Preprint Article Version 1 This version is not peer-reviewed

Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

Version 1 : Received: 15 August 2018 / Approved: 15 August 2018 / Online: 15 August 2018 (11:34:43 CEST)

How to cite: Hernandez-Suarez, A.; Sanchez-Perez, G.; Toscano-Medina, K.; Perez-Meana, H.; Portillo-Portillo, J.; Sanchez, V. Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data. Preprints 2018, 2018080269 (doi: 10.20944/preprints201808.0269.v1). Hernandez-Suarez, A.; Sanchez-Perez, G.; Toscano-Medina, K.; Perez-Meana, H.; Portillo-Portillo, J.; Sanchez, V. Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data. Preprints 2018, 2018080269 (doi: 10.20944/preprints201808.0269.v1).

Abstract

In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user’s description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

Subject Areas

social sensing; supervised learning; statistical methods; social networks; twitter; tweets; natural disaster; random forest, kernel density estimation

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