You’ve focussed on the statistical measures here, and I wondered if you could say a little more about what you think the implications of this might be. I know that you’ve stated which kinds of things you are comfortable with, but I’m wondering more about the general premise that we compare ourselves to ‘average use’. What does that really mean for a service like Twitter? How has this organisation decided on those particular averages, and what understandings of social media are driving that? It seems to me that ‘tweeting 2 times a week’ is more about a kind of corporate social media strategy, than considering the educative value of reading and/or posting to Twitter. So the broader question here is, who and what are constructing the ‘normal’ Twitter user, and what might that mean for educational use of the service?

Your focus on the stats is also interesting, as one of the core offerings of QuillConnect is the data-to-text computation. This is a burgeoning area of algorithmic web development, where articles are ‘written’ automatically from lots of different kinds of web data. (I was at a data-to-text event earlier this week in Edinburgh http://www.macs.hw.ac.uk/InteractionLab/d2t/). Ed shared this Tweet recently which is typical of this data-to-text field, an article ‘written’ from stock market data:

Here are articles written by algorithms — amazing: http://t.co/oPaTaDSyz7 #mscedc

— Ed Guzman (@emberday) March 10, 2015

Other big developments include sports articles written from various sources of data like sports stats and social media comments.

So, what are the issues around writing text about your Twitter activity? Does that make it more believable? Some of this work is motivated by the idea that stats are simply to ‘dry’, and that people prefer written accounts of activity.