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Riccardo Gallotti

@ricgallotti.bsky.social

Interdisciplinary physicist Head of the CHuB research unit at FBK Human behaviour, mobility, transport, decision making, infodemics, data science

9 Posts661 Follower193 Folgt
Lucila Alvarez Zuzek@luzuzek.bsky.social · 11mo ago

Our study of 400 million tweets shows that the “war on science” is far more complex than a simple clash between truth and falsehood. 🔍 read more in our latest article magazine.fbk.eu/en/news/what... @ricgallotti.bsky.social @chub-fbk.bsky.social @vtraag.bsky.social

What a 400-Million-Tweet study revealed about the “War on Science”

The European project “Understanding Misinformation and Science in Societal Debates” (UnMiSSeD) aims at understanding the overlap between science and misinformation with a particular focus on the COVID...

magazine.fbk.eu

Lucila Alvarez Zuzek@luzuzek.bsky.social · 12mo ago

🚨 Finally our paper "Predicting human cooperation: sensitizing drift-diffusion model to interaction and external stimuli" is out 🤩 doi.org/10.1098/rsif... Laura Ferrarotti, Bruno Lepri and @ricgallotti.bsky.social @mobs-fbk.bsky.social @chub-fbk.bsky.social

Predicting human cooperation: sensitizing drift-diffusion model to interaction and external stimuli | Journal of The Royal Society Interface

Human cooperation arises naturally and is essential for the development of successful societies. This study aims to identify which aspects of the interaction influence societal cooperation and defection. Specifically, we investigate human cooperation ...

doi.org

Ulysse Marquis@ulyssemarquis.bsky.social · last yr.

New pre-print out ! Work led by @eleandre.bsky.social, in collaboration with M. Napolitano and @ricgallotti.bsky.social. Using Foursquare data from Bologna, we find that the distribution of POIs follows a clear power-law pattern at the city scale. To explain this, we introduce a framework where ...

physics.soc-ph@arxiv-soc-ph.bsky.social · last yr.

Scale-free Points-of-Interest Distribution in a City Emerging from Homogeneous Poissonian-point Processes https://arxiv.org/abs/2509.01699

Matteo Scianna@matteoscianna.bsky.social · last yr.

📣🚨 New work out on @plos.org Computational Biology! Excited and proud to have provided the world with more maps of Puglia🕷️ Thank you all @luzuzek.bsky.social Oriol Artime @ricgallotti.bsky.social @mtizzoni.bsky.social journals.plos.org/ploscompbiol... 💊main results below👇

Comparing the effectiveness of ring and block-vaccination strategies on networks

Author summary Infectious diseases such as measles and Ebola have shown how quickly outbreaks can spread and how essential vaccination is to protect individuals. In our work, we investigate a spectrum...

journals.plos.org

Vincent Traag@vtraag.bsky.social · last yr.

This research was done by @luzuzek.bsky.social zek.bsky.social, Juan Pablo Bascur, @annabertani.bsky.social, and @ricgallotti.bsky.social. This project is supported by European Media and Information Fund. ⤴️

Vincent Traag@vtraag.bsky.social · last yr.

New preprint! 🚨 We study the interaction between misinformation and science on Twitter during COVID-19 based on ~407M tweets. Both science and misinformation featured prominently during the pandemic, but the interaction between the two has not been studied on this scale before. 🧵 (1/10)

Mapping the interaction between science and misinformation in COVID-19 tweets. Publication from @luzuzek.bsky.social, Juan Pablo Bascur, @annabertani.bsky.social, @ricgallotti.bsky.social. This project is supported by European Media and Information Fund. Abstract: During the COVID-19 pandemic, scientific understanding related to the topic evolved rapidly. Along with scientific information being discussed widely, a large circulation of false information, labelled an infodemic by the WHO, emerged. Here, we study the interaction between misinformation and science on Twitter (now X) during the COVID-19 pandemic. We built a comprehensive database of 407M COVID-19 related tweets and classified the reliability of URLs in the tweets based on Media Bias/Fact Check. In addition, we use Altmetric data to see whether a tweet refers to a scientific publication. We find that many users find that many users share both scientific and unreliable content; out of the 1.2M users who share science, also share unreliable content. Publications that are more frequently shared by users who also share unreliable content are more likely to be preprints, slightly more often retracted, have fewer citations, and are published in lower-impact journals on average. Our findings suggest that misinformation is not related to a ``deficit'' of science. In addition, our findings raise some critical questions about certain open science practices and their potential for misuse. Given the fundamental opposition between science and misinformation, our findings highlight the necessity for proactive scientific engagement on social media platforms to counter false narratives during global crises.
Lucila Alvarez Zuzek@luzuzek.bsky.social · last yr.

So excited to see this come together! 🎉 Our latest study explores the interplay between science and misinformation in public debates during COVID-19 🔍 arxiv.org/abs/2507.01481 👇Take a look

Mapping the interaction between science and misinformation in COVID-19 tweets

During the COVID-19 pandemic, scientific understanding related to the topic evolved rapidly. Along with scientific information being discussed widely, a large circulation of false information, labelle...

arxiv.org

Vincent Traag@vtraag.bsky.social · last yr.

New preprint! 🚨 We study the interaction between misinformation and science on Twitter during COVID-19 based on ~407M tweets. Both science and misinformation featured prominently during the pandemic, but the interaction between the two has not been studied on this scale before. 🧵 (1/10)

Mapping the interaction between science and misinformation in COVID-19 tweets. Publication from @luzuzek.bsky.social, Juan Pablo Bascur, @annabertani.bsky.social, @ricgallotti.bsky.social. This project is supported by European Media and Information Fund. Abstract: During the COVID-19 pandemic, scientific understanding related to the topic evolved rapidly. Along with scientific information being discussed widely, a large circulation of false information, labelled an infodemic by the WHO, emerged. Here, we study the interaction between misinformation and science on Twitter (now X) during the COVID-19 pandemic. We built a comprehensive database of 407M COVID-19 related tweets and classified the reliability of URLs in the tweets based on Media Bias/Fact Check. In addition, we use Altmetric data to see whether a tweet refers to a scientific publication. We find that many users find that many users share both scientific and unreliable content; out of the 1.2M users who share science, also share unreliable content. Publications that are more frequently shared by users who also share unreliable content are more likely to be preprints, slightly more often retracted, have fewer citations, and are published in lower-impact journals on average. Our findings suggest that misinformation is not related to a ``deficit'' of science. In addition, our findings raise some critical questions about certain open science practices and their potential for misuse. Given the fundamental opposition between science and misinformation, our findings highlight the necessity for proactive scientific engagement on social media platforms to counter false narratives during global crises.
Vincent Traag@vtraag.bsky.social · last yr.

New preprint! 🚨 We study the interaction between misinformation and science on Twitter during COVID-19 based on ~407M tweets. Both science and misinformation featured prominently during the pandemic, but the interaction between the two has not been studied on this scale before. 🧵 (1/10)

Mapping the interaction between science and misinformation in COVID-19 tweets. Publication from @luzuzek.bsky.social, Juan Pablo Bascur, @annabertani.bsky.social, @ricgallotti.bsky.social. This project is supported by European Media and Information Fund. Abstract: During the COVID-19 pandemic, scientific understanding related to the topic evolved rapidly. Along with scientific information being discussed widely, a large circulation of false information, labelled an infodemic by the WHO, emerged. Here, we study the interaction between misinformation and science on Twitter (now X) during the COVID-19 pandemic. We built a comprehensive database of 407M COVID-19 related tweets and classified the reliability of URLs in the tweets based on Media Bias/Fact Check. In addition, we use Altmetric data to see whether a tweet refers to a scientific publication. We find that many users find that many users share both scientific and unreliable content; out of the 1.2M users who share science, also share unreliable content. Publications that are more frequently shared by users who also share unreliable content are more likely to be preprints, slightly more often retracted, have fewer citations, and are published in lower-impact journals on average. Our findings suggest that misinformation is not related to a ``deficit'' of science. In addition, our findings raise some critical questions about certain open science practices and their potential for misuse. Given the fundamental opposition between science and misinformation, our findings highlight the necessity for proactive scientific engagement on social media platforms to counter false narratives during global crises.
Riccardo Gallotti@ricgallotti.bsky.social · last yr.

I finally have the pleasure of attending Sunbelt in person for the first time, together with four members of my unit @chub-fbk.bsky.social This year among the organisers we also have many familiar faces such as @camcom.bsky.social , @chavalarias.org , Floriana Gargiulo and Emmanuel Lazega.

Complex Human Behaviour Lab@chub-fbk.bsky.social · last yr.

🎙️Busy morning today at @sunbelt2025paris.bsky.social for CHuB talking about mis/disinformation on Twitter and Mastodon @ricgallotti.bsky.social @kaveh-kadkhoda.bsky.social @annabertani.bsky.social

Ulysse Marquis@ulyssemarquis.bsky.social · last yr.

How do cities expand? Using surface growth physics, we found a unique exponent governing their local geometry. Instead, their dynamics range from smooth diffusion to abrupt coalescence, with demographic pressure driving where each city lands on that spectrum. arxiv.org/abs/2506.10656

Universal roughness and the dynamics of urban expansion

We present a new approach to quantify urban sprawl using tools from surface growth physics. Analyzing built-up area expansion in 19 cities (1985-2015), we uncover anisotropic growth with branch-like e...

arxiv.org