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.

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 27th 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.

Tweet topics and sentiments relating to distance learning among Italian Twitter users.

The outbreak of COVID-19 forced a dramatic shift in education, from in-person learning to an increased use of distance learning over the past 2 years. Opinions and sentiments regarding this switch from traditional to remote classes can be tracked in real time in microblog messages promptly shared by Twitter users, who constitute a large and ever-increasing number of individuals today. Given this framework, the present study aims to investigate sentiments and topics related to distance learning in Italy from March 2020 to November 2021. A two-step sentiment analysis was performed using the VADER model and the syuzhet package to understand the overall sentiments and emotions. A dynamic latent Dirichlet allocation model (DLDA) was built to identify commonly discussed topics in tweets and their evolution over time. The results show a modest majority of negative opinions, which shifted over time until the trend reversed. Among the eight emotions of the syuzhet package, 'trust' was the most positive emotion observed in the tweets, while 'fear' and 'sadness' were the top negative emotions. Our analysis also identified three topics: (1) requests for support measures for distance learning, (2) concerns about distance learning and its application, and (3) anxiety about the government decrees introducing the red zones and the corresponding restrictions. People's attitudes changed over time. The concerns about distance learning and its future applications (topic 2) gained importance in the latter stages of 2021, while the first and third topics, which were ranked highly at first, started a steep descent in the last part of the period. The results indicate that even if current distance learning ends, the Italian people are concerned that any new emergency will bring distance learning back into use again.

The combined role of biomarkers and interim PET scan in prediction of treatment outcome in classical Hodgkin's lymphoma: a retrospective, European, multicentre cohort study.

BACKGROUND: Early-interim fluorodeoxyglucose (FDG)-PET scan after two ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) chemotherapy courses (PET-2) represents the most effective predictor of treatment outcome in classical Hodgkin's lymphoma. We aimed to assess the predictive value of PET-2 combined with tissue biomarkers in neoplastic and microenvironmental cells for this disease. METHODS: We enrolled 208 patients with classical Hodgkin's lymphoma and treated with ABVD (training set), from Jan 1, 2002, to Dec 31, 2009, and validated the results in a fully matched independent cohort of 102 patients with classical Hodgkin's lymphoma (validation set), enrolled from Jan 1, 2008, to Dec 31, 2012. The inclusion criteria for both the training and validation sets were: the availability of a representative formalin-fixed, paraffin-embedded tissue sample collected at diagnosis; treatment with ABVD with or without radiotherapy; baseline staging and interim restaging after two ABVD courses with FDG-PET; no treatment change based solely on interim PET result; and HIV-negative status. We used Cox multivariate analysis classification and regression tree (CART) to compare the predictive values of these markers with that of PET-2 and to assess the biomarkers' ability to correctly classify patients whose outcome was incorrectly predicted by PET-2. FINDINGS: In multivariate analysis, PET-2 was the only factor able to predict both progression-free survival (hazard ratio [HR] 33·3 [95% CI 13·6-83·3]; p<0·0001) and overall survival (HR 31·3 [95% CI 3·7-58·9]; p=0·002). In the training set, no factor had a stronger adverse predictive value than a positive PET-2 scan and none was able to correctly reclassify PET-2 positive patients. In PET-2 negative patients, expression of CD68 (≥25%) and PD1 (diffuse or rosetting pattern) in microenvironmental cells, and STAT1 negativity in Hodgkin Reed Sternberg cells identified a subset of PET-2 negative patients with a 3 year progression-free survival significantly lower than that of the remaining PET-2 negative population (21 [64%] of 33 [95% CI 45·2-79·0] vs 130 [95%] of 137 [95% CI 89·4-97·7]; p<0·0001). These findings were reproduced in the validation set. INTERPRETATION: The CART algorithm correctly predicted the response to treatment in more than a half of patients who had a relapse or disease progression despite a negative PET-2 scan, thus increasing the negative predictive value of PET-2. In keeping with preliminary results from interim PET response adapted clinical trials of patients with advanced Hodgkin's lymphoma, there might be a non-negligible proportion of treatment failures in the interim PET negative group treated with standard ABVD. FUNDING: Italian Association for Cancer Research, Bologna Association against leukaemia, lymphoma and myeloma, and Bologna University.