Saturday, 31 May 2014

Some life analysis with Twitter

There was a great post recently on Flowing Data, The Change My Son Brought, Seen Through Personal Data. It got me thinking about what my life looks like through personal data, and probably the best source of data since the advent of smartphones is Twitter. Twitter recently made it possible to download your personal archive and it makes for some interesting analysis. Along with RSS feeds, Twitter is my major source of online news, education and entertainment, and it is also useful for personal communications and microblogging.

Downloading your personal archive is easy, but you need to do a little manipulation before you can play with it. My tweets were time-stamped in UTC time (I'm not sure why - perhaps by default, perhaps because of my location settings) so I had to adjust this for time zone changes due to day-light savings and overseas trips (I didn't bother with domestic trips as I don't have an easy record of them, and they don't make too much difference - an hour here and there).

The following has a dot for every tweet I've written since the end of 2010. Take note that the x-axis is quite long (3.5 years) and the dots are quite large (bigger than a day). I haven't annotated it, but it is interesting to spot life events - the birth of my children, various periods of leave and holidays, over-tweeting during The Ashes etc. There are auto-tweets that came out at the same time each week (which I've now stopped as they're annoying). There was a definite shift in the time I rise in the morning after December 2010 when my son was born and a surge in late-night tweets after my daughter was born in 2013.


Breaking it down is a little more interesting. The following shows tweet frequency for work days and non-work days (weekends, leave) since the start of 2013. On a work day, I tweet in the main on the train. I usually catch a train around 7 or 8am in the morning and the return train around 5 or 6pm. During work hours there is a trickle through coffee breaks and lunch, and after dinner is another peak. This type of profile aligns somewhat with the findings of other social media studies (Yellow Social Media Report – 2014 - thanks @problogger), although the amount I tweet on the train is more than the norm, whilst the amount I tweet at work is less (although it is a great way to horizon scan the various fields of science in which I work, once you follow the right people).

Non-work days follow a different profile, at least until after dinner. There's a slightly later rise in the morning, dips when we would be attempting to get out of the house, a dip at an earlier dinner time and a large peak in the evening once the kids are in bed. This peak is higher than a work day, in which time I might be preparing for the next day or falling asleep on the couch. By about 10pm it is basically the same till 6am the next day.


I'm posting this at about 9am on a weekend, having written it at about 10pm last night - that fits the curve pretty well. If you are a social media marketer (of which, at last count, there are 1,083,645,638 on Twitter), target my work trips (although I'm sure you know this from all that stunning big data analysis you do). The downside of this is that the train trip is too short to read anything of any length, which would explain why the 140 characters of Twitter spikes at these times.

No comments:

Post a Comment