Let\u2019s start by looking at the total registrations over the time period in question, a couple of different ways. The two charts below show both the average ID registered on the day (i.e. where in the Twitter ID range the users are), as well as the number of users who registered their account on each of those days.<\/p>\n
<\/p>\n
<\/p>\n <\/p>\n As you can see, there are two dates where the pattern looks odd, namely 1 February and 18 February. Taking a closer look at the first of these, and registrations on a per-minute basis, we see a rather odd pattern that appears to show re-occurring outages:<\/p>\n We have yet to discover if this is an error with our data, or represents periods where twitter was either down or overloaded, but it does not appear to impact directly on the analysis that follows.<\/p>\n Registrations during Queensland Floods:<\/strong><\/p>\n Let\u2019s begin our detailed analysis with a closer look at the Queensland floods. There are a few different ways of attempting to locate users on Twitter, the most obvious being geolocation, although we didn’t attempt that for this analysis as it is well established that the percentage of users with geolocation on is historically around 1%. Another approach is to look for users with \u201cBrisbane\u201d (or similar) in their location field, while it is also possible to look at the time zone a Twitter user has set for their account, and at the UTC offset applied to their account. Let\u2019s start with the location field and UTC offset:<\/p>\n <\/p>\n It is immediately clear that \u201cBrisbane\u201d in the location field is not a good proxy. Even allowing for other cities and regions where the +36000 offset is applicable, the difference between 220 registrations and over 1200 is obvious. Both charts however show a spike around the time of the floods (which peaked in Brisbane on 13 January), while the UTC offset chart also shows a peak around the time of the NZ earthquake, and the recovery that followed. Drilling deeper than the UTC offset, and not quite as deep as matching text in the location, is the time zone field, so let\u2019s take a look at that:<\/p>\n Here, again, we see large spikes for Brisbane, Melbourne and Sydney during the floods, and spikes particularly for Sydney and Melbourne around the NZ earthquake. So it appears clear that for the floods, not only was Twitter a source of information on the floods, but the floods were a driver of new registrations for Twitter, from users seeking to obtain that information and possibly contribute to the discussion.<\/p>\n Registrations during Christchurch Earthquake:<\/strong><\/p>\n So, how about the Christchurch earthquake, which hit on 22 February? Well, we see a similar pattern. A spike in the +43200 UTC time offset, and in both the Auckland and Wellington time zones, shows the natural disaster was a clear driver of new registrations to Twitter. The second spike shown here, on 11 March, coincides with the Tokyo Earthquake & Tsunami, which posed a potential Tsunami threat to New Zealand.<\/p>\n <\/p>\n Given that Twitter does not have a \u201cChristchurch\u201d specific location field, I repeated the text matching experiment here. But, while the spikes are obvious, the numbers are too low for this to be considered a reliable method of user identification.<\/p>\n Registrations during Tokyo Earthquake & Tsunami:<\/strong><\/p>\n So, on to the Tsunami, and a familiar pattern is visible, albeit one much greater in magnitude most likely due to the sheer population in and around Tokyo:<\/p>\n As you can see from the above chart, the rise in new registrations as a result of the earthquake and tsunami was clearly extended here substantially into the recovery period, with increased numbers of registrations evident right through to the end of March. We already identified a spike in New Zealand registrations during this period, but the effect was also felt in other regions that may have potentially been impacted by the tsunami, as seen below:<\/p>\n Registrations during the Egyptian Revolution.<\/strong><\/p>\n Finally, given that we had the data, it made sense to look at Cairo and Egypt, around the time of the Egyptian revolution on 25 January. Just as with the natural disasters above, the revolution proved to drive users to Twitter, as highlighted in the graphs below:<\/p>\n <\/p>\n![\"\"](\"https:\/\/mappingonlinepublics.net\/dev\/wp-content\/uploads\/2013\/11\/TotalRegs_ID.png\")
<\/a><\/strong><\/p>\n<\/a><\/strong><\/p>\n<\/a><\/strong><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/p>\n<\/a><\/strong><\/p>\n<\/a><\/strong><\/p>\n<\/a><\/strong><\/p>\n<\/a><\/strong><\/p>\n<\/a><\/strong><\/p>\n<\/a><\/p>\n