ntroduction: During emergencies increasing numbers of messages are shared through social media platforms becoming a primary source of information for lay people and emergency managers. For Twitter codified hashtagging is emerging as a practical way to coordinate messages during emergencies and quickly identify relevant information. This paper considers a case study on the use of codified hashtags concerning weather warning in Italy in three different regions. Methods: From November 3rd to December 2nd 2014, tweets identified by the 3 codified hashtags #allertameteoTOS, #allertameteoLIG and #allertameteoPIE were retrieved, collecting a total of 35,558 tweets published by 7361 unique tweets authors, with the aim to assess if codified hashtags could represent an effective way to align formal and informal sources of information during weather related emergencies. An auxiliary R-package was built to lead the analytics used in this study. Authors performed a manual coding of users, hashtags and content of messages of all Twitter data considered. Results: Content analysis showed that tweets were overwhelmingly related to situational updates, with a high percentage containing geo-location information. Communication patterns of different user types were discussed for the three contexts. In accordance with previous studies, individuals showed an active participation primarily functioning as information hub during the emergency. Discussion: In the proposed cases codified hashtags have proven to be an effective tool to convey useful information on Twitter by formal and informal sources. Where institutions supported the use of the predefined hashtag in communication activities, like in Tuscany, messages were very focused, with more than 90% of tweets being situational updates. In this perspective, use of codified hashtags may potentially improve the performance of systems for automatic information retrieval and processing during disasters.

Codified Hashtags for Weather Warning on Twitter: an Italian Case Study / Grasso, Valentina; Crisci, Alfonso. - ELETTRONICO. - 1:(2016), pp. 0-0.

Codified Hashtags for Weather Warning on Twitter: an Italian Case Study

GRASSO, VALENTINA;
2016

Abstract

ntroduction: During emergencies increasing numbers of messages are shared through social media platforms becoming a primary source of information for lay people and emergency managers. For Twitter codified hashtagging is emerging as a practical way to coordinate messages during emergencies and quickly identify relevant information. This paper considers a case study on the use of codified hashtags concerning weather warning in Italy in three different regions. Methods: From November 3rd to December 2nd 2014, tweets identified by the 3 codified hashtags #allertameteoTOS, #allertameteoLIG and #allertameteoPIE were retrieved, collecting a total of 35,558 tweets published by 7361 unique tweets authors, with the aim to assess if codified hashtags could represent an effective way to align formal and informal sources of information during weather related emergencies. An auxiliary R-package was built to lead the analytics used in this study. Authors performed a manual coding of users, hashtags and content of messages of all Twitter data considered. Results: Content analysis showed that tweets were overwhelmingly related to situational updates, with a high percentage containing geo-location information. Communication patterns of different user types were discussed for the three contexts. In accordance with previous studies, individuals showed an active participation primarily functioning as information hub during the emergency. Discussion: In the proposed cases codified hashtags have proven to be an effective tool to convey useful information on Twitter by formal and informal sources. Where institutions supported the use of the predefined hashtag in communication activities, like in Tuscany, messages were very focused, with more than 90% of tweets being situational updates. In this perspective, use of codified hashtags may potentially improve the performance of systems for automatic information retrieval and processing during disasters.
2016
1
0
0
Grasso, Valentina; Crisci, Alfonso
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1055604
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