Mining and Analyzing Social Networks Data for Decision
Support, the 3rd Multidisciplinary International Social
Networks Conference, and a few received as responses to call
for papers.
The first paper is Kamil Topal and Gultekin Ozsoyoglu’s
“Emotional classification and visualization of movies based on
their IMDb reviews”. The authors apply the visualization
approach to classify emotion based on the reviews available in
a very famous movie database IMDb. To cluster movies
according to different dimensions of reviewers’ emotion, the
k-means clustering algorithm is used. Finally, various
visualization techniques are used to present the results of
clustering in order to test the proposed approach.
The second paper is Laurens Elkin, Kamil Topal and
Gurkan Bebek’s “Network based model of social media big
data predicts contagious disease diffusion”. This is an
interesting paper discussing the prediction of the diffusion of
contagious disease by network-based model of social media
Big Data. To develop the model, location factors are
considered as the most important factors. The network model
provides a prediction, and possible warning, to hospitals to
help disease control and prevention.
The third paper is Kuo-Cheng Ting, Ruei-Ping Wang,
Yi-Chung Chen, Don-Lin Yang and His-Min Chen’s
“Finding m-similar users in social networks using the
m-representative skyline query”. The authors are trying to find
m-similar users in social networks based on users’ input search
result. The approach that authors used in the paper is a
so-called m-representative skyline algorithm, and the two base
algorithms are naïve algorithm and R-tree algorithm. Finally,
the authors use a simulation approach to test the performance
of the proposed approach.
The fourth paper is Basit Shahzad, Sagib M. Nawaz, Waqar
Aslam, Raza Mustafa and Atif Mashkoor’s “Discovery and
classification of user interests on social media”. The focus of
the paper is on the discovery and classification of user interests
on social media, which is a hot topic in the area of social
networks analysis. In the paper, data from twitter are used as
the data source. The proposed methodology includes data
labeling, modeling, machine learning (SVM) and
visualization. The authors also evaluate the proposed
approach by comparing other classifiers.
The fifth paper is Liang Liu, Bin Chen, Wangchun Jiang,
Lingnan He and Xiaogang Qiu’s “Spatio-temporal dynamics
of web pages diffused in WeChat”. In the last paper of the
special issue, the authors focus on analyzing the
spatio-temporal dynamics diffusion of web pages in WeChat,
which is a widely used messenger service in China.
Topological analysis, temporal analysis and spatial analysis are
the three macroscopic analyses that form the major part of the
paper. The authors use different visualization means to
present the results.
All the five papers in the special issue fall within the scope of
the issue and cover the most popular topics and trends in
digital information supply chain for social media data analysis.
The editors of the special issue believe that these papers
positively contribute to the journal of information discovery
and delivery.
J. Jenny Li, I-Hsien Ting and Charles Perez
Information Discovery and Delivery
45/3 (2017) 109
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-08-2017-0067]
109
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