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Urban mobility is a current and hot topic for local administrations, and not only due to the numerous road accidents in urban areas, often caused by the incorrect behaviour of scooter drivers. The diffusion of electric scooters occurred, above all, after the pandemic. While on the one hand, social distancing has forced many to re-evaluate traveling by car, there are many citizens who have adopted innovations such as pedal-assisted bicycles and, above all, electric scooters - the latter being the protagonists of a real boom. In addition to ensuring fast travel and ease of use, the electric scooter and its use have sparked public debate regarding safety and apparent deregulation. Very often in Italy and beyond, the use of electric scooters is associated with a perception of fear and scepticism about micro-mobility, as well as the diffusion of some models relating to sustainable mobility. This derives in part from the previous characteristics of the vehicles produced and manufactured in China that do not comply with the law, and in that case, are truly dangerous. But given the recent rules at both the Italian and European levels, we are very often protected against products that do not comply with the laws, and therefore, we have greater road safety. The recent legislation on use on Italian roads has changed the characteristics of users, vehicles, and infrastructures in which to circulate. The safety of the electric scooter has been a long-debated topic, especially since this vehicle has seen a boom in sales and rentals since 2020, thanks to the incentives for the purchase and the willingness of Italians to use public transport less and less. To define the choice of an electric scooter as a sustainable modal choice, it is necessary, beyond safety, to investigate the perception of users in terms of gender equity, use in the first and last mile, and perception in terms of green transport modes. Starting from this, a survey was implemented, and subsequently, a descriptive statistical analysis was conducted on about 550 users in the metropolis of Palermo in Sicily. The results related to the period 2019-2022 showed that the electric scooter is certainly a practical and green vehicle but must be driven with the same attention we pay in cars or motorcycles. The chi-square analysis defined the trend variations connected to the users' psychosocial parameters according to the user's gender and age. The scenario shows the need for timely interventions, above all to protect vehicle users but also pedestrians who are often involved in accidents due to the use of vehicles on sidewalks. The survey shows that scooter users are the first to request more safety, starting with the creation of protected lanes such as cycle paths or dedicated areas. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic reminded us how vaccination can be a divisive topic on which the public conversation is permeated by misleading claims, and thoughts tend to polarize, especially on online social networks. In this work, motivated by recent natural language processing techniques to systematically extract and quantify opinions from text messages, we present a differential framework for bivariate opinion formation dynamics that is coupled with a compartmental model for fake news dissemination. Thanks to a mean-field analysis we demonstrate that the resulting Fokker-Planck system permits to reproduce bimodal distributions of opinions as observed in polarization dynamics. The model is then applied to sentiment analysis data from social media platforms in Italy, in order to analyze the evolution of opinions about Covid-19 vaccination. We show through numerical simulations that the model is capable to describe correctly the formation of the bimodal opinion structure observed in the vaccine-hesitant dataset, which is witness of the known polarization effects that happen within closed online communities. [ABSTRACT FROM AUTHOR]

Growing Mediterranean seafood consumption, increasing consumers' awareness of food safety and quality, and changing food lifestyles are leading to the development of new food products. However, the majority of new food products launched on the market are expected to fail within the first year. One of the most effective ways to enhance new product success is by involving consumers during the first phases of New Product Development (NPD), using the so-called co-creation approach. Based on data collected through online discussion rooms, two new seafood product concepts—sardine fillets and sea burgers—were evaluated by a set of potential consumers in three Mediterranean countries—Italy, Spain, and Croatia. Textual information was analyzed by first using the topic modeling technique. Then, for each main topic identified, sentiment scores were calculated, followed by the identification of the main related emotions that were evoked. Overall, consumers seem to positively evaluate both proposed seafood product concepts, and three recurrent positive emotions (trust, anticipation, joy) were identified in relation to the main topics aroused during the discussions. The results of this study will be useful to guide future researchers and actors in this industry in the next development steps of the targeted seafood products in Mediterranean countries. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for sentiment analysis with emphasis on the emotional aspects of people visiting the Herculaneum Archaeological Park in Italy during the period of the COVID-19 pandemic. The methodology provides a valuable means of continuous feedback on perceived risk of the site. A semantic analysis on Twitter text messages provided input to the risk management team with which they could respond immediately mitigating any apparent risk and reducing the perceived risk. A two-stage approach was adopted to prune a massively large dataset from Twitter. In the first phase, a social network analysis and visualisation tool NodeXL was used to determine the most recurrent words, which was achieved using polarity. This resulted in a suitable subset. In the second phase, the subset was subjected to sentiment and emotion mapping by survey participants. This led to a hybrid approach of using automation for pruning datasets from social media and using a human approach to sentiment and emotion analysis. Whilst suffering from COVID-19, equally, people suffered due to loneliness from isolation dictated by the World Health Organisation. The work revealed that despite such conditions, people's sentiments demonstrated a positive effect from the online discussions on the Herculaneum site. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objectives: To describe how the recent lock-down, related to SARS-COV-II outbreak in Italy, affected People With Epilepsy (PwE), we designed a survey focused on subjective reactions. Using Natural Language Processing (NLP), we analyzed words PwE and People without Epilepsy (PwoE) chose to express their reactions. Methods: As a subset of a larger survey, we collected from both PwE (427) and PwoE (452) single words (one per subject) associated to the period of lock down. The survey was spread thanks to the efforts of Italian league against epilepsy Foundation during the days of maximum raise of the pandemic. Data were analyzed via bag of word and sentiment analysis techniques in R. Results: PwoE and PwE showed significantly different distribution in word choice (X2, p = 4.904e−13). A subset of subject used positive words to describe this period, subjects with positive feelings about the lock down were more represented in the PwE group (X2, p = 0.045). Conclusion: PwoE developed reactive stress response to the restrictions enacted during lock-down. PwE, instead, chose words expressing sadness and concern with their disease. PwE appear to internalize more the trauma of lock down. Interestingly PwE also expressed positive feelings about this period of isolation more frequently than PwoE. Our study gives interesting insights on how People with Epilepsy react to traumatic events, using methods that evidence features that do not emerge with psychometric scales. [ABSTRACT FROM AUTHOR]

With the increase of social media usage, a huge new source of data has become available. Despite the enthusiasm linked to this revolution, one of the main outstanding criticisms in using these data is selection bias. Indeed, the reference population is unknown. Nevertheless, many studies show evidence that these data constitute a valuable source because they are more timely and possess higher space granularity. We propose to adjust statistics based on Twitter data by anchoring them to reliable official statistics through a weighted, space-time, small area estimation model. As a by-product, the proposed method also stabilizes the social media indicators, which is a welcome property required for official statistics. The method can be adapted anytime official statistics exists at the proper level of granularity and for which social media usage within the population is known. As an example, we adjust a subjective well-being indicator of "working conditions" in Italy, and combine it with relevant official statistics. The weights depend on broadband coverage and the Twitter rate at province level, while the analysis is performed at regional level. The resulting statistics are then compared with survey statistics on the "quality of job" at macro-economic regional level, showing evidence of similar paths. [ABSTRACT FROM AUTHOR]

According to the literature about customer satisfaction and loyalty, it is possible to define knowledge-based system to support management decision-making in the organizations. Nevertheless, the problem as to how much the context impacts on correlation has not been investigated in the literature. This paper focuses on developing of Decision Support System (DSS) taking into account correlations among statistical factors, i.e., expert knowledge, and customers' opinions depending on several contextual features, e.g., culture, location, in order to build context-sensitive simulation environment. The proposed work defines a general system design workflow to tailor knowledge-based DSS by using a fuzzy model to quantify correlations among variables in a given context. We explore ontologies to represent correlations among statistical factors, e.g., Calculative Commitment, Quality of Service. We apply fuzzy data analysis techniques to train fuzzy classifier on the customer's opinions collected by survey. Finally, synergistic usage of Description Logic and Fuzzy Theory allows the implementation of a simulation environment that supports the management team to tune business strategies. The framework has been instantiated for a case study to support public administration at the University of the Salerno. [ABSTRACT FROM AUTHOR]

What is the relationship between Internet usage and political trust? To answer this question, we performed a cross-sectional analysis of Eurobarometer survey data related to 27 countries and a supervised sentiment analysis of online political information broadcast during the Italian debate on the reform of public funding of parties. The results disclose the differences between Web 1.0 websites and Web 2.0 social media, showing that consumption of news from information/news websites is positively associated with higher trust, while access to information available on social media is linked with lower trust. This has implications for the debate on social media as a public sphere and for the tension between professional and citizen journalism. [ABSTRACT FROM AUTHOR]

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users' opinions about the web interface and the corresponding functionality. [ABSTRACT FROM AUTHOR]

Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better handle the Twitter jargon. This work aims to introduce a different approach for Twitter sentiment analysis based on two steps. Firstly, the tweet jargon, including emojis and emoticons, is transformed into plain text, exploiting procedures that are language-independent or easily applicable to different languages. Secondly, the resulting tweets are classified using the language model BERT, but pre-trained on plain text, instead of tweets, for two reasons: (1) pre-trained models on plain text are easily available in many languages, avoiding resource- and time-consuming model training directly on tweets from scratch; (2) available plain text corpora are larger than tweet-only ones, therefore allowing better performance. A case study describing the application of the approach to Italian is presented, with a comparison with other Italian existing solutions. The results obtained show the effectiveness of the approach and indicate that, thanks to its general basis from a methodological perspective, it can also be promising for other languages.

Social media have become a common way for people to express their personal viewpoints, including sentiments about health topics. We present the results of an opinion mining analysis on vaccination performed on Twitter from September 2016 to August 2017 in Italy. Vaccine-related tweets were automatically classified as against, in favor or neutral in respect of the vaccination topic by means of supervised machine-learning techniques. During this period, we found an increasing trend in the number of tweets on this topic. According to the overall analysis by category, 60% of tweets were classified as neutral, 23% against vaccination, and 17% in favor of vaccination. Vaccine-related events appeared able to influence the number and the opinion polarity of tweets. In particular, the approval of the decree introducing mandatory immunization for selected childhood diseases produced a prominent effect in the social discussion in terms of number of tweets. Opinion mining analysis based on Twitter showed to be a potentially useful and timely sentinel system to assess the orientation of public opinion toward vaccination and, in future, it may effectively contribute to the development of appropriate communication and information strategies.

Background: Recently, social networks have become a popular source of information on health topics. Particularly, in Italy, there is a lively discussion on the web regarding vaccines also because there is low vaccination coverage, vaccines hesitancy, and anti-vaccine movements. For these reasons, in 2017, Institutions have introduced a law to force children to make ten compulsory vaccines for school attendance and proposed a vaccination campaign. On social networks, this law has fostered a fierce discussion between pro-vaccinations and anti-vaccinations people. This paper aims to understand if and how the population's opinion has changed before the law and after the vaccination campaign using the titles of the videos uploaded on Youtube in these periods., Method: Using co-occurrence network (CON) and sentiment analysis, we analysed the topics of YouTube Italian videos on vaccines in 2017 and 2018., Results: The CON confirms that vaccinations were very disapproved before the law. Instead, after the communication campaign, people start to be less critical. The sentiment analysis shows that the intense vaccination campaign also promoted by medical doctors pushed the sentiment to change polarity from a prevailing negative opinion in 2017 (52% negative) to a positive one in 2018 (54% positive)., Conclusion: At the population level, the potential misinformation of social networks could be significant and is a real risk for health. Our study highlights that vaccination campaigns on social networks could be an essential instrument of health policies and a sharp weapon to fight ignorance and misrepresentations of non-qualified people influencing individuals' decision-making.