Valentina Grasso

Codified hashtags for weather warning on Twitter: an Italian case study

Introduction: 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. Keywords: social media, emergency management, Twitter, severe weather

Predicting TV programme audience by using twitter based metrics

The predictive capabilities of metrics based on Twitter data have been stressed in different fields: business, health, market, politics, etc. In specific cases, a deeper analysis is required to create useful metrics and models with predicting capabilities. In this paper, a set of metrics based on Twitter data have been identified and presented in order to predict the audience of scheduled television programmes, where the audience is highly involved such as it occurs with reality shows (i.e., X Factor and Pechino Express, in Italy). Identified suitable metrics are based on the volume of tweets, the distribution of linguistic elements, the volume of distinct users involved in tweeting, and the sentiment analysis of tweets. On this ground a number of predictive models have been identified and compared. The resulting method has been selected in the context of a validation and assessment by using real data, with the aim of building a flexible framework able to exploit the predicting capabilities of social media data. Further details are reported about the method adopted to build models which focus on the identification of predictors by their statistical significance. Experiments have been based on the collected Twitter data by using Twitter Vigilance platform, which is presented in this paper, as well.

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

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 weather related disasters, also due to climatic changes. Many works showed that during such events people tend to share messages by means of social media platforms, especially Twitter. Not only they contribute to ”situational” awareness, 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 activity volumes across keywords and hashtags, including geo-names. Impacting events present a recognizable visual pattern recalling a ”Half Onion Shape”, where Twitter activity across keywords is synchronized. In reason of these interesting indications, it’s becoming fundamental to have a suite of reliable 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.