A new emerging trend concerns the implementation of services and distributed applications through the blockchain technology. A blockchain is an append-only database, which guarantees security, transparency and immutability of records. Blockchains can be used in the field of Cultural Heritage to protect minor artworks, i.e. artistic relevant works not as famous as masterpieces. Minor artworks are subjected to counterfeiting, thefts and natural disasters because they are not well protected as famous artworks. This paper describes a blockchain-based application, called MApp (Minor Artworks application), which lets authenticated users (private people or organizations), store the information about their artworks in a secure way. The use of blockchain produces three main advantages. Firstly, artworks cannot be deleted from the register thus preventing thieves to remove records associated stolen objects. Secondly, artworks can be added and updated only by authorized users, thus preventing counterfeiting in objects descriptions. Finally, records can be used to keep artworks memory in case of destruction caused by a natural disaster.

This study investigates the use of social networks as a scientific tool for gathering medical data from young subjects while promoting healthier habits. Our first hypothesis is that social networks can facilitate epidemiological studies, reducing time and cost. The second question is whether social networks can enable the collection of data from young and healthy subjects who are otherwise beyond the reach of conventional social health polls. A Facebook application was created to collect data concerning adherence to the Mediterranean diet, considering the significant risk of cardiovascular and neurological degenerative diseases in subjects with poor adherence to a healthy diet. More than 1400 users were recruited in a short time without any promotional action. Collected data indicate that adherence to the Mediterranean diet is in general greater in older users vs young (p <0.01) and Italian vs Other Countries (mainly participants from the US) (p <0.03), while no statistical differences were found concerning gender. Results show that the proposed approach offers advantages in terms of reduced cost, faster data gathering and processing, and improved efficiency compared to a form-based epidemiology campaign. However, the initial network may influence the sample constitution in age and geographical location, especially if the spread does not become viral and autonomous. Based on the case study, we provide designers of Facebook apps with some simple guideline suggestions aimed at maximizing the heterogeneity of the sample, in order to collect significant data. The proposed scenario, suitable for collecting health data, can easily be extended to other fields.

Online Social Media (OSM) platforms, such as Facebook or Twitter, are part of everyday life as powerful communication tools. They let users communicate anywhereanytime, and improve their own public image. For this reason, OSM are becoming more and more popular. Social Media data may play a crucial role in various decision-making processes. In this setting, research topics connected to monitoring of Social Media data are becoming increasingly important. The presented work is grounded on direct extensive experiences in data collection from different Social Media sources, and on the different methodologies applied in different reference domains (namely Online Reputation, Social Media Intelligence, and Opinion Mining in tourism). The crawlers developed for these domains provide valuable suggestions to elicit diverse requirements. After lessons learned in such fields, a general architecture for data capture from Social Media sources has been devised, and the interfaces of the composing modules have been defined. The resulting API can be exploited for an orderly re-engineering of crawling tools in the reference domains, thus implementing specific versions of the generic architecture.

Tax evasion is a widespread phenomenon confirmed by numerous European and American reports. To contrast it, governments already adopt software solutions that support tax inspectors in their investigations. However, the currently existing systems do not normally take advantage of the constant stream of data published on the Web. Instead, the ASIA project aims to prove the effectiveness of combining this kind of open source information with official data contained in Public Administration archives to fight tax evasion. Our prototype platform deals with two cases of investigation, people and businesses. Public officers have been involved throughout the project, and took part in a preliminary test phase which showed very promising results.

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 user-friendly network graphs.

Social sensing is based on the idea that communities or groups of people can provide a set of information similar to those obtainable from a sensor network. Emergency management is a candidate field of application for social sensing. In this work we describe the design, implementation and deployment of a decision support system for the detection and the damage assessment of earthquakes in Italy. Our system exploits the messages shared in real-time on Twitter, one of the most popular social networks in the world. Data mining and natural language processing techniques are employed to select meaningful and comprehensive sets of tweets. We then apply a burst detection algorithm in order to promptly identify outbreaking seismic events. Detected events are automatically broadcasted by our system via a dedicated Twitter account and by email notifications. In addition, we mine the content of the messages associated to an event to discover knowledge on its consequences. Finally we compare our results with official data provided by the National Institute of Geophysics and Volcanology (INGV), the authority responsible for monitoring seismic events in Italy. The INGV network detects shaking levels produced by the earthquake, but can only model the damage scenario by using empirical relationships. This scenario can be greatly improved with direct information site by site. Results show that the system has a great ability to detect events of a magnitude in the region of 3.5, with relatively low occurrences of false positives. Earthquake detection mostly occurs within seconds of the event and far earlier than the notifications shared by INGV or by other official channels. Thus, we are able to alert interested parties promptly. Information discovered by our system can be extremely useful to all the government agencies interested in mitigating the impact of earthquakes, as well as the news agencies looking for fresh information to publish.

The creation, customization, and maintenance of knowledge resources are essential for fostering the full deployment of Language Technologies. The definition and refinement of knowledge resources are time- and resource-consuming activities. In this paper we explore how the Wiki paradigm for online collaborative content editing can be exploited to gather massive social contributions from common Web users in editing knowledge resources. We discuss the Wikyoto Knowledge Editor, also called Wikyoto. Wikyoto is a collaborative Web environment that enables users with no knowledge engineering background to edit the multilingual network of knowledge resources exploited by KYOTO, a cross-lingual text mining system developed in the context of the KYOTO European Project.

This demo presents LeXFlow, a work-flow management system for cross-fertilization of computational lexicons. Borrowing from techniques used in the domain of document workflows, we model the activity of lexicon management as a set of workflow types, where lexical entries move across agents in the process of being dynamically updated. A prototype of LeXFlow has been implemented with extensive use of XML technologies (XSLT, XPath, XForms, SVG) and open-source tools (Cocoon, Tomcat, MySQL). LeXFlow is a web-based application that enables the cooperative and distributed management of computational lexicons.