#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)


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