Covid-19 Vaccines in Italian public opinion: identifying key issues using Twitter and Natural Language Processing

The COVID-19 pandemic has changed society and people’s lives. The vaccination campaign started December 27-th 2020 in Italy, together with most countries in the European Union. Social media platforms can offer relevant information about how citizens have experienced and perceived the availability of vaccines and the start of the vaccination campaign. This study aims to use machine learning methods to extract sentiments and topics relating to COVID-19 vaccination from Twitter. Between February and May 2021, we collected over 71,000 tweets containing vaccines-related keywords from Italian Twitter users. To get the dominant sentiment throughout the Italian population, spatial and temporal sentiment analysis was performed using VADER, highlighting sentiment fluctuations strongly influenced by news of vaccines' side effects. Additionally, we investigated the opinions of Italians with respect to different vaccine brands. As a result, ‘Oxford-AstraZeneca’ vaccine was the least appreciated among people. The application of the Dynamic Latent Dirichlet Allocation (DLDA) model revealed three fundamental topics, which remained stable over time: vaccination plan info, usefulness of vaccinating and concerns about vaccines (risks, side effects and safety). To the best of our current knowledge, this one the first study on Twitter to identify opinions about COVID-19 vaccination in Italy and their progression over the first months of the vaccination campaign. Our results can help policymakers and research communities track public attitudes towards COVID-19 vaccines and help them make decisions to promote the vaccination campaign

Twitter users perceptions of AI-based e-learning technologies

Today, teaching and learning paths increasingly intersect with technologies powered by emerging artificial intelligence (AI).This work analyses public opinions and sentiments about AI applications that affect e-learning, such as ChatGPT, virtual and augmented reality, microlearning, mobile learning, adaptive learning, and gamification. The way people perceive technologies fuelled by artificial intelligence can be tracked in real time in microblog messages promptly shared by Twitter users, who currently constitute a large and ever-increasing number of individuals. The observation period was from November 30, 2022, the date on which ChatGPT was launched, to March 31, 2023. A two-step sentiment analysis was performed on the collected English-language tweets to determine the overall sentiments and emotions. A latent Dirichlet allocation model was built to identify commonly discussed topics in tweets. The results show that the majority of opinions are positive. Among the eight emotions of the Syuzhet package, ‘trust’ and ‘joy’ are the most common positive emotions observed in the tweets, while ‘fear’ is the most common negative emotion. Among the most discussed topics with a negative outlook, two particular aspects of fear are identified: an ‘apocalyptic-fear’ that artificial intelligence could lead the end of humankind, and a fear for the ‘future of artistic and intellectual jobs’ as AI could not only destroy human art and creativity but also make the individual contributions of students and researchers not assessable. On the other hand, among the topics with a positive outlook, trust and hope in AI tools for improving efficiency in jobs and the educational world are identified. Overall, the results suggest that AI will play a significant role in the future of the world and education, but it is important to consider the potential ethical and social implications of this technology. By leveraging the positive aspects of AI while addressing these concerns, the education system can unlock the full potential of this emerging technology and provide a better learning experience for students

Algoritmi computazionali per l'aggregazione di informazioni "esperte"

L'algoritmo implementato è quello bayesiano di P.A. Morris (1997), che suggerisce di aggregare leinfromazioni, codificate in tremini di distribuzioni di probabilità su 0, in un a densità-sintesi individuata come densiutà a posteriori. Tale impostazione è caratterizzata da una "funzione di calibrazione" che può essere modellizata adoperando l'argomento fiduciale fisheriano (Monari, Agati, 2001) e stimando le varianze degli indicatori di performance attraverso il metodo Delta (Monari, Stracqualursi, 2001). Il software, adoperato dalle Autrici per studiare il comportamento del modello aggregativo bayesiano-fiduciale al variare dei parametri che lo caratterizzano, implementa sia l'acquisizione e aggregazione simultanea delle funzioni di densità fornite degli esperti, sia un processo sequenziale (Agati, 2001) governato da opportuni criteri di stop e di scelta dell'esperto da consultare a ogni stadio.

In situ assessment of quality-related compounds in fruits by using fluorescence sensors

Fruit quality compounds, such as antioxidant phenolics and chlorophyll, were assessed in situ by using a fluorescence method applied by a portable sensor. Indices of anthocyanins (ANTH) and flavonols (FLAV) localized on the fruit surface were obtained based on their screening of chlorophyll fluorescence excitation. The chlorophyll content was estimated by the far red to red chlorophyll fluorescence ratio (CHL index), due to the partial reabsorption of red fluorescence by chlorophyll itself. In kiwifruits, the CHL index was found to be well linearly correlated to the chlorophyll content determined by wet chemistry on the same fruit samples. Full sunlight exposed kiwifruits possessed a higher content of chlorophyll than shaded kiwifruits. This is an important parameter to know for assessing fruit quality and storability. Based on the estimation of the red-pigmented anthocyanins, we defined a new rapid method to determine the maturity level of olives after harvest, giving the proportion of red and green olives, important for the quality of the olive oil produced. In plums, ANTH and FLAV were found to be linearly correlated to the actual content of compounds measured by HPLC analysis of skin extracts. These indices can be, therefore, used to predict the phenolic antioxidant potential of plums and to define their maturity stage

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Stime fiduciali di probabilità nell'aggregazione bayesiana di informazioni

Among the aggregative algorithms suitable to compose various probability densities in a single synthesis distribution - which abstracts information, evaluations, opinions coming from different sources about an unknown quantity -, the bayesian scheme suggested by Peter A. Morris is characterized by the recourse to a "calibration function": a kind of probabilistic balancer, technically configurable like second order prob-ability density and consequently specifiable by fiducial models for estimating probabilities