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Investigating the COVID-19 vaccine discussions on Twitter through a multilayer network-based approach

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Investigating discussions on social networks, particularly on sensitive topics such as politics or healthcare, can be challenging. However, understanding the opinions and trends within these debates is crucial for identifying user cohesion on specific topics. In this work, we proposed a multilayer network approach for modeling discussions on a social network. We applied our model to a Twitter dataset containing tweets about COVID19 vaccines. We compared our approach to traditional single network analysis, and found that it is more effective at identifying influential users with larger networks and more interactions with their neighbors.

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Mobile Programming

Bachelor course in Computer Science and Automation Engineering, Polytechnic University of Marche, 2019

Teaching tutor activity for the Mobile Programming course (24 hours).

Big Data Analytics Laboratory

Master course in Computer Science and Automation Engineering, Polytechnic University of Marche, 2019

Teaching tutor activity for the Big Data Analytics Laboratory course (20 hours).

Data Science

Master course in Computer Science and Automation Engineering, Polytechnic University of Marche, 2020

Teaching tutor activity for the Data Science course (20 hours).

Data Science

Master course in Computer Science and Automation Engineering, Polytechnic University of Marche, 2021

Teaching tutor activity for the Data Science course (20 hours).

Big Data Analytics Lab

Master course in Data Science for Economics and Business, Polytechnic University of Marche, 2022

Course lecturer of the Big Data Analytics Lab course for the Master’s Degree Program in Data Science for Economics and Business in the Department of Economic and Social Sciences (44 hours, 6 CFU). Syllabus

Python Lab for Applications

Master course in Data Science for Economics and Business, Polytechnic University of Marche, 2022

Course lecturer of the Python Lab for Applications course for the Master’s Degree Program in Data Science for Economics and Business in the Department of Economic and Social Sciences (22 hours, 3 CFU). Syllabus

AI-Driven Cybersecurity

Ph.D. School in Electronics, Computer Science, and Electrical Engineering, University of Pavia, 2022

Course lecturer of the AI-Driven Cybersecurity for the PhD School in Electronics, Computer Science, and Electrical Engineering (12 hours).

Big Data Analytics Lab

Master course in Data Science for Economics and Business, Polytechnic University of Marche, 2023

Course lecturer of the Big Data Analytics Lab course for the Master’s Degree Program in Data Science for Economics and Business in the Department of Economic and Social Sciences (44 hours, 6 CFU). Syllabus

Python Lab for Applications

Master course in Data Science for Economics and Business, Polytechnic University of Marche, 2023

Course lecturer of the Python Lab for Applications course for the Master’s Degree Program in Data Science for Economics and Business in the Department of Economic and Social Sciences (22 hours, 3 CFU). Syllabus

Big Data Analytics Lab

Master course in Data Science for Economics and Business, Polytechnic University of Marche, 2024

Course lecturer of the Big Data Analytics Lab course for the Master’s Degree Program in Data Science for Economics and Business in the Department of Economic and Social Sciences (44 hours, 6 CFU). Syllabus

Data Science Laboratory

Master course in Data Science for Economics and Business, Polytechnic University of Marche, 2024

Course lecturer of the Data Science Laboratory course for the Master’s Degree Program in Data Science for Economics and Business in the Department of Economic and Social Sciences (22 hours, 3 CFU). Syllabus

Object Orienting Programming

Bachelor course in Computer Science and Automation Engineering, Polytechnic University of Marche, 2024

Course lecturer of the Object Orienting Programming course for the Bachelor’s Degree Program in Computer Science and Automation Engineering in the Department of Information Engineering (72 hours, 9 CFU). Syllabus

Fundamentals of Computer Science

Bachelor course in Computer Science and Automation Engineering, Polytechnic University of Marche, 2025

Course lecturer of the Fundamentals of Computer Science course for the Bachelor’s Degree Program in Computer Science and Automation Engineering in the Department of Information Engineering (72 hours, 9 CFU). Syllabus

thesis

Fake News Detection

Combine LLMs and computer vision approaches to tackle misinformation. For example, integrate text-based analysis with image verification to identify and classify fake news, leveraging the strengths of both modalities.

Hallucination Detection

Develop techniques to identify when LLMs generate false or nonsensical information (“hallucinations”). Use metrics or methods to evaluate output reliability and explore corrective strategies to improve accuracy in various applications.

Mental Health Analysis

Analyze Reddit posts to identify typical writing patterns linked to mental health conditions. Use embeddings from Large Language Models (LLMs) to represent these patterns and compare across different models. Build a network of these embeddings and study its structure to uncover insights into mental health trends or behaviors.

Vision Distillation

In a Vision Transformer, distill the information from the query matrix (Q) onto a blank image to visualize how the model identifies and extracts key details, providing insights into its behavior when processing visual inputs.

Vision Explainability

Calculate the gradient of the attention matrix in a Vision Transformer and construct a multilayer gradient network. Create interpretive masks based on centrality, token importance, or other metrics to enhance the understanding of the model’s decision-making process.