Social Media Mining

flickr photo by ianguest shared under a Creative Commons (BY-NC-SA) license

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.

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