The World Health Organization (WHO) announced on 11 March 2020 that the new coronavirus disease (i.e., COVID-19) could be classified as a pandemic due to its high contagion rate and the overall worldwide mobility. In an attempt to restrict and limit the spread of the disease, governments introduced restriction and confinement strategies that immediately affect peoples’ routines and usual activities [1]. Italy was one of the first European countries subjected to the pandemic state.

Learning latent low-dimensional vectors of network’s nodes is the central aim of GRL [1], and nowadays node embeddings are crucial in order to solve machine learning tasks on graphs. Usually they are computed with self-supervised training, using edge reconstruction as a pretext task [2], with the result of encoding node proximities into distances of a metric space.

Could data science help measure peacefulness and understand the factors that influence it? Could we anticipate the level of peacefulness before official sources publish their estimations?

I took the opportunity of the SoBigData++ Transnational Access to develop a collaboration with the CEU research unit directed by János Kertész in Vienna (Austria) on the program titled “Analysis of opinion dynamics over a realistic dynamic social network”. I was hosted for three weeks in the Department of Network and Data Science and I worked with the unit closely: we ended up with very interesting results that we are planning to summarize in a conference paper shortly.

For decades, researchers from different fields have been trying to understand how people form their opinions. With the rise of social media platforms, this quest has become even more significant, especially due to the emergence of some alarming and extreme phenomena, such as the polarization of opinions, online hating, etc. 

Soccer & Data Cup at Expo Dubai 2020 has been a 3-days international hybrid marathon of Sport Analytics combining fundamental techniques of data analysis and Artificial Intelligence. The event covers the subject area of Data Science, and aims to raise young people's awareness to the new frontiers of the complex analysis of digital data in the sport area.

With the technological advent of the last few decades, it is possible to record a huge quantity of data from athletes. Wearable devices, video analysis systems, tracking systems, and questionnaires are only a few examples of the devices used currently to record data in sports. These data can be used for scouting, performance analysis, and tactical analysis, but an increased interest is in assessing the risk of injuries.

As the volume of online content and discussions grows, the amount of misinformation grows with it. The most extreme type of misinformation (content created with malicious intent), which includes fabricated or manipulated data, can be automatically identified in certain domains (e.g., bot detection, image deep fake analysis) and is the target of extensive research.

Cryptocurrency market, after its surge in 2009, gained immense popularity among not only small-scale investors, but also large hedge funds. The ever increasing popularity of cryptocurrencies attracted professional investors who started constructing portfolios using cryptocurrencies, however, the vast majority of the market share still belongs to individuals.