Popular social media applications on smartphones (e.g. Facebook, Instagram, Twitter) enabled the creation of an unprecedented amount of user-generated content. Social media can be useful to extract valuable information concerning human dynamics and behaviors, such as mobility.  Popular events such as music festivals attract thousands of participants.

From June 1, 2022 to July 31, 2022, for two months, I had a privileged opportunity to visit the SoBigData++ team at Pisa, Italy, led by Prof. Fosca Gianotti at Scuola Normale Superiore (SNS) and Prof. Dino Pedreschi at University of Pisa. This was part of a program run by National Science Foundation (NSF), US that connects a PI in the US to a PI in the Europe with overlapping interests and partially supports the visit for research collaboration by paying airfares.

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.