Public crowdsensing of heat waves by social media data

Abstract

Abstract. Investigating on society-related heat wave hazards is a global issue concerning the people health. In the last two decades, Europe experienced several severe heat wave episodes with catastrophic effects in term of human mortality (2003, 2010 and 2015). Recent climate investigations confirm that this threat will represent a key issue for the resiliency of urban communities in next decades. Several important mitigation actions (Heat-Health Action Plans) against heat hazards have been already implemented in some WHO (World Health Organization) European region member states to encourage preparedness and response to extreme heat events. Nowadays, social media (SM) offer new opportunities to indirectly measure the impact of heat waves on society. Using the crowdsensing concept, a micro-blogging platform like Twitter may be used as a distributed network of mobile sensors that react to external events by exchanging messages (tweets). This work presents a preliminary analysis of tweets related to heat waves that occurred in Italy in summer 2015. Using TwitterVigilance dashboard, developed by the University of Florence, a sample of tweets related to heat conditions was retrieved, stored and analyzed for main features. Significant associations between the daily increase in tweets and extreme temperatures were presented. The daily volume of Twitter users and messages revealed to be a valuable indicator of heat wave impact at the local level, in urban areas. Furthermore, with the help of Generalized Additive Model (GAM), the volume of tweets in certain locations has been used to estimate thresholds of local discomfort conditions. These city-specific thresholds are the result of dissimilar climatic conditions and risk cultures.

Assessing the reTweet proneness of tweets: predictive models for retweeting

“…Opinions pools and politic elections predictions have been proposed to be correlated with the volume of tweets by using Sentiment Analysis techniques in O'Connor et al [43]. Different models based on volume of tweets and other means have been also used for predicting purposes: voting results in Bermingham and Smeaton [3] and in Tumasjan et al [56], economics [4,15], marketability of consumer goods [50], public health seasonal flu [1,34,51], box-office revenues for movies [2,36,38,54], crimes [58], book sales [26], recommendations on places to be visited [14] and weather forecast information [24,25]. Moreover, Twitter-based metrics have been used to predict and estimate the number of people in some location, such as airports, the so-called crowd size estimation by the work of Botta et al [5], as well as 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) [17].…”

“…The derived metrics and information can be useful to understand which are the most widely used or emerging hashtags, as well to detect which are the most influential in determining the positive/negative signature and polarity detection in the sentiment analysis, and thus for better tuning the tweet collected and for precomputing basic metrics that can be useful for the researcher to make further analysis in different domains and generically for communication and media, predictive models [24,25]. It can be a useful tool for identifying reasons for positive/negative tweets, as well as the reaction of the community.…”

Analyzing Spatial and Temporal User Behavior in Participatory Sensing

“…Until now, the factors that drive the participants to contribute their observed data remain unclear [54]. Stimulating factors such as incentives [33], gamification techniques [55] and their internal willingness [56] have been studied, but we found few works [57][58][59][60][61] investigating the spatial and temporal behavior of participants. City Probe [57] is an application designed to help citizens in reporting certain city issues, whereby the exact location (latitude and longitude) of a report submission was stored.…”

“…In their work, Grasso et al extracted the information on heat waves from social media data (tweets) and examined the relation between the social media activities and the spatio-temporal pattern of the heat waves [58]. Due to the extremely low number of tweets with geo-location meta-data, the location of a tweet had to be inferred from the content of the tweet through entity recognition and natural language processing.…”

Predicting TV programme audience by using twitter based metrics

“…[56], places to be visited observing the most frequently attended places in a given location [8]. In addition, Twitter data has been used for assessing weather forecast information in [17], and in [18].…”