Enhancing Resilience to Disasters using Social Media

Emmanouil Chaniotakis, Constantinos Antoniou, Francisco Camara Pereira

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    During the last decade, Social Media (SM) have emerged as a prominent trend in social communication, with online platforms such as Facebook and Twitter to conquer the internet space with millions of visitors per day. SM usage generates an astonishing amount of information, which could be used for scarcely experienced situations, such as mass convergence and emergency events. This study presents a preliminary exploratory analysis on examining the capacity of Social Media to extract information on individuals choices during evacuation. We collect tweets from the evacuation in Oroville, California USA due to danger of flood and the evacuation. The data is used for the creation of a user sample which allows the collection of historical data The historical data is compared with the data collected during and after the evacuation. The goal of this comparison is the extraction of potential information related to the evacuation.
    Original languageEnglish
    Title of host publicationProceedings of the 2017 5th Ieee International Conference on Models and Technologies for Intelligent Transportation Systems (mt-its)
    PublisherIEEE
    Publication date2017
    Pages699-703
    DOIs
    Publication statusPublished - 2017
    Event5th Ieee International Conference on Models and Technologies for Intelligent Transportation Systems (mt-its) - Naples, Italy
    Duration: 26 Jun 201728 Jun 2017

    Conference

    Conference5th Ieee International Conference on Models and Technologies for Intelligent Transportation Systems (mt-its)
    CountryItaly
    CityNaples
    Period26/06/201728/06/2017

    Cite this

    Chaniotakis, E., Antoniou, C., & Pereira, F. C. (2017). Enhancing Resilience to Disasters using Social Media. In Proceedings of the 2017 5th Ieee International Conference on Models and Technologies for Intelligent Transportation Systems (mt-its) (pp. 699-703). IEEE. https://doi.org/10.1109/MTITS.2017.8005602