LAUSR

Social network data, utilised as a VGI source, was analysed using the September 2016 flood event in Messinia, South Greece. The flood event led to damage in the urban and rural environment in the general area, and to human deaths. An innovative methodology is based on applying machine learning to classify Twitter content. Tweets were classified into the following ten categories: (i) flood identification, (ii) rain identification, (iii) consequences of the flood, (iv) expressed emotions, (v) ironic attitude to local disaster management authorities, (vi) disaster management information, (vii); volunteer actions, (viii); situation overview, (ix); social effects, and (x); weather information. Some of the categories were divided further, to quantify significant information. The classified output was sequentially geo-referenced by identifying geographic entities within the text of each post (geo-parsing) and replicating each post according to the number of geolocations. The data processing involved various geo-validations and performance metrics. The final output was used to create maps and graphs of different time periods, that provide useful insights into the flood event for disaster management purposes. The applied methodology is an evolution of previous research published by the author, this time providing complete results, based on the analysis of 100 % of the data available, with maps and graphs which demonstrate how the flood event unfolded in different time periods. The methodology is fully automated in terms of data processing, and can be applied using a script developed by the author in the R programming language. This research is a step towards the real-time delivery of advanced information for all disaster management stakeholders, from official authorities and rescue teams, to volunteers and locals who may be situated within the area of a disastrous flood occurrence.