1,721,045 research outputs found
The Diffusion of Mainstream and Disinformation News on Twitter: The Case of Italy and France
Full text linkIn this work we provide preliminary results from an ongoing investigation on the Twitter diffusion of news pertaining to two classes of sources, namely websites which notably produce disinformation, i.e. misleading and harmful information, opposed to more traditional and mainstream websites which instead publish credible information. We used the Twitter Streaming API to collect a large-scale dataset of thousands of tweets containing links to news articles in two different countries, Italy and France. We show that mainstream news outlets generate a much larger engagement in both settings, with a larger discrepancy between the two news domains in France. We also show that only a handful of Italian outlets actively engage with Twitter users, whereas in France there is a larger number of outlets sharing misleading information which exhibit a non-negligible volume of shares. We observe a strong tendency towards sharing mainstream news in those users who also share non-credible information in both countries. Analyzing the diffusion networks of distinct news domains and countries, we observed that disinformation networks are more clustered and connected, but much smaller than the mainstream ones (with the largest discrepancy in the French scenario)
Different causes of closure of small business enterprises: Alternative models for competing risks survival analysis
Full text linkWe examine the time until closure of Small Business Enterprises in Umbria, Italy between 2008 and 2013, and the factors that influence it. Earlier analysis, using Cox regression, considered "failure" (closure) from any cause. However, there are different reasons for inactivity: voluntary winding-up (1808 of 15184 firms in our data, 59.3% of the 3049 failures); bankruptcy (236, 7.7%); and closure without action by creditors or courts (1005, 33.0%). While the earlier analysis provides a valuable overall picture, it is also interesting to examine the separate causes, their rates of occurrence and which factors influence them separately. We do this using competing risks analyses, employing both of the regression methods that are prominent in the literature, based on cause-specific and sub-distribution hazard functions (Fine-Gray model). Furthermore, a proportional odds model was used to estimate cumulative incidences of failure by cause. Data included the firm's year of foundation, location, legal form and sector of activity. Financial indexes were constructed from annual balance sheets. The date and reason for closure were recorded if the firm ceased activity. Findings included major differences between types of firm; for example, cooperatives had greatly increased haz-ards for winding-up (HR of 2.44 and 2.61 in the two approaches) but greatly reduced hazards for closure (0.48 and 0.45) compared to publicly traded com-panies. All-causes analysis averaged these strong effects into an insignificant one (1.05). Coefficients from the proportional odds model were similar to those from the Fine-Gray model, but have the advantage of interpretability
Decoupled Impedance and Passivity Control Methods
No full textIn this Chapter, the problem of control of Aerial RObotic MAnipulators (AROMAs) involved in operations requiring the contact with the environment is tackled. Two different approaches are presented: a Cartesian admittance scheme and a decoupled impedance in which selective behaviors for the aerial vehicle and the manipulator are enforced. Finally, some experimental results, collected within the ARCAS project and regarding the Cartesian admittance, are reported and discussed
False news on social media: A data-driven survey
Full text linkIn 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 deceptive information has been motivated by considerable political and social backlashes in 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 false news. 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
A multi-layer approach to disinformation detection in US and Italian news spreading on Twitter
Full text linkWe tackle the problem of classifying news articles pertaining to disinformation vs mainstream news by solely inspecting their diffusion mechanisms on Twitter. This approach is inherently simple compared to existing text-based approaches, as it allows to by-pass the multiple levels of complexity which are found in news content (e.g. grammar, syntax, style). As we employ a multi-layer representation of Twitter diffusion networks where each layer describes one single type of interaction (tweet, retweet, mention, etc.), we quantify the advantage of separating the layers with respect to an aggregated approach and assess the impact of each layer on the classification. Experimental results with two large-scale datasets, corresponding to diffusion cascades of news shared respectively in the United States and Italy, show that a simple Logistic Regression model is able to classify disinformation vs mainstream networks with high accuracy (AUROC up to 94%). We also highlight differences in the sharing patterns of the two news domains which appear to be common in the two countries. We believe that our network-based approach provides useful insights which pave the way to the future development of a system to detect misleading and harmful information spreading on social media
Topology comparison of Twitter diffusion networks effectively reveals misleading information
Full text linkIn 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
Information disorders during the COVID-19 infodemic: The case of Italian Facebook
Full text linkThe recent COVID-19 pandemic came alongside with an “infodemic”, with online social media flooded by often unreliable information associating the medical emergency with popular subjects of disinformation. In Italy, one of the first European countries suffering a rise in new cases and dealing with a total lockdown, controversial topics such as migrant flows and the 5G technology were often associated online with the origin and diffusion of the virus. In this work we analyze COVID-19 related conversations on the Italian Facebook, collecting over 1.5M posts shared by nearly 80k public pages and groups for a period of four months since January 2020. On the one hand, our findings suggest that well-known unreliable sources had a limited exposure, and that discussions over controversial topics did not spark a comparable engagement with respect to institutional and scientific communication. On the other hand, however, we realize that dis- and counter-information induced a polarization of (clusters of) groups and pages, wherein conversations were characterized by a topical lexicon, by a great diffusion of user generated content, and by link-sharing patterns that seem ascribable to coordinated propaganda. As revealed by the URL-sharing diffusion network showing a “small-world” effect, users were easily exposed to harmful propaganda as well as to verified information on the virus, exalting the role of public figures and mainstream media, as well as of Facebook groups, in shaping the public opinion
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Effetti del sesso sui potenziali evocati uditivi del tronco dell'encefalo nei primi due anni di vita
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