Connections, connections

flickr photo by GotCredit shared under a Creative Commons (BY) license

Catching up on a few podcasts today, I came across an edition of the Teachers’ Education Review in which an old online buddy and highly reflective educator, Aaron Davis was musing on Twitter. (You can listen in to Aaron’s piece by following the link and sliding forward to 24:11)

Aaron opened by noting that he often heard educators claim that ‘Every teacher needs to be on Twitter,’ but he questioned whether Twitter was the right answer? Is it really the best option for online professional development?

Although Twitter provided the opening for which seeded his personal learning network, it soon outlived its usefulness and he found other tools which better provided for his needs. Feedly in particular automatically aggregates content from multiple sources, allowing it to be easily skimmed and categorised for future reference. Diigo, a social bookmarking tool, provides another powerful way of curating information which can be shared amongst others and forge a developing resource upon which you can later draw. Several other tools were also mentioned, but Aaron observed that in the end, it came down to personal preference.

Another criticism leveled at Twitter, that Aaron echoed, is that it imposes limitations on the depth of dialogue possible. An alternative which provides greater freedom of expression is Voxer which, since it involves sharing audio, conveys a greater sense of humanity. Aaron also questioned a report “‘Follow’ Me: Networked Professional Learning for Teachers” (Holmes et al, 2013), wondering why it focused solely on Twitter, and ignored other platforms. What might prove more illuminating he argued is research into the impact of being connected, however that might be facilitated.

It’s interesting that although Twitter provided the point of entry, it’s shortcomings quickly became apparent for Aaron. Other tools afforded better ways to enable the learning opportunities and make the connections he was seeking.

There are a number of things that struck me in what Aaron had to say:

The limitations of Twitter as a medium for dialogue. This is a criticism I’ve heard many make and an interesting point I will need to interrogate further. What is a constriction for some in some ways is celebrated by others as a useful affordance.

The importance of connections and how different tools allow them to be made in different ways. This forefronts the  sociomateriality of the professional learning he undertakes and perhaps provides encouragement for me choosing actor-network theory as a methodological framework. It will allow me to ‘follow the actors’ Aaron references in the podcast and build up a picture of the extent of those assemblages and the part they play in professional learning.

It’s clear that although Twitter might be an entry point (both for individuals looking to learn professionally and for me commencing an ethnography), the boundaries of the ‘site’ are likely to be incredibly fluid and potentially elusive. Earlier today, I started Christine Hine’s new book ‘Ethnography for the Internet’ (2015) in which she proposes (p24) the idea:

…that ethnography can be focused on following connections, rather than being focused on a specific place.

It seems that both Aaron and Christine are guiding me in a particular direction.

Final thought. At around 08:30 I was reading the quote in the book, which had been written by Christine Hine months earlier presumably. An hour or so later I was listening to Aaron Davis from Australia on my mp3 player whilst I was out running alongside the Chesterfield canal (which can be checked on my gps tracking application). After a little mental processing, here I am committing thoughts to (digital) paper a few hours later. From there … where … when … who … how?

I wonder what an actor-network theory analysis would make of all that?

Hine, C. (2015). Ethnography for the Internet: Embedded, Embodied and Everyday. Bloomsbury Publishing.

Holmes, K., Preston, G., Shaw, K., & Buchanan, R. (2013). ‘Follow’ Me: Networked Professional Learning for Teachers. Australian Journal of Teacher Education, 38(12).

Discourse of Twitter and Social Media

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

Most of the books I’ve been reading are assisting my understanding of the topics that are central to my study. Others are in areas which I hadn’t initially considered, but might inform my research. ‘Discourse of Twitter and Social Media’ (Zappavigna, 2012) perhaps surprisingly falls into the latter group. Yes it may be on Twitter, but I hadn’t really thought about discourse analysis as a possible method, mainly because although I was aware of it, I didn’t really appreciate what it entailed … which is why I chose the book.

Dealing with the issues of corpora for social media and then outlining the specific features of microblogging, Zapavigna then uses successive chapters to explore in detail how people share their experiences and enact relationships by adopting a social semiotic perspective. Covering memes, slang, humour and political discourse, she provides illustrative examples under each category, together with the analytical linguistic techniques used. A particularly resonant chapter for me was that on ambient affiliation, seen as an alternative mechanism through which people with a shared set of values and interests become associated, other than as a community. This bonding is achieved through common behaviours and simple yet powerful semiotic markers like the hashtag. Affiliations may be more transient than communities, virtual or otherwise, and be time-bound to specific incidents or issues. Whilst others form, stick and show durability over time. We begin to see here the importance of what Zappavigna calls ‘interpersonal search’ where people employ the affordances of the medium to actively (and passively?) seek out other people with whom to bond around (or clash over!) certain issues.

Another aspect of the book I found valuable was the way in which many of the technical elements of microblogging were scrutinized. In addition to the aforementioned hashtag, the functions served by the @ and RT were also discussed. This naturally prompts me to consider here what ANT role these components play; actors, mediators, intermediaries? Although the part played by emoticons was also discussed, I wondered whether these markers worked at a different level than #, @ and RT, instead serving more as semiotic replacements for the visual cues lacking in online settings. Do emoticons perform a significant functional role in professional learning exchanges? I suspect not, but it might be interesting to find out.

One small topic which caught my attention was ways in which we can classify Twitter users as information sources, friends or information seekers (Armentano et al, 2011), or indeed as informers or meformers (Naaman et al, 2010). This seems particularly relevant to my study where people often claim that one element of the professional learning they derive from Twitter is that it is a wonderful source of resources. Are they the information seekers who rely on the information sources? Are some of those information sources also meformers? Should we extend or amend the categories to resource sources and resource seekers?

An area which has been flitting in and out of my mind and one which I will need to discuss at much greater length is my approach to sampling. Zappavinga highlighted how episodic the nature of the Twitterstream is and how that influences the choices you need to make when devising a sampling strategy. The 100 million word HERMES corpus used throughout most of the book was collected over a relatively short period from the whole Twitter population and therefore is to some extent skewed by what was topical at the time (among other things, Valentine’s Day apparently!). Since I’m focusing on a particular issue, the whole population is of less interest to me than those who are teachers or involved in education (need to think more about what my target group will be). How I establish that target, devise a meaningful time-period and thereby compose my Twitter snapshot will require careful and informed consideration, as indeed of course will the subsequent analysis.

One finding in the book which I found reassuring was that the most common 3-gram (3-word triplet) in the HERMES corpus was “Thanks for the”, which suggests that people are indeed getting something from their fellow Twitter users. Perhaps that 3-gram might be worth investigating within the samples I choose to use, as an indication of resource and information sharing? It might be interesting therefore to follow that up with what appears in the R1 position i.e. is the next word in the sequence.

What I learned

  • Some of the techniques and processes involved in discourse analysis.
  • A better understanding and appreciation of the importance of some of the specialised lexis which Twitter users employ.
  • How the medium was set up mainly to allow broadcasting and that engaging in dialogue or co-ordinating action may be consequently be more difficult to achieve.

What I need to do next

  • To give further consideration to distinctions, similarities and crossovers between affiliations, communities and actor-networks.
  • To explore further texts which will help me think about sampling strategies.
  • To learn more about discourse analysis and whether there are elements (like n-grams) I might be able to fruitfully use.


Armentano, M.G., Godoy, D.L., Amandi, A.A., 2011. A topology-based approach for followees recommendation in Twitter, in: Workshop Chairs. p. 22. (PDF)

Naaman, M., Boase, J., Lai, C.-H., 2010. Is it really about me?: message content in social awareness streams, in: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work. ACM, pp. 189–192. (PDF)

Zappavigna, M., 2012. Discourse of Twitter and social media: How we use language to create affiliation on the web. A&C Black.

#NSMNSS chat

When I recently became aware of NSMNSS (New Social Media, New Social Science?) via a YouTube video I happened by:

the first thing I did was to subscribe to their channel, followed swiftly after by following their blog. That was how I became aware that they have a monthly Twitter chat (#NSMNSS), so found myself in my first chat with a new community; one of researchers, rather than the educators with whom I feel more at one.

It wasn’t an entirely comfortable experience, coming to terms as I am with being a ‘new’ researcher, but then nor should it have been. What I do expect however is that (assuming the chat continues) I should become more comfortable in the company, but perhaps more importantly shift the balance increasingly towards being a contributor.

The topic for the session again was for me, a timely one, thinking as I am about potential tools for mining data. The questions covered during the hour:

  1. What experience do you have of collecting data from different social media platforms? What tools do you use?
  2. What are some of your favorite tools for collecting, and or analysing social media data?
  3. What features would you like to see social media tools incorporate? What features do you already use?
  4. What do you think is the biggest barrier in using a tool? What could be done to improve accessibility?
  5. How should we interpret data collected via social media?

Although at this stage, I had little to contribute to 1, 2 and 3, the responses from others (including two tool providers, @Chorus_Team & @nodexl) provided some incredibly useful ideas for further exploration. What Q3 did provoke me to do though was to think of what features I would want from a data collection tool – although I quickly remembered that a Twitter chat affords little time for a brain with a clock speed as slow as mine to undertake that an exercise like that. One for later.

Even with my brief exposure to this aspect of my study, my answer to Q4 mirrored @SportMgmtProf‘s:

though would expand the latter point by adding complexity. Those tools I’ve encountered so far have incredibly steep learning curves it seems, not only from a deployment perspective, but also from an analytical and interpretive one too. Yes we can gather the data, but how do we make sense of it and use the outputs to tell a story? Which links nicely with my response to Q5 which is that there should be alignment with one’s research questions and objectives; the interpretation will have been determined by those questions, which suggested potential methods (and tools) and therefore leads the interpretation. I also heeded the caution of @BSADigitalSoc:

In writing this post and checking back for a couple of links, I was surprised and delighted to find that the same chat topic was being repeated for those in the AEST time zone on the other side of the world. That’s the first chat I’ve seen do that, so now I have a second stream to scan, albeit at a more sedate pace.

Observations: An hour of my time well spent; one which rewarded me with a number of positive outcomes:

  • I always find it rewarding connecting with like-minded others, especially those from whom I can learn.
  • I was made aware of a number of tools which might offer new opportunities.
  • If social network analysis becomes a significant part of my studies, then this training course in programming for social media researchers might prove a useful find, assuming that it’s on in the future.

(The session chat has been archived using Storify here)