ArticleNature human behaviour2023
Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections.
Article in Nature human behaviour, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Measuring narrative polarization in online discourse.PNAS nexus · 2026Article
- Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow.STAR protocols · 2026Article
- [Misinformation and disinformation about dietary supplements in social media: limiting the risks, seizing the opportunities, and shaping structures].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Article
- Ideology and polarization set the agenda on social media.Scientific reports · 2025Article
- Cognitive distortions are associated with increasing political polarization.Communications psychology · 2025Article
- The benefits and dangers of anthropomorphic conversational agents.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The role of social media ads for election outcomes: Evidence from the 2021 German election.PNAS nexus · 2025Article
- Unveiling the drivers of active participation in social media discourse.Scientific reports · 2025Article
- Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility.Nature communications · 2025Article
- Evaluating the effect of viral posts on social media engagement.Scientific reports · 2025Article
- Evaluating the relationship between news source sharing and political beliefs.Scientific reports · 2025Article
- Dynamics of temporal influence in polarised networks.PloS one · 2025Article
- Identifying and characterizing ideologically homogeneous clusters on Twitter and Parler during the 2020 election.PloS one · 2025Article
- Article
- Finding polarized communities and tracking information diffusion on Twitter: a network approach on the Irish Abortion Referendum.Royal Society open science · 2025Article
- Social media usage and cyberbullying: the moderating role of tie strength.Frontiers in psychology · 2025Article
- A multilevel network approach to revealing patterns of online political selective exposure.PloS one · 2025Article
- Co-evolution of groups and opinions in an agent-based model.PloS one · 2025Article
- Patterns of partisan toxicity and engagement reveal the common structure of online political communication across countries.Nature communications · 2024Article
- Partisan styles of self-presentation in U.S. Twitter bios.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.