Social Media as Seismic Networks for the Earthquake Damage Assessment

Monday, 15 December 2014
Carlo Meletti1, Stefano Cresci2, Mariantonietta N. La Polla2, Andrea Marchetti2 and Maurizio Tesconi2, (1)National Institute of Geophysics and Volcanology, Rome, Italy, (2)Institute for Informatics and Telematics - National Research Council, Pisa, Italy
The growing popularity of online platforms, based on user-generated content, is gradually creating a digital world that mirrors the physical world. In the paradigm of crowdsensing, the crowd becomes a distributed network of sensors that allows us to understand real life events at a quasi-real-time rate. The SoS-Social Sensing project [http://socialsensing.it/] exploits the opportunistic crowdsensing, involving users in the sensing process in a minimal way, for social media emergency management purposes in order to obtain a very fast, but still reliable, detection of emergency dimension to face. First of all we designed and implemented a decision support system for the detection and the damage assessment of earthquakes. Our system exploits the messages shared in real-time on Twitter. In the detection phase, data mining and natural language processing techniques are firstly adopted to select meaningful and comprehensive sets of tweets. Then we applied a burst detection algorithm in order to promptly identify outbreaking seismic events. Using georeferenced tweets and reported locality names, a rough epicentral determination is also possible. The results, compared to Italian INGV official reports, show that the system is able to detect, within seconds, events of a magnitude in the region of 3.5 with a precision of 75% and a recall of 81,82%. We then focused our attention on damage assessment phase. We investigated the possibility to exploit social media data to estimate earthquake intensity. We designed a set of predictive linear models and evaluated their ability to map the intensity of worldwide earthquakes. The models build on a dataset of almost 5 million tweets exploited to compute our earthquake features, and more than 7,000 globally distributed earthquakes data, acquired in a semi-automatic way from USGS, serving as ground truth. We extracted 45 distinct features falling into four categories: profile, tweet, time and linguistic. We run diagnostic tests and simulations on generated models to assess their significance and avoid overfitting. Overall results show a correlation between the messages shared in social media and intensity estimations based on online survey data (CDI).