Health

“Detecting adherence to the recommended childhood vaccination schedule from user-generated content in a US parenting forum” PLoS Comput Biol 17(4): e1008919. 2021.

Betti L, De Francisci Morales G, Gauvin L, Kalimeri K, Mejova Y, Paolotti D, Starnini M

The importance and effectiveness of vaccines is generally high, but concerns toward vaccination contribute to eroding confidence in vaccination. In this work, we create a Natural Language Processing pipeline to automatically identify parents who state their adherence to the recommended or alternative vaccination schedule on a popular parenting forum, BabyCenter US. We find that these users have distinct interests and different experiences with vaccination, although they frequently share similar sources of information. Differently from what is observed on most popular digital platforms like Facebook or Twitter, Babycenter users communicate between each other independently of the vaccination schedule they adopt. These observations suggest that parenting fora may be a more suitable medium to develop intervention aiming to influence positively the vaccination behavior of parents.

"Falling into the echo chamber: the Italian vaccination debate on Twitter" International AAAI Conference on Web and Social Media (ICWSM 2020)

Cossard A, De Francisci Morales G, Kalimeri K, Mejova Y, Paolotti D, Starnini M

The reappearance of measles in the US and Europe, a disease considered eliminated in early 2000s, has been accompanied by a growing debate on the merits of vaccination on social media. In this study we examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant. We focus on Italy, one of the countries most affected by the latest measles outbreaks. We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct “echo chambers”. The structure of these communities differs as well, with skeptics arranged in a tightly connected cluster, and advocates organizing themselves around few authoritative hubs. At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers (attaining 95% cross-validated accuracy). Insights of this study provide several avenues for potential future interventions, including network-guided targeting, accounting for the political context, and monitoring of alternative sources of information.

“Firsthand opiates abuse on social media: monitoring geospatial patterns of interest through a digital cohort” The World Wide Web Conference, 2019.

Balsamo D, Bajardi P, Panisson A

In the last decade drug overdose deaths reached staggering proportions in the US. Besides the raw yearly deaths count that is worrisome per se, an alarming picture comes from the steep acceleration of such rate that increased by 21% from 2015 to 2016. While traditional public health surveillance suffers from its own biases and limitations, digital epidemiology offers a new lens to extract signals from Web and Social Media that might be complementary to official statistics. In this study we present a computational approach to identify a digital cohort that might provide an updated and complementary view on the opioid crisis. We introduce an information retrieval algorithm suitable to identify relevant subspaces of discussion on social media, for mining data from users showing explicit interest in discussions about opioid consumption in Reddit. A measure of prevalence of interest in opiate consumption has been estimated at the state level, producing a novel indicator with information that is not entirely encoded in the standard surveillance.

“Global misinformation in online vaccination debate before and during COVID-19”. 2022

Lenti J, Mejova Y, Kalimeri K, Panisson A, Paolotti D, Tizzani M, Starnini M

While previous studies focused on specific countries, the COVID-10 pandemic brought the vaccination discourse worldwide, underpinning the need to tackle low-credible information flows on a global scale to design effective countermeasures. Here, we leverage a Twitter dataset in 18 languages to quantify misinformation flows between users exposed to anti-vaccination content. We find that, during the pandemic, no-vax communities became more central in the country-specific debates and their cross-border connections strengthened, revealing a global Twitter anti-vaccination network.