Community Notes undermoderate polarizing content by design creating risks in electoral processes

TL;DR

This work analyzes notes relating to four recent elections in the US, UK, France, Germany and Germany and demonstrates that they are systematically under-moderated when compared to other notes, posing potential risks to civic discourse and electoral processes.

Abstract

Community Notes (CNs) of X enables users to collaboratively moderate misleading content. To resolve conflicting moderation, CNs infers a latent ideological dimension and selects notes garnering cross-partisan support. As this system is now deployed worldwide, we evaluate its operation across diverse polarization contexts. We analyze all 1.9 million moderation notes receiving 135 million ratings by March 2025, cross-referencing ideological scaling data on 13 countries. Our results show that the CNs algorithm effectively captures the main polarizing dimensions across countries, surfacing notes that garner cross-partisan support. This also means that, by design, CNs systematically under-moderate polarizing content. We analyze notes relating to four recent elections in the US (2024), the UK (2024), France (2024) and Germany (2025) and demonstrate that they are systematically under-moderated when compared to other notes, posing potential risks to civic discourse and electoral processes.

Figures (36)

• Figure 1: Community Notes usage and outcomes. (A) Fraction of Community Notes proposed per country. To be associated with a country, a note must be predominantly evaluated by contributors who typically rate notes in that country's language and that reference national websites (see SM.S\ref{['subsubsec:country_split_methods']}). (B) Cumulative distribution of time needed for a proposed note to reach Helpful Status via X's algorithm, measured from the publication of the original post and from the publication of the note (truncated tails after 36 hours, representing 9.2% and 17.9% of notes, respectively). (C) Fraction of notes reaching Helpful Status in the US (left) and in the US when referencing expert fact-checks (right), segmented by the ideological leaning of note authors. Error bars show 95% CI via bootstrapping over notes and fact-checking organizations (see SM.S\ref{['paragraph:fact_checks']}).
• Figure 2: Fraction of notes reaching Helpful Status per country during elections. The analysis compares election-related notes with the overall corpus (in the same period, ranging from one month before to one week after the election) during the 2024 United States presidential, 2024 United Kingdom general, 2024 France legislative, and 2025 Germany federal elections. Error bars represent 95% confidence intervals, estimated by bootstrapping over the keywords used to identify election-related notes.
• Figure 3: Distribution of Left- and Right-leaning X accounts that authored posts for which (A) Community Notes were requested by users; (B) Community Notes were proposed by contributors; and (C) Community Notes reached Helpful Status. Statistics computed over non-deleted posts authored by accounts with an inferred ideological leaning (see SM.S\ref{['subsec:epo_dataset']}).
• Figure 4: (A to C) Distribution of Left-Right leaning of X accounts (A) segmented by the sign of the community note latent ideology $\theta_n$, (B) rated helpful/not-helpful by raters with negative latent ideology $\theta_r<0$, (C) rated helpful/not-helpful by raters with positive latent ideology $\theta_r>0$. (D to G) X accounts positioned in the (Left-Right; Anti-Elite) 2D plane, segmented by the majority sign of the latent ideology $\theta_n$ of their associated Community Notes (red for $\theta_n<0$ and blue for $\theta_n>0$). The direction that best separates notes by majority outcome, learned through logistic regression, is displayed as $CN$ along with the corresponding AUC score for sign prediction based on account positions in the 2D plane (standard deviation from 10-fold cross-validation). The direction structuring each country's political landscape $\delta_1$ is projected onto the plane.
• Figure S1: (A) An example of a Community Note flagging a post as "misinformed or potentially misleading", prompting contributors to rate its "helpfulness". (B) An example of a Community Note that has reached Helpful Status and is displayed beneath the original post on X.