The GeoBurst algorithm detects local news events by looking for spatiotemporal ‘bursts’ of activity. This cluster analysis uses methods which look at geo-tag clusters of phrases.
Phrase network analysis has been able to historically link user clouds, however the use of GPS in mobile devices has led many users of social media to indicate their wherabouts on a reliable basis. Clusters appear not only in the spatial proximity of phrases, but also in their temporal proximity. This is being compared to a recent history which is sampled from a ‘sliding frame’ of historic phrases.
Possible changes may emerge as I rework the sampling process, in order to account for larger historic contextualization from previous years of data, in order to compare seasonal events, such as famous weather systems or sports. In the case of my research, the events are sports (specifically Football). This is because sports are temporal events on Twitter which happen in a simultaneous manner in the USA, giving me lots of clusters to look at. Though politics would be a fun topic, it is not resolved well in my dataset which dates to 2013.
The pursuit of GeoBurst is eventually to work towards disaster relief, however the behaviour of humans may arguably not be directed to social media in some disasters. The objective being that existing cyberGIS infrastructure may benefit from social media and be used to inform disaster response decision making.
In the mean time, it’s time to get GeoBurst running and looking at the Twitter API.