Utilising social network analysis to study the characteristics and functions of the co-occurrence network of online tags
| Date | 25 February 2014 |
| DOI | https://doi.org/10.1108/OIR-11-2012-0124 |
| Pages | 232-247 |
| Published date | 25 February 2014 |
| Author | Ma Feicheng,Li Yating |
| Subject Matter | Library & information science,Information behaviour & retrieval |
Utilising social network analysis
to study the characteristics and
functions of the co-occurrence
network of online tags
Ma Feicheng and Li Yating
Centre for Studies of Information Resources, Wuhan University, Wuhan,
People’s Republic of China
Abstract
Purpose – This paper aims to explore the characteristics of the co-occurrence network of online tags
and propose new approaches of applying social network analysis by utilising social tagging in order to
organise data.
Design/methodology/approach – The authors collected online resources labelled “tag” from 7
November 2004 to 31 October 2011 from the CiteULike website, comprising 684 papers and their URLs,
titles and data on tagging (users, times, and tags). They examined the co-occurrence network of online
tags by using the analyses of social networks, including the analysis of coherence, the analysis of
centricity and core to periphery categorical analysis.
Findings – Some features of the co-occurrence of online tags are as follows: the internet is subject to
the “small world” phenomenon, as well as being “scale-free”. The structure of the internet reflects
stable areas of core knowledge. In addition to five possible applications of social network analysis,
social tagging has the greatest significance in organising online resources.
Originality/value – This research finds that co-occurrence of tags online is an effective way to
organise and index data. Some suggestions are provided on the organisation of online resources.
Keywords Social networkanalysis, CiteULike, Folksonomy,Recommendation, Social tagging,
Tag co-occurrencenetwork
Paper type Research paper
Introduction
Social tagging or folksonomy is one of the essential features of many web 2.0 services.
It can be accurate, flexible, open, and interesting. It is how users categorise their own
papers, pictures, audio works, videos, or a series of documents (Mathes, 2004). The
flexibility of tagging enables users to annotate web resources according to their own
needs and understanding. Tag-based clustering, tag clouds, and other methods of
organisation have been used widely, and have achieved some success in organising
and managing internet databases with a large number of users (Tang and Quan, 2008).
However despite its popularity, it lacks semantics, therefore some in-depth research on
recognising users’ demands and foci is still needed.
Social network analysis (SNA) is a quantitative method of analysis developed by
sociologists, based on mathematical models and graph theory. Currently social
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1468-4527.htm
This study was partially supported by the National Natural Foundation of China (Grant
No. 71173249). The authors are grateful for the translation support from Ruan Lin, who teaches
in the School of Foreign Languages and Literature at Wuhan University.
OIR
38,2
232
Received 11 December 2012
First revision approved 14
April 2013
Online Information Review
Vol. 38 No. 2, 2014
pp. 232-247
qEmerald Group Publishing Limited
1468-4527
DOI 10.1108/OIR-11-2012-0124
network analysis has wide usage in metrology. SNA has matured and it is often used in
co-author or co-citation analysis, and other relevant applications (Wang et al., 2006).
This method can efficiently analyse the development of networks in a globa l context
from multiple perspectives, where it is able to judge the hierarchy and semantics of
networks and clarify various internal relationships within the system.
This paper examines social tags and their co-occurrence relationships within the
structure of an online network, using the social network analysis method to analyse the
basic characteristics of the network, and then explores new approaches and new ways
to use SNA to organise and index information using social tags. This research is of
great significance to the organisation of online data.
Related work
Since its inception, social tagging has been of great interest to scholars and research
institutions. Early research mainly focused on the characteristics and common
tendencies of tags,such as their ambiguities, parallels,and growth trends. Mathes (2004)
compared and analysed user-created metadata with professional and author-created
metadata, and then described user-generated metadata as implemented and applied
using two web services designed to share and organise digital media in order to better
understand their fundamental classification. Golder and Huberman (2006) analysed the
structure of collaborative tagging systems as well as their dynamic aspects, and
presented a dynamic model of collaborative tagging that predicts the stability in these
states. Cattuto et al. (2007) analysed the main characteristics of two online social media
sharing systems, YouTube and Delicious (formerly del.icio.us.), and measured variables
such as characteristic path lengths and clustering coefficients. These studies confirmed
the unique advantages of folksonomy in the organisation of online resources.
Recently a lot of workhas been done in this field, because of theincreasing interest in
the gathering of data about tags by researchers and institutions, including the
construction of user models (Yeung et al., 2008), tag forecasting (Russell, 2006),
navigation and retrieval (Begelman et al., 2006; Cui and Liu, 2007), recommendations
(Sigurbjo
¨rnsson and Van Zwol, 2008), as well as the ontology of tagging (Mika, 2007;
Kim et al., 2008). Macgregor and McCulloch (2006) analysed the applicability of tags for
data management and resource discovery in summarising their research on social
tagging. Shepitsen et al. (2008) pointed out that tag clusters can be used asthe basis for
an effective personalised recommendation that assists users in navigation. They
presented an algorithm that establishes personalised recommendations in folksonomies
that relies on the hierarchical ordering of tag clusters. Derntl et al. (2011) investigated the
web 2.0 phenomenon of socialtagging in the context of existing approachesto semantic
data structuring, where they proposed a concept called “inclusive social tagging,” and
subsequently assessed the tagging system in web 2.0 services.
Most of these studies focus on the conceptual model of tagging, such as tag clouds
and tag clusters, but in recent years more research has been done on the occurrence of
tags using SNA. Some research discussed the users’ behaviour when they use a social
tagging system. Hsieh and Chiu (2011) designed a database and used it to compare four
network indicators in order to understand the structural differences in how tags are
used to organise resources. Ke and Chen (2012) identified patterns and structures in the
social tagging of scholarly papers in CiteULike and built a network to understand
users’ data organisation behaviour. Other studies centred on how to org anise online
Co-occurrence
network of online
tags
233
To continue reading
Request your trial