Over the last decade, the erosion of trust in public institutions and traditional media sources have been proceeding in parallel. As recent developments in media consumption have led to a proliferation of politically charged online misinformation, it is no wonder that many have been questioning whether the spread of fake news has affected the results of recent elections, contributing to the growth of populist party platforms. In this work, we aim to quantify this impact by focusing on the causal effect of the spread of misinformation over electoral outcomes in the 2018 Italian General elections. We exploit the presence of Italian and German linguistic groups in the Trento and Bolzano/Bozen autonomous provinces as an exogenous source of variation, assigning individuals into distinct filter bubbles each differently exposed to misinformation. To do so, we introduce a novel index based on text mining techniques to measure populism. Using this approach, we analyse the social media content of each party and their leaders over the course of the electoral campaign for the 2013 and 2018 elections. We then collect electoral and socio-demographic data from the region and, after constructing a proxy for exposition to misinformation, we measure the change in populist vote across the two groups in-between the two general elections, using a combination of difference-indifference and two-stageleast-squares inference methods. Our results indicate that misinformation had a negligible and non-significant effect on populist vote in Trentino and South Tyrol during the Italian 2018 general elections.

Over the last decade, the erosion of trust in public institutions and traditional media sources have been proceeding in parallel. As recent developments in media consumption have led to a proliferation of politically charged online misinformation, it is no wonder that many have been questioning whether the spread of fake news has affected the results of recent elections, contributing to the growth of populist party platforms. In this work, we aim to quantify this impact by focusing on the causal effect of the spread of misinformation over electoral outcomes in the 2018 Italian General elections. We exploit the presence of Italian and German linguistic groups in the Trento and Bolzano/Bozen autonomous provinces as an exogenous source of variation, assigning individuals into distinct filter bubbles each differently exposed to misinformation. To do so, we introduce a novel index based on text mining techniques to measure populism. Using this approach, we analyse the social media content of each party and their leaders over the course of the electoral campaign for the 2013 and 2018 elections. We then collect electoral and socio-demographic data from the region and, after constructing a proxy for exposition to misinformation, we measure the change in populist vote across the two groups in-between the two general elections, using a combination of difference-in-difference and two-stageleast-squares inference methods. Our results indicate that misinformation had a negligible and non-significant effect on populist vote in Trentino and South Tyrol during the Italian 2018 general elections.

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We compare individuals engaged in online crowdwork against workers in traditional occupations from the United States and Europe, investigating the determinants of access into crowd employment and the nature of the deterioration of salary conditions and job quality characterizing these markets. We do so by matching responses from comparable working conditions surveys, and controlling for most individual and socio-economic characteristics affecting pay. We find that most differences in earnings are largely unexplained by the ability of individuals, and that labor in crowdwork is vastly under-utilized.