No abstract

Messages posted to social media in the aftermath of a natural disaster are not only useful for detecting the event itself. More importantly, mining such deliberately dropped digital traces allows a precise situational awareness, from which disaster's consequences on population and infrastructures can be timely estimated. Yet, to date, the automatic assessment of damage has received little attention. Here, the authors explore feeding predictive models by tweets conveying on-the-ground social sensors observations to nowcast the perceived intensity of earthquakes.

Organised crime uses information technology systems to communicate, work or expand its influence. The EU FP7 Security Research Project CAPER (Collaborative information, Acquisition, Processing, Exploitation and Reporting for the prevention of organised crime), created in cooperation with European Law Enforcement Agencies (LEAs), aims to build a common collaborative and information sharing platform for the detection and prevention of organised crime, which exploits Open Source Intelligence (OSINT). LEAs are becoming more inclined to using OSINT tools, and particularly tools able to manage Online Social Networks (OSNs) data. This paper presents the CAPER Facebook crawling and analysis subsystem. Heuristic algorithms have been implemented in order to extract specific properties of Facebook's social graph, in particular user interactions. To support analysis tasks specifically, extensive effort has been spent on the analysis of textual user generated content and on the recognition of named-entities, in particular person names, locations and organisations. Relationships between users and entities mentioned in posts and in related comments are created and merged into the users networks extracted from the social graph. All entity relationships are finally visualised in userfriendly network graphs.

Crowdsensing systems can be either participatory or opportunistic, depending on whether the user intentionally contributes data, or she simply acts as the bearer of a sensing device from which data is transparently collected. In this paper, we propose hybrid crowdsensing, a social mediabased paradigm which aims at combining the strengths of both participatory and opportunistic crowdsensing. With hybrid crowdsensing, possibly relevant data is collected via an opportunistic approach. Then, users that spontaneously contributed are directly contacted and asked to provide additional information following a participatory approach. To demonstrate its feasibility and usefulness, we experimented the proposed paradigm for involving Twitter users in an emergency relief scenario. For each of the two real-world experiments we analyze the answer ratio to our questions, their time distribution, and responders' willingness to collaborate. Results support the adoption of hybrid crowdsensing, especially in those practical scenarios where users are emotionally involved.

No abstract