In the past decades, the amount of publicly available data has surged, offering new opportunities to enhance our decision-making process with the aid of accurate predictions and insightful analytics. This is the essence of data science.
Under this framework, we seek to understand how popularity follows the products of creativity, which may remain concealed in the underground scene or become widely acclaimed artworks. As an applied mathematician, I investigate musical records using statistical theory and machine learning, to determine how artistic merits, public’s tastes, and commercial strategies will designate the next popular hit.
Misinformation threatens our societies, but little is known about how the production of news by unreliable sources relates to supply and demand dynamics. We exploit the burst of news production triggered by the COVID-19 outbreak through an Italian database partially annotated for questionable sources. We compare news supply with news demand, as captured by Google Trends data. We identify the Granger causal relationships between supply and demand for the most searched keywords, quantifying the inertial behaviour of the news supply. Focusing on COVID-19 news, we find that questionable sources are more sensitive than general news production to people’s interests, especially when news supply and demand mismatched. We introduce an index assessing the level of questionable news production solely based on the available volumes of news and searches. We contend that these results can be a powerful asset in informing campaigns against disinformation and providing news outlets and institutions with potentially relevant strategies.
COVID-19, Electricity consumption, Electricity generation, Governmental restrictions, Renewable share, Electricity grid”,
abstract = “When COVID-19 pandemic spread in Europe, governments imposed unprecedented confinement measures with mostly unknown repercussions on contemporary societies. In some cases, a considerable drop in energy consumption was observed, anticipating a scenario of sizable low-cost energy generation, from renewable sources, expected only for years later. In this paper, the impact of governmental restrictions on electrical load, generation and transmission was investigated in 16 European countries. Using the indices provided by the Oxford COVID-19 Government Response Tracker, precise restriction types were found to correlate with the load drop. Then the European grid was analysed to assess how the load drop was balanced by the change in generation and transmission patterns. The same restriction period from 2020 was compared to previous years, accounting for yearly variability with ad hoc statistical technique. As a result, generation was found to be heavily impacted in most countries with significant load drop. Overall, generation from nuclear, and fossil coal and gas sources was reduced, in favour of renewables and, in some countries, fossil gas. Moreover, intermittent renewables generation increased in most countries without indicating an exceptional amount of curtailments. Finally, the European grid helped balance those changes with an increase in both energy exports and imports, with some net exporting countries becoming net importers, notably Germany, and vice versa. Together, these findings show the far reaching implications of the COVID-19 crisis, and contribute to the understanding and planning of higher renewables share scenarios, which will become more prevalent in the battle against climate change.