We are a team of scientists from different fields (computer science, physics, engineering, and math). We apply complex systems modeling to data to study societal processes from information spreading to mobility up to epidemics. The story of our lab (Bottega) started in 2013 when we started collecting data points on Facebook jokes to mock users. We end up collecting tons of data about users’ behavior on misleading content. The first paper we published, Collective Narratives in the Age of Misinformation studying the virality of content, went viral.
A second study comparing how scientific information and conspiracy information get consumed showed that users tend to acquire information adhering to their system of beliefs, substantially ignoring the information’s truth value Science vs. Conspiracy: Collective Narratives in the Age of Misinformation. We got cited in major international newspapers as the scientists that trolled conspiracy theorists.
Then, to prove another aspect: how users respond to information dissenting their system of beliefs, we studied how followers of alternative information sources react to debunking/fact-checking posts. It was the paper Debunking in a World of Tribes. We found evidence that users simply ignore opposing viewpoints. If someone tries to force them, the reaction is to back-fire: they become more active in consuming alternative content after the fact-checking. The paper was impressively viral, and the results heavily impacted the public debate about information and social media. The famous column "what was fake on the on the internet this week" of the Washington Post closed citing our work.
At that point, we got all the information to provide a model for information spreading online. Users online can find the information they like the most, ignore dissenting information, join groups of like-minded peers (echo chambers) in which we cooperate to frame a shared narrative. We published this in PNAS in 2016 in the paper The spreading of misinformation online. The paper nowadays is considered one of the pioneering studies on misinformation dynamics.
After that, we explored how users consume news through social media. We found the same dynamics of selective exposure dominating the system. The paper was published in PNAS Anatomy of news consumption on Facebook. Misinformation proliferates when polarization is high. We provided a classification model to predict misinformation targets in the paper Fake News and Polarization: Early Warning of Potential Misinformation Targets.
In this period, we are exploring the diverse dynamics in different platforms, especially during COVID. We are studying how the language adapted to the new information ecosystem and how those processes affect our society. Our works made explicit that social media heavily changed the way we process information and shape our opinion. Even Facebook admitted that in this blog post.
From our work, we learned that there is a huge work to rebuild trust in Science. Keeping this in mind, we set up the Center of Data Science and Complexity for Society. We want to build a network to address important societal problems with an open and inclusive scientific approach. Science is for all of us.
Along this path, we are now actively organizing dissemination events, conferences, and courses to face the wonderful science to make data science useful for society. Stay tuned, and check our FAQ!