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The unequal distribution of the costs and benefits of living with wildlife is one of the causes of human-carnivore conflicts. The existence of large carnivores is valued globally, but the costs of damages and management impacts human residents. The Apennine brown bear is endemic of central Italy and, besides its ecological value, it can attract tourists in search of nature and wilderness. The Advertising Value Equivalent of the bears' appearances in the national newspaper and on television from 2015 to 2020 was used to calculate the economic value of this flagship species as a destination image. The 11 million Euro of Advertising Value Equivalent estimated largely exceeded the amount of reimbursements sustained by the Park to manage this carnivore in the same period. This evaluation of cultural value could be used to highlight the economic benefits provided by the bear and contribute to the discussions with managers and stakeholders. [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]

The pervasiveness of advanced technologies and their disruptive impact on society have spurred the debate on the emergence of a new industrial revolution and on its positive and negative effects, both at an individual and spatial level. This paper aims to contribute to this debate, focusing attention on the perception of changes related to the Fourth Industrial Revolution and exploring new methods of analysis of the manifestations of both techno‐enthusiasm and opposition to it. Starting from the extensive literature in this field, the work adopts two research perspectives: the study of imaginaries and narratives developed around the Fourth Industrial Revolution, which convey different messages from social groups and places; the geographies of opportunity and discontent, which address the resentment expressed by some localities towards advanced technological models and growing inequalities. In this work the Fourth Industrial Revolution is not interpreted through data about the technological variables or interviews to protagonists of the phenomenon; rather, emphasis is on the points of view of non‐institutional subjects and, in particular, the opinions expressed by people on the Web. For this reason, the sentiment analysis has been adopted to identify both positive and negative polarities and the relevance of specific feelings through the selection of key words related to the notion of the Fourth Industrial Revolution. The empirical analysis based on this methodology focuses on the Italian case in a specific period (first and second phase of the pandemic, from January 2020 to September 2021) and, at a local level, on the comparison between four medium‐sized cities (Pisa, Lecce, Taranto and Terni). This paper also tries to extend recent contributions through the provision of new perspectives for the definition of policies designed with the involvement of the population and places regarding both the processes of technological change and the definition of new socio‐spatial models. [ABSTRACT FROM AUTHOR]

This study undertakes sentiment analysis of online reviews of public exterior spaces – historic squares and railway stations – in popular destinations in Italy, with the aim of offering new perspectives of community engagement in urban design analysis. The experience of walking through urban spaces in Italian heritage cities is evaluated under indicators of place quality and connectivity, i.e., aesthetic perception, social interaction, body mobility, facilities and amenities, sense of safety, and destination loyalty. Such advanced analysis can reshape the way we interpret the thoughts and emotions of wider communities so that these are included in local place-focused development strategies. [ABSTRACT FROM AUTHOR]

The debate on climate change has increasingly attracted attention, especially among young people, since the foundation of the movement Friday for Future and the raising fame of Greta Thunberg. Social media websites can be used as a data source for mining public opinion on a variety of subjects including climate change. Twitter, in particular, allows for the evaluation of public opinion across time. Although it is a known problem that Twitter population is biased with respect to the whole population, it is also true that Twitter users are more likely to be young people. For this reason, the sentiment analysis of Twitter textual data on climate topics provides valuable insights into the climate discussion and could be considered as representative of the rising climate movement. In this study, a large dataset of Italian tweets between 2016 and 2022 containing a set of keywords related to climate change (e.g. Global warming, sustainable development, etc.) is analysed using volume analysis and text mining techniques such as topic modelling and sentiment analysis. Topic modelling, performed using word embedding, allows validating the keywords' set and providing the prevalent discussion in Italy about the climate agenda and the major concerns related to climate emergency. Both daily volume and sentiment of tweets series have been analysed. The first series allows assessing the Italian participation to the climate debate, while the latter provides useful insights on the overall evolving mood during these years. In particular, we show that the major Italian concerns are related with global warming with a negative mood while a positive mood is recorded when public policies on environment are implemented. [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]

Since 2012, driven by the desire to propose a subjective well-being index (SWBI) complementary to the traditional measures, with high time and space frequency, our team evaluates, analysing Twitter data, a composite index that captures various aspects and dimensions of individual and collective life. The SWBI is a multidimensional indicator whose components were inspired by the dimensions adopted for the Happy Planet Index provided by the New Economic Foundation. In detail, it consists of eight dimensions that describe three different areas: personal well-being, social well-being and well-being at work. The Italian subjective well-being index ( SWBI ITA ), that we display here, audits the Italian subjective well-being revealed by tweets acquired via the public Twitter API, written in the Italian language, and posted from Italy from January 2012 to December 2017. Around 1–5% of the data includes geo-referenced information, which allows us to provide an index at local level. The Twitter data analysis is carried on with a human supervised sentiment analysis method, the Integrated Sentiment Analysis (iSA) algorithm. In this work, after a weighting procedure adopted to partially overcome the selection bias caused by the use of data from social network, we describe the SWBI ITA dimensions in the considered period at the regional level. Moreover, for some dimensions, for which a similar currently available measure provided by Italian official statistics exists, comparisons are proposed emphasizing novelties, similarities and differences. [ABSTRACT FROM AUTHOR]

Since December 2019, Coronavirus has been spreading rapidly across the world. From its early stages, while the rest of the world was still new to COVID-19, Italy was one of the first and most severely hit countries. The chronic nature of the COVID-19 outbreak and untimely treatments were translated into an unstable social and emotional environment. This study explores the societal response to the first wave of the pandemic outbreak in Italy, looking at geographic variations, social and policy perspectives, and sentiments across northern, central, and southern regions. For this study, we collected nearly three million tweets associated with the coronavirus outbreak in the Italian regions of Lombardy, Lazio, and Sicily, during the months of March-May, 2020. A sentiment analysis was developed to (i) identify the spatio-temporal distribution of the polarity patterns of anger, fear, and joy; (ii) test correlation between these sentiments and COVID-19 cases; and (iii) determine the statistical significance between the sentiments across geographies. Findings provide insights on the relationship between sentiment variations and spatio-temporal case distribution associated with regional differences. These findings align with those of the "Track IT. COVID-19 International Survey," that investigated the societal response of Italians to the new measures enforced by local governments. Overall, the findings of this study can be weaved together into important implications for emotional wellbeing and economic perspective. [ABSTRACT FROM AUTHOR]

To understand the predominant topics of discussion, stance and associated language used on social media platforms relating to maternal vaccines in 15 countries over a six-month period. In 2019, the World Health Organisation prioritised vaccine hesitancy as a top ten global health threat and recognized the role of viral misinformation on social media as propagating vaccine hesitancy. Maternal vaccination offers the potential to improve maternal and child health, and to reduce the risk of severe morbidity and mortality in pregnancy. Understanding the topics of discussion, stance and language used around maternal vaccines on social media can inform public health bodies on how to combat vaccine misinformation and vaccine hesitancy. Social media data was extracted (Twitter, forums, blogs and comments) for six months from 15 countries (Australia, Brazil, Canada, France, Germany, India, Italy, Korea, Mexico, Panama, South Africa, Spain, United Kingdom and United States). We used stance, discourse and topic analysis to provide insight into the most frequent and weighted keywords, hashtags and themes of conversation within and across countries. We exported a total of 19,192 social media posts in 16 languages obtained between 1st November 2018 and 30th April 2019. After screening all posts, 16,000 were included in analyses, while excluding retweets, 2,722 were annotated for sentiment. Main topics of discussion were the safety of the maternal influenza and pertussis vaccines. Discouraging posts were most common in Italy (44.9%), and the USA (30.8%). The content and stance of maternal vaccination posts from November 2018 to April 2019 differed across countries, however specific topics of discussion were not limited to geographical location. These discussions included the promotion of vaccination, involvement of pregnant women in vaccine research, and the trust and transparency of institutions. Future research should examine the relationship between stance (promotional, neutral, ambiguous, discouraging) online and maternal vaccination uptake in the respective regions. [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]

Going beyond source and content pluralism, we propose a two-dimensional audience-based measure of perceived pluralism by exploiting the practice of "social TV". For this purpose, 135,228 tweets related to 30 episodes of prime time political talk shows broadcast in Italy in 2014 have been analyzed through supervised sentiment analysis. The findings suggest that the two main TV networks compete by addressing generalist audiences. The public television offers a plural set of talk shows but ignores the anti-political audience. The ideological background of the anchorman shapes the audience's perception, while the gender of the guests does not seem to matter. [ABSTRACT FROM AUTHOR]

The spread of the Covid-19 pandemic in Italy, in the period March–May 2020, quickly triggered a deep crisis, causing an immediate economic slowdown and consequently a strong contraction in domestic demand and trade. The food supply chain faced severe difficulties, although its anti-cyclical nature allowed for greater resilience compared to other economic sectors. In this framework of ongoing uncertainty, it is important to understand the response of farms to the crisis, and their role in the sustainability of the post-pandemic food supply chain, even for future policy interventions in the short and medium term. The purpose of the paper is to investigate how diversification affected the response of farms to the Covid-19 crisis, and explore whether the changes required by the post-crisis scenario can produce opportunities for their activities. The study investigates a sample of fifteen farms in central Italy through semi-structured interviews, performing a lexicon-based text and sentiment analysis. The results highlight the importance of farm diversification in dealing with the Covid-19 crisis, and emphasise the role of diversified farms for the sustainability of the agri-food system. These results have interesting policy implications, particularly regarding support for the competitiveness of farms by improving sustainable logistics, electronic commerce and exchanges of knowledge and innovations among farmers; these measures should be taken into account to target the future agricultural, rural and food policies, at both national and local level. • The first Covid-19 pandemic wave in March–May 2020 strongly hit the Italian food supply chain. • A direct survey on 15 diversified farms was carried out, through semi-structured interviews. • Text and sentiment analysis were applied to understand how farmers tackled the challenges of the crisis period. • Results stress the importance of farm diversification during the pandemic wave. • Policy interventions to reinforce competitiveness are needed to overcome the crisis. [ABSTRACT FROM AUTHOR]

COVID-19 has had economic, social and environmental impacts worldwide. Governments have adopted containment measures to limit the spread of the virus. Urban green spaces (UGSs) were included among the non-essential activities and were consequently closed during the lockdown periods in some countries. This study analysed tweets posted by users to understand the citizens' perception and sentiment in relation to the closure of UGS in Italy. Results revealed that people felt a strong deprivation feeling in relation to the restrictions imposed on UGS access, which limited the number of spaces for supporting mental and physical wellbeing of citizens. Users from urban areas were more affected by the lockdowns and more willing to share thoughts on social media, demonstrating a strong emotionality. Furthermore, findings show that users seemed concerned about their children's health, expressing awareness about the benefits of being in contact with nature. UGS is able to provide services to citizens, and close-to-home parks are fundamental for the community, in particular during a health emergency. The implementation of urban design, which includes green areas to support health and environment challenges, should be addressed by policy-makers to create opportunities for a green and resilient recovery of cities, and prepare for future emergencies. • The closure of urban green spaces produced a strong deprivation feeling in citizens. • Urban green spaces provide services and relief to citizens during a health emergency. • Twitter users expressed concern for the health of their children, stressing benefits of contact with nature. • Planning green spaces is fundamental for the recovery of cities and preventing future emergencies. [ABSTRACT FROM AUTHOR]

The rise of social network sites reopened the debate on the ability of traditional media to influence public opinion and act as an agenda setter. To answer this question, the present paper investigates first-level and second-level agenda-setting effects in the online environment by focusing on two heated Italian political debates (the reform of public funding of parties and the debate over austerity). By employing innovative and efficient statistical methods such as the lead–lag analysis and supervised sentiment analysis, we compare the attention devoted to each issue and the content spread by online news media and Twitter users. Our results show that online media keep their first-level agenda-setting power even though we find a marked difference between the slant of online news and the Twitter sentiment. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing attention in the literature on the possibility of analyzing social media as a useful complement to traditional off-line polls to monitor an electoral campaign. Some scholars claim that by doing so, we can also produce a forecast of the result. Relying on a proper methodology for sentiment analysis remains a crucial issue in this respect. In this work, we apply the supervised method proposed by Hopkins and King to analyze the voting intention of Twitter users in the United States (for the 2012 Presidential election) and Italy (for the two rounds of the centre-left 2012 primaries). This methodology presents two crucial advantages compared to traditionally employed alternatives: a better interpretation of the texts and more reliable aggregate results. Our analysis shows a remarkable ability of Twitter to “nowcast” as well as to forecast electoral results. [ABSTRACT FROM AUTHOR]