During the last two years we have been working with an large team of researchers on a follow up of my paper on fake news Twitter during the 2016 US presidential election. I am very happy to announce that the results have been published a few days ago in Nature Human Behaviour.
This time we not only studied the spread of fake news but also the changes and polarization of news media influencers between 2016 and 2020.
On the positive side, we measured a decrease in the number of tweets and users propagating fake and extremely biased news in 2020 compared to 2016, probably due to the measures put in place by Twitter to tackle such content. But we also revealed an increase in polarization, at the level of the top influencers and of the average users, in 2020, i.e. users were less likely to share information from other users with opposite political ideologies. This indicates increasing echo chambers for users with a lack of contrary views.
We also observed interesting changes in the top news influencers. Between 2016 and 2020, for influencers with center and right-leaning political ideologies, the number of influencers affiliated with media organizations (journalists and accounts belonging to news outlets) declined by 10%, replaced mostly by politicians. On the other hand, influencers spreading fake news, who were largely comprised of users not affiliated with political or media organizations in 2016, have been replaced in good part by new users affiliated with media organizations that emerged between 2016 and 2020. This change in the news media landscape on Twitter indicates a shift in the relative influence of journalists and political organizations as well as a professionalization of the disinformation industry.
Full list of authors and citation below:
James Flamino, Alessandro Galeazzi, Stuart Feldman, Michael W. Macy, Brendan Cross, Zhenkun Zhou, Matteo Serafino, Alexandre Bovet, Hernán A. Makse & Boleslaw K. Szymanski. Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections. Nat Hum Behav (2023). https://doi.org/10.1038/s41562-023-01550-8