Whilst out for a run this week, I was catching up my podcast listening. On my playlist was Episode 91 of Data Stories in which the creators of RAW were sharing what is, what it does and how it came into being. RAW claims to be ‘The missing link between spreadsheets and data visualization.’ Back when I wrote my research proposal, I thought that social network analysis (SNA) would be one technique I might use to learn more about teacher learning on Twitter. There are a raft of tools that can help with this, which exist on a spectrum from those which rely on having expertise in coding, to those (like TAGS and NodeXL) which are usable by novice like me. In addition to gathering tweets, they often allow you to produce visualisations of the connections between those tweets:
Early in my study, I toyed with the idea of conducting social network analysis as the means to explore how people might be interconnected online and what implications different forms of connection might cause.. I attended this session to push the boundaries of my knowledge somewhat and see how researchers are using network analysis techniques; was there something I’ve missed as I’ve moved away from my original plan?
The range of topics were suitably eclectic and spanned: audience brokerage by the media in Spain; sustaining a car enthusiast online community in Thailand; what we can begin to learn from the whole Twittersphere of Australia; and the structures of online communities in Russia. Silvia Majo-Vazquez, Shih-Yun Chen, Axel Brun and Yuri Rykov took us around the world in just over an hour.
Interesting though the tools, techniques and topics were, it helped me to recognise two things about my own research. Firstly, that this isn’t a method that can easily be bolted on to my study without a major commitment to understanding the theory behind the way the tools work and the meanings of the emergent patterns in the data. I suspect I could achieve a surface understanding and capability, but I’m not entirely sure I could get to the stage where I could defend myself in a viva. Moreover, I’m not at all convinced that a network understanding would be appropriate for my study, given the turn in my thinking.
SNA sure produces entrancing pictures, but would they reveal anything about the realities of my participants?
Based on the Learning How to Learn project, this book explores the issues around how networks can help improve teacher learning and practice.
The authors seek to distinguish between communities, which are useful in knowledge creation, and networks which have the power to share knowledge more widely. The intent was to shed light on factors such as network technologies and infrastructure, policies and practices, teacher capability and confidence (in using networks), whether new forms of networking are driven by the demands for knowledge sharing and if new networking practices reflect current patterns of collaboration.
They employed a hybrid model of analysis, assembled using concepts/interpretations such as social network analysis (SNA), social capital, small worlds, actor-network theory (ANT) and in so doing, sought to determine whether it is appropriate to use networks as analytical tools or simply as metaphors. Participants in the project were invited to illustrate how they visualised the project-related networks in which they were involved. The diagrams generated provided the initial data for analysis. Despite being widely disparate in form, the illustrations lent themselves to being interpreted largely as ego-centric networks in which certain individuals are key. Although SNA would seem to be the most obvious lens to bring to bear, the authors drew from it only those terms and concepts which proved more informative, leaving behind the more quantitative elements which were felt to be less revealing.Read More »
Social Media Mining starts from the premise that with social media being so pervasive, with such a large proportion of our population engaged in it and with the ease of posting, enormous quantities of data are being generated. This provides both opportunities and challenges.
Social Media Mining is the process of representing, analyzing and extracting actionable patterns from social media data.
The challenges include gathering the data, whilst ensuring they are in a format which can be processed; devising procedures which will render the data in a format which they can be interpreted; and bringing to bear appropriate frameworks which allow meaning to emerge.
On page five we’re informed that those “with a basic computer science background and knowledge of data structures, search and graph algorithms will find this book easily accessible” and “having a data mining or machine learning background is a plus.” Hmm, that didn’t bode well. However, nothing ventured as they say. The book takes you through the essentials of processing and representing the data in ways which facilitate interpretation. To do this and illustrate how this might be achieved, it draws on set theory, linear algebra, calculus and constructs algorithms. These are not areas in which I’m experienced (the undergraduate maths I used was mostly that demanded by classical physics), but if it proved necessary, I could probably get grips with them. The question is whether I will need to. If social network analysis does indeed prove to be a useful method, then my first instinct would be to look for tools already available; there’s no point investing time developing an application if someone’s already done that heavy lifting. If I need to pursue an line of enquiry for which nothing is currently available, then my next step might be to explore potential partnerships with someone who has the expertise. Are there any undergraduates studying in this area who are looking for a real-world project to fulfill their course criteria for example? If not and the outcome of my research depended on it, then it would just be a matter of rolling up my sleeves and getting stuck in.
Where I found the book really useful was in introducing some of the concepts and terminology which would be needed to represent and interpret the data. Nodes, edges, centrality, transitivity, reciprocity and assortativity are useful ideas when discussing concepts embodied within social media. The book goes on to to discuss how we can identify communities, how they form, how they are interconnected and how information travels within and across them. Both emic (explicit) and etic (implicit) communities are discussed, though there’s little exploration of what the intent or purpose of communities might be. I’m left pondering on the significance of communities within my study. Facebook, LinkedIn & Google+ all have group features, whereas Twitter does not. Curious then that despite Twitter apparently failing to provide the means through which communities might self-organise, it appears to be the dominant SNS to which teachers have been attracted. Why might that be? Although only discussed briefly in the preceding post, the hashtag seems to be an important actor here and a potential candidate around which communities might assemble. Perhaps too, the hashtag community (if it exists), cocks a snook at the emic/etic dichotomy?
Through explorations of ‘friendship’ networks, we learn, not only how to recognise ‘influentials,’ but how to measure their potency through the notions of ‘influence’ and ‘homophily.’ The ‘twitterati‘ certainly exist, even within the education-oriented subset of Twitter users. What role then, if any, do they have in the context of professional learning? Are they hubs around which actions occur? Are they champions or in any sense leaders? Connected with influence and homophily, ‘confounding,’ is the environment‘s effect on making people similar. Assuming similarity might be an important factor in promoting interaction (not at all necessarily a given), how does the environment encourage that? By introducing a non-human actor, I acknowledge that I’m well on the way to recruiting actor-network theory.
I may or may not turn to social media mining as a method, but whatever the outcome, many of the underlying principles might prove useful. What I definitely need to resolve is whether to hold on to the notion of communities and explore how they might integrate with an actor-network theory interpretation, or whether I should lose that terminology and translate those ideas into solely ANTish terms and ANTish analyses.
It’s becoming increasingly clear that in addition to the conventional literature, other sources of information are proving fruitful as I familiarise myself with those aspects of my study that are new to me. Whilst searching for materials related to Social Network Analysis (SNA), it became clear that there were a number of videos, often available through YouTube, which might assist my studies. In some cases these were produced by students, perhaps in fulfilling the requirements of an assignment they had been set, whilst others were recordings of lectures and seminars, often by lecturers noted in the field, or by other researchers employing SNA within their study. This range and variety proved particularly helpful, often providing a snapshot or an insight which would have taken much longer for me to tease out through reading the conventional literature. Does that make me lazy, or am I simply making effective use of my time during these initial exploratory forays?
Here I just wanted to leave a notional bookmark to which I can refer back should it prove appropriate to incorporate SNA into my study. This video shows a workshop from a conference in which the facilitator, Michael Bauer takes the audience through the stages of identifying, gathering, tidying, processing and analysing data from Twitter.
It’s one particular technique using a specific set of (open) tools (Gephi, Refine), but Michael generously provided all the instructions necessary for those of us not present to subsequently follow the same procedures. From that brief video (OK, it was an hour and a half), I gained:
awareness of two powerful (and open) tools for exploring networks
insights into the kinds of information they might yield
awareness of the ‘School of Data,’ which teaches “… data wrangling skills by doing. Work with real data, real people, real world issues.” It provides a series of free online courses, for people new to and experienced with managing and analysing large amounts of data.
In a few short minutes then, not only had I been made aware of fresh, exciting possibilities, but I also had the means to develop the capability to use them.
Observation – It was interesting to note the temporal and spatial displacement of my learning compared with that of the workshop participants and to reflect on the efficacy of the experiences we shared. Since Michael responded to requests from the floor, the workshop was imbued with a rather stuttering flow, one which my learning also reflected, since I was able to determine my own pace. Perhaps then time/place-shifted learning (and professional development brings certain advantages?
Marginal Notes 