arXiv subjects

Salvatore Vilella

Publications and source records attributed to Salvatore Vilella.

8 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↗

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

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

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↗