False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

Topology comparison of Twitter diffusion networks effectively reveals misleading information

In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that misleading information spreads faster, deeper and more broadly than factual information on social media, where echo chambers, algorithmic and human biases play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of diffusion networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of misleading articles, including false news intended to harm, satire intended to make people laugh, click-bait news that may be entirely factual or rumors that are unproven. We carried out an extensive comparison of these networks using several alignment-free approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these techniques allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream news, compared to those sharing misleading news, tend to shape diffusion networks with subtle yet systematic differences which might be effectively employed to identify misleading and harmful information.Comment: A revised new version is available on Scientific Report

How Brazil's Agrarian Dynamics Shape Development Cooperation in Africa

This article shows how Brazil's history of agrarian dynamics shapes development cooperation. In particular, Brazil's dualistic agrarian structure frames policy discourse, and shapes development cooperation thinking and practice. Given Brazil's recent experience of rural poverty reduction, the article argues that a focus on ‘family farming’ is potentially the most productive form of engagement in development cooperation. This is illustrated through an analysis of Brazilian cooperation promoted by the Ministry of Agrarian Development (MDA), and in particular its More Food International Programme. While Brazilian family farms are very different to those found in Africa, there can be a productive exchange of experience, expertise and equipment. Key lessons from the Brazilian experience are the need for state backing and support, providing social security for the poor, offering financial support and technical expertise for family farming and the existence of effective social mobilisation by civil society

Responsiveness of open innovation to COVID-19 pandemic: The case of data for good

Due to the COVID-19 pandemic, countries around the world are facing one of the most severe health and economic crises of recent history and human society is called to figure out effective responses. However, as current measures have not produced valuable solutions, a multidisciplinary and open approach, enabling collaborations across private and public organizations, is crucial to unleash successful contributions against the disease. Indeed, the COVID-19 represents a Grand Challenge to which joint forces and extension of disciplinary boundaries have been recognized as main imperatives. As a consequence, Open Innovation represents a promising solution to provide a fast recovery. In this paper we present a practical application of this approach, showing how knowledge sharing constitutes one of the main drivers to tackle pressing social needs. To demonstrate this, we propose a case study regarding a data sharing initiative promoted by Facebook, the Data For Good program. We leverage a large-scale dataset provided by Facebook to the research community to offer a representation of the evolution of the Italian mobility during the lockdown. We show that this repository allows to capture different patterns of movements on the territory with increasing levels of detail. We integrate this information with Open Data provided by the Lombardy region to illustrate how data sharing can also provide insights for private businesses and local authorities. Finally, we show how to interpret Data For Good initiatives in light of the Open Innovation Framework and discuss the barriers to adoption faced by public administrations regarding these practices

Distributed Fault-Tolerant Control for Networked Robots in the Presence of Recoverable/Unrecoverable Faults and Reactive Behaviors

The paper presents an architecture for distributed control of multi-robot systems with an integrated fault detection, isolation, and recovery strategy. The proposed solution is based on a distributed observer-controller schema where each robot, by communicating only with its direct neighbors, is able to estimate the overall state of the system; such an estimate is then used by the controllers of each robot to achieve global missions as, for example, centroid and formation tracking. The information exchanged among the observers is also used to compute residual vectors that allow each robot to detect failures on anyone of the teammates, even if not in direct communication. The proposed strategy considers both recoverable and unrecoverable actuator faults as well as it properly manages the possible activation of reactive local control behaviors of the robots (e.g., the activation of obstacle avoidance strategy), which generate control inputs different from those required by the global mission control. In particular, when the robots are subject to recoverable faults, those are managed at a local level by computing a proper compensating control action. On the other side, when the robots are subject to unrecoverable faults, the faults are isolated from anyone of the teammates by means of a distributed fault detection and isolation strategy; then, the faulty robots are removed from the team and the mission is rearranged. The proposed strategy is validated via numerical simulations where the system properly identifies and manages the different cases of recoverable and unrecoverable actuator faults, as well as it manages the activation of local reactive control in an integrated case study

A novel phase retrieval technique based on propagation diversity via a dielectric slab.

This paper deals with a novel technique to determine the far field of an aperture starting from the knowledge of two near-field intensity data sets collected over the same measurement plane. The diversity between the two intensity data sets is achieved by ensuring different conditions of the near field propagation between the aperture and the measurement plane. In particular, one measurement is performed under free-space propagation condition while the second one is performed by exploiting a dielectric slab, with known properties, filling partly the space between the aperture and the measurement plane. A phase retrieval technique, that faces a non linear inverse problem, is solved by assuming as unknown the plane wave spectrum of the aperture field. The feasibility of the novel approach is presented also in comparison with the usual near field phase retrieval technique exploiting measurements of the near field intensity over two scanning planes. © 2007 Optical Society of America

Numerical Investigation about Frequency Behaviour of Conformal FSS

Frequency selective surfaces (FSSs) are spatial filters widely employed in high-performance applications like hybrid radomes for radars and antennas. While planar geometries are widely studied, less attention has been devoted to conformal ones, where we must consider the influence of both the lattice geometry and the shape and size of the individual elements. In the planar case, periodicity first impacts on the general reflecting properties of the surface, while the shape and the size of the individual element affect its detailed both spatial and frequency filtering behaviour. In particular, the frequency response is dictated mainly by the scattering by the individual element and attains its maximum at resonance conditions. We mean to numerically investigate whether the same also occurs for non-planar surfaces and curved elements, for both cylindrical and conical surfaces. We compare the results of the general frequency behaviour of FSS both made of strips in free space and slots cut in a perfectly conducting material. The effect of the lattice geometrical parameters is also appreciated. The main conclusions are that also for curved elements a frequency selective behaviour can be appreciated and the interaction with the single elements plays an important role, when mutual coupling is not strong