Last week (week 8) we were tasked with visualizing Twitter data from IDEL using TAGS explorer. The task involved searching the hashtag #mscidel. The resulting visualisation looked like this:

screen-shot-2016-11-16-at-17-09-58

I then tried some alternative search criteria and ran the script again. However, the resulting visualisation appeared to confuse the results of both searches. I would need to play more with the script to investigate what the issue was here.

The initial visualisation demonstrated a mismatch in the potential for Twitter data harvesting data and the relatively small scale activity around the #mscidel hashtag. I would imagine a lot more could be gleaned from a MOOC hashtag for example.

It’s also worth bearing in mind that all this tells us of course is who has typed #mscidel in a tweet. It doesn’t tell us who is on the course (although it is unlikely that it would be used by someone not enrolled on the course). It also doesn’t tell us everyone enrolled on the course. Many course participants either won’t have a Twitter account or simply won’t have tweeted using that particular hashtag. And how do we account for a possible hashjacking?

So we know what data we have. But what data do we need? Are we using Twitter to harvest data just because we can? What questions are we trying to answer? Who is we? Do we understand why we are collecting this data? Who is ultimately benefitting from the collection of this data? To whom is the data being made available? Finally, and perhaps most importantly, what are the ethical considerations of this activity? Harvesting data from Twitter appears at first uncontroversial. Twitter is an open platform and each tweet can be considered a publication.  However, Michael Zimmer raises some interesting points in his blog post Is it ethical to harvest public twitter accounts without consent? Can we really assume that those who tweet do so understanding how their data may be used? And even if we can conclude that we don’t require to seek specific consent from Tweeters to harvest their data, how do we suppose this data will be used? I was particularly interested to read of Militello et al.’s (2013) study which showed the contrast between how different groups responded to data (Selwyn 2015 p.71). If Education researchers are to use Twitter APIs, these are the kinds of questions we need to keep at the forefront of our minds.