Exploring social media relationships
Derek L. Hansen
Abstract
Purpose – The purpose of this paper is to demonstrate novel techniques for exploring relationship data
extracted from social media sites for actionable insights by educators, researchers, and administrators.
Design/methodology/approach – The paper demonstrates how non-programmers can use NodeXL,
an open source social network analysis tool built into Excel 2007/2010, to collect, analyze, and visualize
network data from social media sites like Twitter and YouTube.
Findings – Researchers and education professionals can use NodeXL to explore (a) social networks to
identify important individuals and subgroups, as well as (b) content networks to map the underlying
structure of a domain and find important content. Illustrative examples are provided using NodeXL to
examine followers of a Twitter user focused on open education, as well as a content network of YouTube
videos about surgery.
Research limitations/implications – Tools like NodeXL are making network analysis accessible to
non-technical researchers in a variety of fields spanning the sciences, social sciences, and the
humanities. Despite their value, network analysis techniques are only as good as the data that underlie
them, requiring careful assessment of possible selection biases and triangulation of findings.
Practical implications – Educational institutions and educators can benefit from more systematically
analyzing their social media initiatives from a network perspective.
Originality/value – This paper describes some of the techniques and tools needed to make sense of
the social relationships that underlie social media sites. As relational data are increasingly made public,
such techniques will enable more systematic analysis by researchers studying social phenomena and
practitioners implementing social media initiatives.
Keywords Networking, Worldwide web, Multimedia
Paper type Conceptual paper
Introduction
We are increasingly surrounded by a sea of tweets, e-mails, blogs, wiki pages, videos, and
wall posts. Although challenging to navigate, this sea of information can take us to previously
unseen places filled with new insights and opportunities. A growing number of social media
explorers are developing the tools and methods needed to chart this mysterious sea. These
powerful and increasingly usable tools, accompanied by large public social media datasets,
are bringing in a golden age of social science by empowering researchers to measure social
behavior on a scale never before possible. This article describes new tools andmethods that
help researchers, educators, students, and administrators gain actionable insights from
social media data.
Social media datasets are especially useful for mapping relationships between
people. Social networking sites like Twitter, Facebook, and Classroom 2.0 allow members
to friend, follow, or become a fan of other members. These explicitly created ties can be
aggregated into a social map, or network graph, that identifies who is connected to or
interested in whom. Other social media platforms capture implicit ties between people, as for
DOI 10.1108/10748121111107726 VOL. 19 NO. 1 2011, pp. 43-51, Q Emerald Group Publishing Limited, ISSN 1074-8121 j ON THE HORIZON j PAGE 43
Derek L. Hansen is an
Assistant Professor at the
College of Information
Studies and Director of the
Center for the Advanced
Study of Communities and
Information, University of
Maryland, College Park,
Maryland, USA.
The author thanks Microsoft
Research Cambridge, Ben
Shneiderman, and the
anonymous reviewers who
provided useful feedback on an
earlier version.

example when two colleagues exchange e-mail messages. Whether explicit or implicit, this
relational data is hard to capture by traditional means such as surveys. As social media tools
become more prevalent and integrated into our everyday lives and educational
experiences, the relational data they record will become an increasingly accurate
reflection of real-world social and communication networks. Even today, Facebook ties are
accurate representations of real-world friendship ties among college students
(Subrahmanyam et al., 2008) and organizational e-mail exchange networks closely mirror
real-world working and personal connections.
Exploration of the relationships embedded in social media can be useful to a number of
groups. Educational researchers can use them to evaluate the relationships between social
structures and educational outcomes in social media-enabled communities and distance
education settings. Learners, such as doctoral students, can identify people they would like
to connect with to advance their long-term integration into new communities of practice.
Educational administrators and communication experts can explore social media
relationships to understand how they affect enrollment, retention, and alumni donations.
Network analysis goes mainstream
The study of relational data has a rich history among social scientists and mathematicians,
who have gained important insights into the social structure of our society (Freeman, 2004).
Social science analysts have developed the methods and metrics to systematically
characterize different types of networks, identify subgroups (i.e. clusters) of people, and
highlight the most central (i.e. important) people within a social network (Wasserman and
Faust, 1998). Meanwhile, mathematicians, physicists, and computer scientists have
developed graph theory and network models, the mathematical foundations that underlie
the study of networks (Newman, 2010). The study of social network analysis (SNA) has
become increasingly interdisciplinary, largely driven by the opportunity to study large-scale
social media datasets. It has also been popularized in books such as Linked (Barabasi,
2003) and Connected (Christakis and Fowler, 2009) that chronicle recent developments in
network analysis. Since the early days of the internet, scholars such as Haythornthwaite
(1998, 2002) have applied SNA to online learning environments, focusing on the structural
relationships that support learning communities.
Until recently, SNA has remained an esoteric research topic performed by academics or
professionals in highly specialized fields such as intelligence analysis. A number of
sophisticated tools have been developed to support the exploration of networked data.
These tools allow users to calculate network metrics that identify important people based on
their position in the network (e.g. the most ‘‘popular’’ individuals), identify clusters of people
that are highly inter-connected, and characterize entire networks (e.g. measure the overall
level of connectedness or ‘‘density’’). They also allow users to visualize networks, helping
users identify patterns that are hard to see in the raw data tables and matrices.
Unfortunately, many SNA tools require knowledge of obscure terminology and programming
skills not directly related to the domain of interest. This is beginning to change as an
increasing number of free, open-source tools such as NodeXL (see http://nodexl.codeplex.
com) and Gephi (see http://gephi.org/) allow people to more easily capture, visualize, and
analyze network data.
For example, NodeXL, a plugin for Microsoft Excel 2007/2010, allows users to import
network data directly from social media tools like e-mail and Twitter, calculate network
metrics, manipulate the raw data using the familiar spreadsheet paradigm, and visualize the
network in a separate graph pane. The largely non-technical graduate students in my online
communities class have created insightful analyses and visualizations using NodeXL with
only a basic tutorial, 2-3 weeks of effort, and minimal educational scaffolding (Bonsignore
et al., 2009). Student feedback suggests that learning NodeXL and the corresponding
network concepts is challenging, but immensely rewarding and useful. Several students and
recent graduates have found innovative ways to apply their network analysis skills,
PAGE 44 jON THE HORIZONj VOL. 19 NO. 1 2011