The "Green Pass" Controversy in the Italian Twittersphere: a Digital Methods Mapping

In this paper we developed a digital methods mapping of the controversy arises from the adoption of the so-called "Green Pass" in Italy Adopting an "agnostic" approach to our object of study, we used a well-established research design: namely, to collect all the tweets that contain words related to conversations about the green pass in Italy (e.g.: green pass, #greenpass). In this way, the sample collected amounts to 4.307.487 tweets, published between June 15, 2021, and December 15, 2021. To bring out the "voices" of the different actors involved in the controversy we adopted a quali-quantitative approach: on the one hand, by means of computational techniques, we reconstructed the structural relations in which the actors are involved and its evolution over time; on the other hand, by means of content analysis we enriched our map with an interpretation of the discourse surrounding the controversy. Finally, these cartographic results are discussed considering the Italian media system functioning, in order to understand how its conformation may have influenced the public debate concerning the green pass

A machine learning approach to analyse and predict the electric cars scenario: The Italian case

: The automotive market is experiencing, in recent years, a period of deep transformation. Increasingly stricter rules on pollutant emissions and greater awareness of air quality by consumers are pushing the transport sector towards sustainable mobility. In this historical context, electric cars have been considered the most valid alternative to traditional internal combustion engine cars, thanks to their low polluting potential, with high growth prospects in the coming years. This growth is an important element for companies operating in the electricity sector, since the spread of electric cars is necessarily accompanied by an increasing need of electric charging points, which may impact the electricity distribution network. In this work we proposed a novel application of machine learning methods for the estimation of factors which could impact the distribution of the circulating fleet of electric cars in Italy. We first collected a new dataset from public repository to evaluate the most relevant features impacting the electric cars market. The collected datasets are completely new, and were collected starting from the identification of the main variables that were potentially responsible for the spread of electric cars. Subsequently we distributed a novel designed survey to further investigate such factors on a population sample. Using machine learning models, we could disentangle potentially new interesting information concerning the Italian scenario. We analysed it, in fact, according to different geographical Italian dimensions (national, regional and provincial) and with the final identification of those potential factors that could play a fundamental role in the success and distribution of electric cars mobility. Code and data are available at: https://github.com/GiovannaMariaDimitri/A-machine-learning-approach-to-analyse-and-predict-the-electric-cars-scenario-the-Italian-case

The "Green Pass" Controversy in the Italian Twittersphere: a Digital Methods Mapping

In this paper we developed a digital methods mapping of the controversy arises from the adoption of the so-called "Green Pass" in Italy Adopting an "agnostic" approach to our object of study, we used a well-established research design: namely, to collect all the tweets that contain words related to conversations about the green pass in Italy (e.g.: green pass, #greenpass). In this way, the sample collected amounts to 4.307.487 tweets, published between June 15, 2021, and December 15, 2021. To bring out the "voices" of the different actors involved in the controversy we adopted a quali-quantitative approach: on the one hand, by means of computational techniques, we reconstructed the structural relations in which the actors are involved and its evolution over time; on the other hand, by means of content analysis we enriched our map with an interpretation of the discourse surrounding the controversy. Finally, these cartographic results are discussed considering the Italian media system functioning, in order to understand how its conformation may have influenced the public debate concerning the green pass

The "Green Pass" Controversy in the Italian Twittersphere: a Digital Methods Mapping

In this paper we developed a digital methods mapping of the controversy arises from the adoption of the so-called "Green Pass" in Italy Adopting an "agnostic" approach to our object of study, we used a well-established research design: namely, to collect all the tweets that contain words related to conversations about the green pass in Italy (e.g.: green pass, #greenpass). In this way, the sample collected amounts to 4.307.487 tweets, published between June 15, 2021, and December 15, 2021. To bring out the "voices" of the different actors involved in the controversy we adopted a quali-quantitative approach: on the one hand, by means of computational techniques, we reconstructed the structural relations in which the actors are involved and its evolution over time; on the other hand, by means of content analysis we enriched our map with an interpretation of the discourse surrounding the controversy. Finally, these cartographic results are discussed considering the Italian media system functioning, in order to understand how its conformation may have influenced the public debate concerning the green pass.<br /

A machine learning approach to analyse and predict the electric cars scenario: The Italian case.

The automotive market is experiencing, in recent years, a period of deep transformation. Increasingly stricter rules on pollutant emissions and greater awareness of air quality by consumers are pushing the transport sector towards sustainable mobility. In this historical context, electric cars have been considered the most valid alternative to traditional internal combustion engine cars, thanks to their low polluting potential, with high growth prospects in the coming years. This growth is an important element for companies operating in the electricity sector, since the spread of electric cars is necessarily accompanied by an increasing need of electric charging points, which may impact the electricity distribution network. In this work we proposed a novel application of machine learning methods for the estimation of factors which could impact the distribution of the circulating fleet of electric cars in Italy. We first collected a new dataset from public repository to evaluate the most relevant features impacting the electric cars market. The collected datasets are completely new, and were collected starting from the identification of the main variables that were potentially responsible for the spread of electric cars. Subsequently we distributed a novel designed survey to further investigate such factors on a population sample. Using machine learning models, we could disentangle potentially new interesting information concerning the Italian scenario. We analysed it, in fact, according to different geographical Italian dimensions (national, regional and provincial) and with the final identification of those potential factors that could play a fundamental role in the success and distribution of electric cars mobility. Code and data are available at: https://github.com/GiovannaMariaDimitri/A-machine-learning-approach-to-analyse-and-predict-the-electric-cars-scenario-the-Italian-case

Performance for the four different applied models in provincial analysis: Linear Regression (LR), Ridge Regression (RR), Decision Tree Regressor (DT), Extreme Gradient Boosting Regressor (XGBR).

The last row shows the metric’s average value for the four years.</p

The workflow followed for the research.

Starting from the regional analysis (red), the focus of the research was restricted to the province (blue), up to analyzing a sample of the population through the administration of a survey (black).</p

Describing features types, divided for category which have been collected through the distribution of our survey.

Describing features types, divided for category which have been collected through the distribution of our survey.</p

Performance for the four different applied models in regional analysis: Linear Regression (LR), Ridge Regression (RR), Decision Tree Regressor (DT), Extreme Gradient Boosting Regressor (XGBR).

The last row shows the metric’s average value for the four years.</p