Mohsen Mosleh & David G. Rand
Nature Communications volume 13, Article number: 7144 (2022)

Misinformation can come directly from public figures and organizations (referred to here as “elites”). Here, we develop a tool for measuring Twitter users’ exposure to misinformation from elites based on the public figures and organizations they choose to follow. Using a database of professional fact-checks by PolitiFact, we calculate falsity scores for 816 elites based on the veracity of their statements. We then assign users an elite misinformation-exposure score based on the falsity scores of the elites they follow on Twitter. Users’ misinformation-exposure scores are negatively correlated with the quality of news they share themselves, and positively correlated with estimated conservative ideology. Additionally, we analyze the co-follower, co-share, and co-retweet networks of 5000 Twitter users and find an ideological asymmetry: estimated ideological extremity is associated with more misinformation exposure for users estimated to be conservative but not for users estimated to be liberal. Finally, we create an open-source R library and an Application Programming Interface (API) making our elite misinformation-exposure estimation tool openly available to the community. Misinformation online can be shared by major political figures and organizations. Here, the authors developed a method to measure exposure to information from these sources on Twitter, and show how exposure relates to the quality of the content people share and their political ideology.

Read the full article at: www.nature.com

Leto Peel, Tiago P. Peixoto & Manlio De Domenico 
Nature Communications volume 13, Article number: 6794 (2022)

The number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications.

Read the full article at: www.nature.com

Aguadé-Gorgorió, G.; Costa, J.; Solé, R. An Oncospace for Human Cancers. Preprints 2022, 2022110211

Human cancers comprise an heterogeneous array of diseases with different progression patterns and responses to therapy. However, they all develop within a host context that constraints their natural history. As it occurs with the diversity of organisms, one can conjecture that there is order in the cancer multiverse. Is there a way to capture the broad range of tumor types within a space of the possible? Here we define the oncospace, a coordinate system that integrates the ecological, evolutionary and developmental components of cancer complexity. The spatial position of a tumor results from its departure from the healthy tissue along these three axes, and progression trajectories inform about the components driving malignancy across cancer subtypes. We postulate that the oncospace topology encodes new information regarding tumorigenic pathways, subtype prognosis and therapeutic opportunities: treatment design could benefit from considering how to nudge tumors towards empty evolutionary deserts in the oncospace.

Read the full article at: www.preprints.org