Information propagation in social networks during crises: A structural framework

In crisis situations like riots, earthquakes, storms, etc. information plays a central role in the process of organizing interventions and decision making. Due to their increasing use during crises, social media (SM) represents a valuable source of information that could help obtain a full picture of people needs and concerns. In this chapter, we highlight the importance of SM networks in crisis management (CM) to show how information is propagated through. The chapter also summarizes the current state of research related to information propagation in SMnetworks during crises. In particular three classes of information propagation research categories are identified: network analysis and community detection, role and topic-oriented information propagation, and infrastructure-oriented information propagation. The chapter describes an analysis framework that deals with structural information propagation for crisismanagement purposes. Structural propagation is about broadcasting specific information obtained from social media networks to targeted sinks/receivers/hubs like emergency agencies, police department, fire department, etc. Specifically, the framework aims to identify the discussion topics, known as sub-events, related to a crisis (event) from SM contents. A brief description of techniques used to detect topics and the way those topics can be used in structural information propagation are presented

Public crowdsensing of heat waves by social media data

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

Sentiment analysis during Hurricane Sandy in emergency response

Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of users about different aspects of products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying such sentiments from online social networking sites can help emergency responders understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. In this paper, we perform a sentiment analysis of tweets posted on Twitter during the disastrous Hurricane Sandy and visualize online users' sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to their locations, but also based on the distance from the disaster. In addition, we study how the divergence of sentiments in a tweet posted during the hurricane affects the tweet retweetability. We find that extracting sentiments during a disaster may help emergency responders develop stronger situational awareness of the disaster zone itself