Hayley Trowbridge, University of Liverpool
Hansen, Derek L., Ben Shneiderman, and Marc A. Smith. 2011. Analysing Social Media Networks with NodeXL: Insights from a Connected World, Burling- ton: Morgan Kaufmann, 284 pp. ISBN: 978-0-12-382229-1.
Keywords: social media networks, NodeXL, network visualisations, net- work analysis
Analysing Social Media Networks dia, such as email, Facebook, and with NodeXL: Insights from a Con- Twitter, that have made these net- nected World (2011) explores the works more ‘visible and machine usage of NodeXL, a free template, readable’, thus resulting in new op- designed to be used with Microsoft portunities to map them (p. 3). One Excel, for the analysis of online so- of the outcomes of these new op- cial media networks. Hansen et al portunities has been an explosion of state that ‘social network analysis is literature within the field, including the application of the broader field Computational Social Network Anal- of network science to the study of ysis: Trends, Tools and Research human relationships and connec- Advances (Abraham, Hassanien, tions’ (p. 4). Whilst the authors ac- and Snášel, 2010), which outlines knowledge that this field of research social network tools and explores is relatively new, it has flourished in the central methodologies in social the twenty-first century due to the network analysis, and Connected: ‘new global culture of commonplace The Surprising Power of Our Social network connectivity’, within which Networks and How They Shape Our ‘people have changed their lives by Lives (Christakis and Fowler, 2009), creatively using social media’ (p. which examines how social net- 4). Although social networks them- works impact on our everyday lives. selves predate these technological What differentiates Analysing So- developments designed to mediate cial Media Networks with NodeXL: social interactions, it is precisely Insights from a Connected World the inception of online social me- from this canon of work, is that the
Graduate Journal of Social Science December 2011, Vol. 8, Issue 3 © 2011 by Graduate Journal of Social Science. All Rights Reserved. ISSN: 1572-3763 178 GJSS Vol 8, Issue 3 book focuses on examining how sights’ (p. ix). Whilst NodeXL is not NodeXL can be used to create visu- the first social media analysis tool, it alisations of social networks and as- is one of the most accessible, as the sist in their analysis. With NodeXL, authors of the book acknowledge: researchers can input network data The tools for social media network into a table format and, via the click analysis and visualisation have of a button, produce a customisable been emerging from many re- visualisation of the network. These search groups and start-up com- visualisations assist in the quantita- panies. These pioneering network tive dissection of social networks, analysis tools often require pro- as they visually depict key players gramming skills and knowledge in a network, highlight those partici- of technical network terminology, pants who rarely communicate, dis- making it a challenge for those play participants in a network who without programming skills to im- regularly communicate, and illumi- port and make sense of network nate participants in a network who data (p. ix). link together other people within the network. Building on this quantita- In line with the project’s aim and tive analysis of networks, research- NodeXL’s purpose, the authors ers can move into a more qualita- have written this book with the as- tive analysis by studying the ways in sumption that the readers will ‘have which visualisations of social media no prior knowledge of these topics’ networks may change over time in and with the purpose of introduc- line with social trends and cultural ing readers to social media network changes, and also hypothesise over analysis via the use of NodeXL (p. patterns in human behaviour within 1). Structurally, the book is divided these networks. NodeXL also allows into three sections, which the au- networks to ‘be imported from and thors analogise as being ‘organised exported to a variety of data for- in the form of a tree, with roots, a mats, and built-in connections for trunk and branches’ (p. 1). The roots getting networks from Twitter, Flickr, are chapters one to three, in which YouTube’ (NodeXL, 2011, online). the authors introduce the concepts, The development of the NodeXL theoretical frameworks and litera- template was part of a larger re- ture/historical review of social net- search project funded by Microsoft work analysis; the trunk is chapters Research External Research Proj- four through to seven, in which the ects group, which aimed to ‘substan- practical application of NodeXL is tially lower the barrier to entry for dissected into a ‘how-to’ style guide; social media network analysis while and, finally, the branches are chap- at the same time raising the power ters eight to fifteen, in which various offered to users seeking network in- forms of social media networks are Review: Trowbridge 179 discussed and then analysed via ysis can be used in order to uncover importing information into NodeXL.1 the ways in which the online word- The core theme that underpins of-mouth about films spreads, and the various sections and chap- decipher trends emerging within this ters is the exploration of the social discourse. Researchers from other structures and the organisation of disciplines within the social scienc- various forms of social media, with es could use approaches within this the central argument being that ‘[n] book to uncover trends within online etwork analysis provides powerful communities, decipher how online ways to summarise networks and communities are created and main- identify key people or other objects tained, and about the structures of that occupy strategic locations and these communities. positions within the matrix of links’ One of the main strengths of the (p. 5). Underpinning these matrixes, book is its authors’ and contribu- the authors argue, is a ‘sociotech- tors’ clarity of expression, in terms nical infrastructure’ that ‘influences of both the explanations of spe- social interactions’ (p. 11). In stat- cialist lexis, and in the instructions ing this, Hansen et al insist that they concerning the usage of NodeXL. are not presenting a methodological Some of the book’s features, such approach based on technological as the chapters being preceded by determinism, but instead are recog- concise outlines, key terminology nising that ‘technologies change the explanations, and researchers’ and fabric of the material world, which practitioners’ summaries at the end in turn changes the social world’ (p. of each chapter, assist in making the 12). book accessible to non-computer The authors perceive social me- science-based readers. Additionally, dia network analysis to be a key in- advanced topics within the chapters novation in research methodologies are contained within coloured boxes for various industries and academic allowing for them to be read inde- disciplines. Businesses can use this pendently of the main body of text, methodological approach to high- and key points or subjects are bullet light the participants within their net- pointed clearly or sectioned via bold work who ‘play critical and unique headings. roles’ (p. 4). Scholars from disci- Where the book does fall down, plines such as digital humanities can is in the layout of the instructions also use social media network anal- of how to use NodeXL. Firstly, the ysis to understand the connections instructions are laid out not in a between people and the media/cul- step-by-step format usually found tural artefacts that they are examin- in instruction manuals, but instead ing (p. 6). In my own discipline, film in continuous prose. Secondly the studies, social media network anal- visual instructions of the various 180 GJSS Vol 8, Issue 3 steps are not always located on the contributors suggest various filter- same page as the written instruc- ing techniques to limit the data be- tions, making using the visualisa- ing analysed in any one network. In tions with the written instructions applying these strategies in order to difficult. This seems to undermine make the data more manageable it the foreword to the section in which could be argued that the data being it states that these tutorial chapters analysed is not representative of the could be used in order to teach No- total social network. For example, a deXL to students, in the sense that way of filtering an email social net- the layout is not particularly learner- work could be to remove the infre- centred (p. 51). quent email exchanges from the Identified within Analysing Social visualisation (p. 115). This therefore Media Networks with NodeXL: In- limits the network examination to sights for a Connected World, are one the looks at the agents that fre- limitations with the methodological quently exchange emails. However, approaches presented in the book in acknowledging these strategies and with the NodeXL template itself. for overcoming the template’s limi- Whilst NodeXL does allow for the tations, the authors are making their customisation of the appearance of methodologies transparent, and the visualisations produced, it is only therefore justifying their approach. capable of handling networks of a What Analysing Social Media modest size, which is about several Networks with NodeXL: Insights for thousand individual agents, operat- a Connected World does achieve, ing within any given network (p. 54). to a certain degree, is the democ- 2 This means that larger networks ratisation of social media network cannot be clearly visualised via the analysis. It achieves this aim by template and therefore it would be clearly and concisely summarising difficult to effectively analyse them. research within the field, introduc- Ben Shneiderman (2006) has pro- ing readers to the language of so- posed that when creating visualisa- cial network analysis, instructing the tions of networks, we should strive reader as to how to use NodeXL for to ensure that every agent is visible, social network analysis, and by ex- the number of connections between ploring network analysis across a the agents is countable, every con- variety of high-profile social media nection between each agent is trace- platforms. If what Hansen et al ar- able from start to end and that clus- gue is true, that ‘[s]ocial media allow ters/groups of agents can be easily users to collaboratively create, find, identifiable (summarised in Hansen share, evaluate, and make sense et al, 2011, 47). Consequently, in of the mass of information available order to maintain these standards online…[and]…to connect, inform, and to use NodeXL, the authors and inspire and track other people’, then Review: Trowbridge 181 surely an approach which opens up the analysis of such a phenomena is valuable to the social sciences Web References research community (p. 12). Whilst NodeXL (2011)
Endnotes 1 These chapters include studies written by the authors themselves and contributions from other aca- demics working within the field. 2 These agents, known as verti- ces or nodes, can be anything from people or organisations, to states and countries (p.34).
References Abraham, Ajith, Aboul-Ella Hassanien, and Václav Snášel, Computa- tional Social Network Analysis: Trends, Tools and Research Ad- vances, London: Springer-Verlag London, 2010.
Christakis, Nicholas A and James H. Fowler, Connected: The Surpris- ing Power of Our Social Networks and How They Shape Our Lives, London: Little, Brown and Com- pany, 2009.
Shneiderman, Ben and Aleks Aris, Net- work visualisation with seman- tic substrates, IEEE Symposium on Information Visualisation and IEEE Trans, Visualisation and Computer Graphics, Volume 12, Number 5, pp733 – 740, 2006.