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 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

Random variate generation and connected computational issues for the Poisson–Tweedie distribution

After providing a systematic outline of the stochastic genesis of the Poisson–Tweedie distribution, some computational issues are considered. More specifically, we introduce a closed form for the probability function, as well as its corresponding integral representation which may be useful for large argument values. Several algorithms for generating Poisson–Tweedie random variates are also suggested. Finally, count data connected to the citation profiles of two statistical journals are modeled and analyzed by means of the Poisson–Tweedie distribution

L’OPINIONE ITALIANA SULLA DIDATTICA A DISTANZA ATTRAVERSO UNA ‘SENTIMENT ANALYSIS’ DEI TWEET NEL BIENNIO 2020-2021.

ABSTRACT. In 2020, many efforts have been made to limit the diffusion of COVID-19 infection. Faced with this emergency, everyone's lives were changed through ‘social isolation’. So, starting from March 2020, the Italian school system experienced a massive application of Distance Learning. These changes brought about to a certain degree of tension, which have also accelerated the use of innovative educational tools and led the way for a digitalisation of learning. Natural language processing (NLP) is a very powerful technique for social media data processing. Social media can provide the information to understand public opinions of different social phenomena. This paper implements a Sentiment Analysis over 25,000 Italian tweets talking about distance learning from March 2020 to November 2021. The analysis highlighted significant differences between Italian regions and the change in Sentiment due to the transition from an emergency Distance Learning to the Integrated Digital Learning