FakeNewsLab: Experimental Study on Biases and Pitfalls Preventing us from Distinguishing True from False News

Misinformation posting and spreading in Social Media is ignited by personal decisions on the truthfulness of news that may cause wide and deep cascades at a large scale in a fraction of minutes. When individuals are exposed to information, they usually take a few seconds to decide if the content (or the source) is reliable, and eventually to share it. Although the opportunity to verify the rumour is often just one click away, many users fail to make a correct evaluation. We studied this phenomenon with a web-based questionnaire that was compiled by 7,298 different volunteers, where the participants were asked to mark 20 news as true or false. Interestingly, false news is correctly identified more frequently than true news, but showing the full article instead of just the title, surprisingly, does not increase general accuracy. Also, displaying the original source of the news may contribute to mislead the user in some cases, while a genuine wisdom of the crowd can positively assist individuals' ability to classify correctly. Finally, participants whose browsing activity suggests a parallel fact-checking activity, show better performance and declare themselves as young adults. This work highlights a series of pitfalls that can influence human annotators when building false news datasets, which in turn fuel the research on the automated fake news detection; furthermore, these findings challenge the common rationale of AI that suggest users to read the full article before re-sharing.Comment: 18 pages, 12 figures, 3 table

Analyzing and Visualizing American Congress Polarization and Balance with Signed Networks

Signed networks and balance theory provide a natural setting for real-world scenarios that show polarization dynamics, positive/negative relationships, and political partisanships. For example, they have been proven effective for studying the increasing polarization of the votes in the two chambers of the American Congress from World War II on. To provide further insights into this particular case study, we propose the application of a framework to analyze and visualize a signed graph's configuration based on the exploitation of the corresponding Laplacian matrix' spectral properties. The overall methodology is comparable with others based on the frustration index, but it has at least two main advantages: first, it requires a much lower computational cost; second, it allows for a quantitative and visual assessment of how arbitrarily small subgraphs (even single nodes) contribute to the overall balance (or unbalance) of the network. The proposed pipeline allows to explore the polarization dynamics shown by the American Congress from 1945 to 2020 at different resolution scales. In fact, we are able to spot and to point out the influence of some (groups of) congressmen in the overall balance, as well as to observe and explore polarization's evolution of both chambers across the years

The Impact of Disinformation on a Controversial Debate on Social Media

In this work we study how pervasive is the presence of disinformation in the Italian debate around immigration on Twitter and the role of automated accounts in the diffusion of such content. By characterising the Twitter users with an \textit{Untrustworthiness} score, that tells us how frequently they engage with disinformation content, we are able to see that such bad information consumption habits are not equally distributed across the users; adopting a network analysis approach, we can identify communities characterised by a very high presence of users that frequently share content from unreliable news sources. Within this context, social bots tend to inject in the network more malicious content, that often remains confined in a limited number of clusters; instead, they target reliable content in order to diversify their reach. The evidence we gather suggests that, at least in this particular case study, there is a strong interplay between social bots and users engaging with unreliable content, influencing the diffusion of the latter across the network

Structural inequalities emerging from a large wire transfers network

We aim to explore the connections between structural network inequalities and bank’s customer spending behaviours, within an entire national ecosystem made of natural persons (i.e., an individual human being) and legal entities (i.e., private or public organisations), different business sectors, and supply chains that span distinct geographical regions. We focus on Italy, that is among the wealthiest nations in the world, and also an example of a complex economic system. In particular, we had access to a large subset of anonymised and GDPR-compliant wire transfer data recorded from Jan 2016 to Dec 2017 by Intesa Sanpaolo, a leading banking group in the Eurozone, and the most important one in Italy.Intesa Sanpaolo wire transfers network exhibits a strong heavy-tailed behaviour and a giant component that grows continuously around the same core of the 1% highest degree nodes, and it also shows a general disassortative pattern, even if some ranges of degrees’ values stand out from the trend. Structural heterogeneity is explored further by means of a bow-tie analysis, that shows clearly that the majority of relevant, in terms of transferred amount, transactions is settled between a smaller set of nodes that are associated to legal entities and that mostly belong to the strongly connected component. This observation brings to a more comprehensive inspection of differences between Italian regions and business sectors, that could support the detection and the understanding of the interplay between supply chains.Our results suggest that there is a general flow of money that seems to stream down from higher degree legal entities to lower degree natural persons, crossing Italian regions and connecting different business sectors, and that is finally redistributed through expenses sharing within families and smaller communities. We also describe a reference dataset and an empirical contribution to the study on financial networks, focusing on finer-grained information concerned about spending behaviour through wire transfers

Bridging Representation and Visualization in Prosopographic Research: A Case Study

In the last decade, the research on ancient civilizations has started to rely more and more on data science to extract knowledge on ancient societies from the written sources delivered from the past. In this paper, we combine two well-established frameworks: Linked Data to obtain a rich data structure, and Network Science to explore different research questions regarding the structure and the evolution of ancient societies. We propose a multi-disciplinary pipeline where, starting from a semantically annotated prosopographic archive, a research question is translated into a query on the archive and the obtained dataset is the input to the network model. We applied this pipeline to different archives, a Hittite and a Kassite collection of cuneiform tablets. Finally, network visualization is presented as a powerful tool to highlight both the data structure and the social network analysis results

One week of levofloxacin plus dexamethasone eye drops for cataract surgery: an innovative and rational therapeutic strategy

Background: Cataract surgery is the most common operation performed worldwide. A fixed topical corticosteroid-antibiotic combination is usually prescribed in clinical practice for 2 or more weeks to treat post surgical inflammation and prevent infection. However, this protracted schedule may increase the incidence of corticosteroid-related adverse events and notably promote antibiotic resistance. Methods: This International, multicentre, randomized, blinded-assessor, parallel-group clinical study evaluated the non-inferiority of 1-week levofloxacin/dexamethasone eye drops, followed by 1-week dexamethasone alone, vs. 2-week gold-standard tobramycin/dexamethasone (one drop QID for all schedules) to prevent and treat ocular inflammation and prevent infection after uncomplicated cataract surgery. Non-inferiority was defined as the lower limit of the 95% confidence interval (CI) around a treatment difference >\u201310%. The study randomized 808 patients enrolled in 53 centres (Italy, Germany, Spain and Russia). The primary endpoint was the proportion of patients without anterior chamber inflammation on day 15 defined as the end of treatment. Endophthalmitis was the key secondary endpoint. This study is registered with EudraCT code: 2018-000286-36. Results: After the end of treatment, 95.2% of the patients in the test arm vs. 94.9% of the control arm had no signs of inflammation in the anterior chamber (difference between proportions of patients = 0.028; 95% CI: 120.0275/0.0331). No case of endophthalmitis was reported. No statistically significant difference was evident in any of the other secondary endpoints. Both treatments were well tolerated. Conclusions: Non-inferiority of the new short pharmacological strategy was proven. One week of levofloxacin/dexamethasone prevents infection, ensures complete control of inflammation in almost all patients and may contain antibiotic resistance

Measuring user engagement with low credibility media sources in a controversial online debate

We quantify social media user engagement with low-credibility online news media sources using a simple and intuitive methodology, that we showcase with an empirical case study of the Twitter debate on immigration in Italy. By assigning the Twitter users an Untrustworthiness (U) score based on how frequently they engage with unreliable media outlets and cross-checking it with a qualitative political annotation of the communities, we show that such information consumption is not equally distributed across the Twitter users. Indeed, we identify clusters characterised by a very high presence of accounts that frequently share content from less reliable news sources. The users with high U are more keen to interact with bot-like accounts that tend to inject more unreliable content into the network and to retweet that content. Thus, our methodology applied to this real-world network provides evidence, in an easy and straightforward way, that there is strong interplay between accounts that display higher bot-like activity and users more focused on news from unreliable sources and that this influences the diffusion of this information across the network