Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections. Flamino, J., Galeazzi, A., Feldman, S., Macy, M. W., Cross, B., Zhou, Z., Serafino, M., Bovet, A., Makse, H. A., & Szymanski, B. K. Nature Human Behaviour, 7(6):904–916, March, 2023. ![Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Abstract Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.
@article{flaminoPoliticalPolarizationNews2023,
title = {Political polarization of news media and influencers on {Twitter} in the 2016 and 2020 {US} presidential elections},
volume = {7},
copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)},
issn = {2397-3374},
url = {https://www.nature.com/articles/s41562-023-01550-8},
doi = {10.1038/s41562-023-01550-8},
abstract = {Abstract
Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.},
language = {en},
number = {6},
urldate = {2023-10-22},
journal = {Nature Human Behaviour},
author = {Flamino, James and Galeazzi, Alessandro and Feldman, Stuart and Macy, Michael W. and Cross, Brendan and Zhou, Zhenkun and Serafino, Matteo and Bovet, Alexandre and Makse, Hernán A. and Szymanski, Boleslaw K.},
month = mar,
year = {2023},
keywords = {computational social science, disinformation, network science, polarization, social media},
pages = {904--916},
}
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