Weather events identification in social media streams: tools to detect their evidence in Twitter

CNR, Italian National Research Council, Institute of Biometeorology, Florence, Italy
LaMMA Consortium, Tuscany Region - CNR, Sesto Fiorentino, Italy
DISIT Lab, Distributed Technologies Lab - Dep. of Information Engineering (DINFO), University of Florence, Firenze, Italy
Ibimet, CNR, Firenze, Italy
Italian National Research Council, IBIMET - Istitute of Biometeorology, Firenze, Italy
DOI
10.7287/peerj.preprints.2241v2
Subject Areas
Data Science, Emerging Technologies, Natural Language and Speech, Network Science and Online Social Networks
Keywords
weather event identification, twitter, social media data, twitter dashboard
Copyright
© 2017 Grasso et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Grasso V, Zaza I, Zabini F, Pantaleo G, Nesi P, Crisci A. 2017. Weather events identification in social media streams: tools to detect their evidence in Twitter. PeerJ Preprints 5:e2241v2

Abstract

Severe weather impact identification and monitoring through social media data is a good challenge for data science. In last years we assisted to an increase of natural disasters, also due to climate change. Many works showed that during such events people tend to share specific messages by of mean of social media platforms, especially Twitter. Not only they contribute to"situational" awareness also improving the dissemination of information during emergency but can be used to assess social impact of crisis events. We present in this work preliminary findings concerning how temporal distribution of weather related messages may help the identification of severe events that impacted a community. Severe weather events are recognizable by observing the synchronization of twitter streams volumes concerning extractions by using different but semantically graduate terms and hash-tags including the specific containing geo-content names. Impacting events seems immediately recognizable by graphical representation of weather streams and when the time-line show a specific parallel-wise pattern that we named "Half Onion Shape". Different but weather semantically linked twitter streams could exhibits different magnitude, in order to their term popularity, but they show, when a weather event occurs, the same temporal relative maximum. In reason of to these interesting indications, that needs to be confirmed through more deeper analysis, and of the great use of social media, as Twitter, during crisis events it's becoming fundamental to have a suite of suitable tools to monitor social media data. For Twitter data a comprehensive suite of tools is presented: the DISIT-Twitter Vigilance Platform for twitter data retrieve,management and visualization.

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Supplemental Information

Weather DISIT Channel statistics and search terms

DOI: 10.7287/peerj.preprints.2241v2/supp-1

Twitter Vigilance Rossano Calabro Event

DOI: 10.7287/peerj.preprints.2241v2/supp-2

Twitter Vigilance Interface of "Codified Hash-tag Allerta" channel

DOI: 10.7287/peerj.preprints.2241v2/supp-3

Twitter Vigilance Olbia flooding Event

DOI: 10.7287/peerj.preprints.2241v2/supp-4

Twitter Vigilance Severe weather Firenze Event

DOI: 10.7287/peerj.preprints.2241v2/supp-5

Twitter Vigliance Software Architecture

DOI: 10.7287/peerj.preprints.2241v2/supp-6