Populism as a battlefield Populist actors and their representation on social and legacy media during the european parliament elections 2019 in Finland, Italy and the Netherlands

The objective of the present research is to describe and explain populist actors and populism as a concept and their representation on social and legacy media during the 2019 EU elections in Finland, Italy and The Netherlands. This research tackles the topic of European populism in the context of political communication and its relation to both the legacy and digital media within the hybrid media system. Departing from the consideration that populism and populist rhetoric are challenging concepts to define, I suggest that they should be addressed and analyzed through the usage of a combination of methods and theoretical perspectives, namely Communication Studies, Corpus Linguistics, Political theory, Rhetoric and Corpus-Assisted Discourse Studies. This thesis considers data of different provenance. On the one hand, for the Legacy media part, newspapers articles were collected in the three countries under study from the 1st until the 31st of May 2019. Each country’s legacy system is represented by three different quality papers and the articles were collected according to a selection of keywords (European Union Elections and Populism in each of the three languages). On the other hand, the Digital media data takes into consideration Twitter tweets collected during the same timeframe based on particular country-specific hashtags and tweets by identified populist actors. In order to meet the objective of this study, three research questions are posed and the analysis leading to the results are exhaustively presented and further discussed. The results of this research provide valuable and novel insights on how populism as a theme and a concept is being portrayed in the context of the European elections both in legacy and digital media and political communication in general.

Social learning and action understanding in human observers: contributions of sensori-motor constraints and prior information

The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.

Social Welfare and Behavioral Public Policies

In this work I discuss several key aspects of welfare economics and policy analysis and I propose two original contributions to the growing field of behavioral public policymaking. After providing a historical perspective of welfare economics and an overview of policy analysis processes in the introductory chapter, in chapter 2 I discuss a debated issue of policymaking, the choice of the social welfare function. I contribute to this debate by proposing an original methodological contribution based on the analysis of the quantitative relationship among different social welfare functional forms commonly used by policy analysts. In chapter 3 I then discuss a behavioral policy to contrast indirect tax evasion based on the use of lotteries. I show that the predictions of my model based on non-expected utility are consistent with observed, and so far unexplained, empirical evidence of the policy success. Finally, in chapter 4 I investigate by mean of a laboratory experiment the effects of social influence on the individual likelihood to engage in altruistic punishment. I show that bystanders’ decision to engage in punishment is influenced by the punishment behavior of their peers and I suggest ways to enact behavioral policies that exploit this finding.

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

Essays in development and political economy

This thesis includes three papers studying diverse questions in development, economic history and political economy. The first two chapters, that fall under development and economic history, use novel forms of text data and analysis to answer the questions at hand. The first chapter studies the possible impact of a historically matrilineal and matrilocal caste group on present day outcomes of gender equality. It introduces a novel surname strategy using electoral data to deduce caste from the surnames of electors and overcomes the unavailability of caste data. It shows proof of persistence of caste in space. And finally, following a matching exercise it concludes that the effect of the matrilineal and matrilocal caste on present day gender outcomes might not be as strong as previously believed. The second paper studies how discriminatory fake news arises and spatially diffuses. It focuses on India at the onset of the COVID-19 pandemic: on March 30, a Muslim convention (the Tablighi Jamaat) in New Delhi became publicly recognized as a COVID hotspot, and the next day, fake news on Muslims intentionally spreading the virus spiked. Using Twitter data, it finds, in cross-sectional and difference-in-difference settings, that discriminatory fake news became much more widespread after March 30 (1) in New Delhi, (2) in districts closer to New Delhi, and (3) in districts with higher social media interactions with New Delhi. Further, it shows that, after March 30, discriminatory fake news was more common in districts historically exposed to attacks by Muslim groups. The final paper is a political economy paper that studies the short term and long term effect of earlier eligibility on voting in the context of a large North Italian municipality setting with little institutional barriers to voting. It also studies the differing mobilisation of members in the same household by newly eligible voters.