arXiv subjects

Giancarlo Ruffo

Publications and source records attributed to Giancarlo Ruffo.

At least 19 recordsLinked to original sources

arXiv2026

pyBiblioNet: a Python library for a comprehensive network-based bibliometric analysis

Bibliometric analysis is a critical tool for understanding the structure, dynamics, and impact of scientific research. Traditional methods often fall short in capturing the intricate relationships and evolving trends within scientific literature. To address this gap, we present pyBiblioNet, a Python library designed to facilitate comprehensive network-based bibliometric analysis, providing insights into citation networks, co-authorship networks, and keyword co-occurrence networks. The library integrates with OpenAlex, a popular and open catalogue to the global research system, enabling users to easily preprocess, visualize, and analyse bibliometric data. Key features include topic selection, automatic data download via OpenAlex APIs, creation of the root and base sets of manuscripts to analyze, creation of the citation and co-authorship networks, network visualization tools, and a suite of algorithms for computing network centralities, clustering, and community detection, all of them tailored to the bibliometric domain. Additionally, it enables the analysis of key topics and concepts using NLP techniques. We showcase the main functions of the library by performing a bibliometric analysis on the multidisciplinary "15-minute city paradigm", demonstrating the utility of pyBiblioNet in uncovering hidden patterns and emerging trends in various scientific domains. pyBiblioNet can empower researchers, librarians, and policymakers with a powerful, user-friendly tool for enhancing their bibliometric analyses and making data-driven decisions.

cs.DL↗

arXiv2025

FlowSeries: Anomaly Detection in Financial Transaction Flows

In recent years, the digitization and automation of anti-financial crime (AFC) investigative processes have faced significant challenges, particularly the need for interpretability of AI model results and the lack of labeled data for training. Network analysis has emerged as a valuable approach in this context. In this paper, we present WeirdFlows, a top-down search pipeline for detecting potentially fraudulent transactions and non-compliant agents. In a transaction network, fraud attempts are often based on complex transaction patterns that change over time to avoid detection. The WeirdFlows pipeline requires neither an a priori set of patterns nor a training set. In addition, by providing elements to explain the anomalies found, it facilitates and supports the work of an AFC analyst. We evaluate WeirdFlows on a dataset from Intesa Sanpaolo (ISP) bank, comprising 80 million cross-country transactions over 15 months, benchmarking our implementation of the algorithm. The results, corroborated by ISP AFC experts, highlight its effectiveness in identifying suspicious transactions and actors, particularly in the context of the economic sanctions imposed in the EU after February 2022. This demonstrates \textit{WeirdFlows}' capability to handle large datasets, detect complex transaction patterns, and provide the necessary interpretability for formal AFC investigations.

cs.CY↗

arXiv2025

(De)-Indexing and the Right to be Forgotten

In the digital age, the challenge of forgetfulness has emerged as a significant concern, particularly regarding the management of personal data and its accessibility online. The right to be forgotten (RTBF) allows individuals to request the removal of outdated or harmful information from public access, yet implementing this right poses substantial technical difficulties for search engines. This paper aims to introduce non-experts to the foundational concepts of information retrieval (IR) and de-indexing, which are critical for understanding how search engines can effectively "forget" certain content. We will explore various IR models, including boolean, probabilistic, vector space, and embedding-based approaches, as well as the role of Large Language Models (LLMs) in enhancing data processing capabilities. By providing this overview, we seek to highlight the complexities involved in balancing individual privacy rights with the operational challenges faced by search engines in managing information visibility.

cs.CY↗

arXiv2024

Anomaly detection in cross-country money transfer temporal networks

This paper explores anomaly detection through temporal network analysis. Unlike many conventional methods, relying on rule-based algorithms or general machine learning approaches, our methodology leverages the evolving structure and relationships within temporal networks, that can be used to model financial transactions. Focusing on minimal changes in stable ecosystems, such as those found in large international financial institutions, our approach utilizes network centrality measures to gain insights into individual nodes. By monitoring the temporal evolution of centrality-based node rankings, our method effectively identifies abrupt shifts in the roles of specific nodes, prompting further investigation by domain experts. To demonstrate its efficacy, our methodology is applied in the Anti-Financial Crime (AFC) domain, analyzing a substantial financial dataset comprising over 80 million cross-country wire transfers. The goal is to pinpoint outliers potentially involved in malicious activities, aligning with financial regulations. This approach serves as an initial stride towards automating AFC and Anti-Money Laundering (AML) processes, providing AFC officers with a comprehensive top-down view to enhance their efforts. It overcomes many limitations of current prevalent paradigms, offering a holistic interpretation of the financial data landscape and addressing potential blindness to phenomena that cannot be effectively estimated through single-node or narrowly focused transactional approaches.

cs.SI↗

arXiv2023

Analyzing and visualizing polarization and balance with signed networks: the U.S. Congress case study

Signed networks and balance theory provide a natural setting for real-world scenarios that show polarization dynamics, positive/negative relationships, and political partisanship. For example, they have been proven effective in studying the increasing polarization of the votes in the two chambers of the U.S. Congress from World War II on. To provide further insights into this particular case study, we propose the application of a pipeline 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 the exploration of polarization dynamics shown by the U.S. Congress from 1945 to 2020 at different resolution scales. In fact, we are able to spot and 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.

cs.SI↗

arXiv2023

Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language

With the explosive growth of online social media, the ancient problem of information disorders interfering with news diffusion has surfaced with a renewed intensity threatening our democracies, public health, and news outlets' credibility. Therefore, thousands of scientific papers have been published in a relatively short period, making researchers of different disciplines struggle with an information overload problem. The aim of this survey is threefold: (1) we present the results of a network-based analysis of the existing multidisciplinary literature to support the search for relevant trends and central publications; (2) we describe the main results and necessary background to attack the problem under a computational perspective; (3) we review selected contributions using network science as a unifying framework and computational linguistics as the tool to make sense of the shared content. Despite scholars working on computational linguistics and networks traditionally belong to different scientific communities, we expect that those interested in the area of fake news should be aware of crucial aspects of both disciplines.

cs.CY↗

arXiv2022

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.

cs.CY↗

arXiv2022

Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets

We propose a simple model of an idealized online cultural market in which $N$ items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items' popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings. We do so under the assumption that users, having limited time/attention, are able to discriminate the best-quality only within a random subset of the items. We discover the existence of a harmful regime in which improper use of popularity can seriously compromise the emergence of quality, and a benign regime in which wise use of popularity, coupled with a small discrimination effort on behalf of users, guarantees the perfect alignment of quality and popularity ranking. Our findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

cs.SI↗

arXiv2022

Ensemble of Opinion Dynamics Models to Understand the Role of the Undecided in the Vaccination Debate

We present three models used to describe the recruitment of the undecided population by pro-vax and no-vax factions. Starting from real-world data of Facebook pages, we compare three opinion dynamics models that catch different behaviours of the undecided population. The first one is a variation of the SIS model, where undecided position is considered indifferent. Neutrals can be "infected" by one of the two extreme factions, joining their side, and they "recover" when they lose interest in the debate and go back to neutrality. The second model is a three parties Voters model: neutral pages represent a centrist position. They lean their original ideas, that are different from both the other parties. The last is the Bilingual model adapted to the vaccination debate: neutral individuals are in agreement with both pro-, ad anti-vax factions, with a position of compromise between the extremes ("bilingualism''). If they have a one-sided neighbourhood, the convenience to agree with both parties comes out, and bi-linguists can become mono-linguists. Our results depicts an agreement between the three models: anti-vax opinion propagates more than pro-vax, thanks to an initial strategic position in the online social network (even if they start with a smaller population). While most of the pro-vaccines nodes are segregated in their own communities, no-vaccines ones are entangled at the core of the network, where the majority of undecided population is located. In the last section, we propose and compare some policies that could be applied on the network to prevent anti-vax overcome: they lead us to conclude that censoring strategies are not effective, as well as segregating scenarios based on unfollowing decisions, while the addition of links in the network favours the containment of the pro-vax domain, reducing the distance between pro-vaxxers and undecided population.

physics.soc-ph↗

arXiv2022

Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns

Since their announcement in November 2020, COVID-19 vaccines were largely debated by the press and social media. With most studies focusing on COVID-19 disinformation in social media, little attention has been paid to how mainstream news outlets framed COVID-19 narratives compared to alternative sources. To fill this gap, we use cognitive network science and natural language processing to reconstruct time-evolving semantic and emotional frames of 5745 Italian news, that were massively re-shared on Facebook and Twitter, about COVID-19 vaccines. We found consistently high levels of trust/anticipation and less disgust in the way mainstream sources framed the general idea of "vaccine/vaccino". These emotions were crucially missing in the ways alternative sources framed COVID-19 vaccines. More differences were found within specific instances of vaccines. Alternative news included titles framing the AstraZeneca vaccine with strong levels of sadness, absent in mainstream titles. Mainstream news initially framed "Pfizer" along more negative associations with side effects than "AstraZeneca". With the temporary suspension of the latter, on March 15th 2021, we identified a semantic/emotional shift: Even mainstream article titles framed "AstraZeneca" as semantically richer in negative associations with side effects, while "Pfizer" underwent a positive shift in valence, mostly related to its higher efficacy. "Thrombosis" entered the frame of vaccines together with fearful conceptual associations, while "death" underwent an emotional shift, steering towards fear in alternative titles and losing its hopeful connotation in mainstream titles. Our findings expose crucial aspects of the emotional narratives around COVID-19 vaccines adopted by the press, highlighting the need to understand how alternative and mainstream media report vaccination news.

cs.CY↗

arXiv2021

Measuring Scientific Brain Drain with Hubs and Authorities: a Dual Perspective

In this work we study international migrations of researchers, scientists, and academics from a complex-network perspective to identify the central countries involved in the migration phenomenon. We define the scientific migration network (SMN) as temporal directed weighted network where nodes are world countries, links account for the number of scientists moving from one country to another, and timestamps represent years (from 2000 to 2016). 2.8 millions ORCID public profiles are utilized as data source. We then characterize hubs and authorities of the SMN employing the well-know weighted hyperlink-induced topic search (HITS) algorithm to catch the interplay between providing and attracting researchers from a global perspective, and relate these results to other local and global methodologies. We also investigate the local characteristics of successors of hubs and predecessors of authorities to dive deeper into the motivations that establish hubs and authorities as such. Our findings highlight the presence of a set of countries acting both as hubs and authorities, occupying a privileged position in the scientific migration network, and having similar local characteristics. Finally, we showcase how to employ network visualization to evince temporal evolutions of ego-networks of selected hubs and authorities.

cs.SI↗

arXiv2021

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.

cs.SI↗

arXiv2021

PyPlutchik: visualising and comparing emotion-annotated corpora

The increasing availability of textual corpora and data fetched from social networks is fuelling a huge production of works based on the model proposed by psychologist Robert Plutchik, often referred simply as the ``Plutchik Wheel''. Related researches range from annotation tasks description to emotions detection tools. Visualisation of such emotions is traditionally carried out using the most popular layouts, as bar plots or tables, which are however sub-optimal. The classic representation of the Plutchik's wheel follows the principles of proximity and opposition between pairs of emotions: spatial proximity in this model is also a semantic proximity, as adjacent emotions elicit a complex emotion (a primary dyad) when triggered together; spatial opposition is a semantic opposition as well, as positive emotions are opposite to negative emotions. The most common layouts fail to preserve both features, not to mention the need of visually allowing comparisons between different corpora in a blink of an eye, that is hard with basic design solutions. We introduce PyPlutchik, a Python library specifically designed for the visualisation of Plutchik's emotions in texts or in corpora. PyPlutchik draws the Plutchik's flower with each emotion petal sized after how much that emotion is detected or annotated in the corpus, also representing three degrees of intensity for each of them. Notably, PyPlutchik allows users to display also primary, secondary, tertiary and opposite dyads in a compact, intuitive way. We substantiate our claim that PyPlutchik outperforms other classic visualisations when displaying Plutchik emotions and we showcase a few examples that display our library's most compelling features.

cs.HC↗

arXiv2020

#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection

Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isolation, while social studies highlight that social communities may contribute to influence users' opinion. Furthermore, stance should be studied in a diachronic perspective, since it could help to shed light on users' opinion shift dynamics that can be recorded during the debate. We analyzed the political discussion in UK about the BREXIT referendum on Twitter, proposing a novel approach and annotation schema for stance detection, with the main aim of investigating the role of features related to social network community and diachronic stance evolution. Classification experiments show that such features provide very useful clues for detecting stance.

cs.CL↗

arXiv2020

News and the city: understanding online press consumption patterns through mobile data

The always increasing mobile connectivity affects every aspect of our daily lives, including how and when we keep ourselves informed and consult news media. By studying a DPI (deep packet inspection) dataset, provided by one of the major Chilean telecommunication companies, we investigate how different cohorts of the population of Santiago De Chile consume news media content through their smartphones. We find that some socio-demographic attributes are highly associated to specific news media consumption patterns. In particular, education and age play a significant role in shaping the consumers behaviour even in the digital context, in agreement with a large body of literature on off-line media distribution channels.

cs.CY↗

arXiv2019

Visualizing structural balance in signed networks

Network visualization has established as a key complement to network analysis since the large variety of existing network layouts are able to graphically highlight different properties of networks. However, signed networks, i.e., networks whose edges are labeled as friendly (positive) or antagonistic (negative), are target of few of such layouts and none, to our knowledge, is able to show structural balance, i.e., the tendency of cycles towards including an even number of negative edges, which is a well-known theory for studying friction and polarization. In this work we present Structural-balance-viz: a novel visualization method showing whether a connected signed network is balanced or not and, in the latter case, how close the network is to be balanced. Structural-balance-viz exploits spectral computations of the signed Laplacian matrix to place network's nodes in a Cartesian coordinate system resembling a balance (a scale). Moreover, it uses edge coloring and bundling to distinguish positive and negative interactions. The proposed visualization method has characteristics desirable in a variety of network analysis tasks: Structural-balance-viz is able to provide indications of balance/polarization of the whole network and of each node, to identify two factions of nodes on the basis of their polarization, and to show their cumulative characteristics. Moreover, the layout is reproducible and easy to compare. Structural-balance-viz is validated over synthetic-generated networks and applied to a real-world dataset about political debates confirming that it is able to provide meaningful interpretations.

cs.SI↗

arXiv2019

Discovering Polarized Communities in Signed Networks

Signed networks contain edge annotations to indicate whether each interaction is friendly (positive edge) or antagonistic (negative edge). The model is simple but powerful and it can capture novel and interesting structural properties of real-world phenomena. The analysis of signed networks has many applications from modeling discussions in social media, to mining user reviews, and to recommending products in e-commerce sites. In this paper we consider the problem of discovering polarized communities in signed networks. In particular, we search for two communities (subsets of the network vertices) where within communities there are mostly positive edges while across communities there are mostly negative edges. We formulate this novel problem as a "discrete eigenvector" problem, which we show to be NP-hard. We then develop two intuitive spectral algorithms: one deterministic, and one randomized with quality guarantee $\sqrt{n}$ (where $n$ is the number of vertices in the graph), tight up to constant factors. We validate our algorithms against non-trivial baselines on real-world signed networks. Our experiments confirm that our algorithms produce higher quality solutions, are much faster and can scale to much larger networks than the baselines, and are able to detect ground-truth polarized communities.

cs.DS↗

arXiv2019

Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources

Real estate appraisal is a complex and important task, that can be made more precise and faster with the help of automated valuation tools. Usually the value of some property is determined by taking into account both structural and geographical characteristics. However, while geographical information is easily found, obtaining significant structural information requires the intervention of a real estate expert, a professional appraiser. In this paper we propose a Web data acquisition methodology, and a Machine Learning model, that can be used to automatically evaluate real estate properties. This method uses data from previous appraisal documents, from the advertised prices of similar properties found via Web crawling, and from open data describing the characteristics of a corresponding geographical area. We describe a case study, applicable to the whole Italian territory, and initially trained on a data set of individual homes located in the city of Turin, and analyze prediction and practical applicability.

cs.CY↗