Like-minded sources on Facebook are prevalent but not polarizing

Many critics raise concerns about the prevalence of ‘echo chambers’ on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from ‘like-minded’ sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.


Article
social media.Prior observational studies of information exposure on platforms focus on Twitter, which is used by only 23% of the public [19][20][21][22] , or the news diet of the small minority of active adult users in the US who self-identified as conservative or liberal on Facebook in 2014-2015 23 .Without access to behavioural measures of exposure, studies must instead rely on survey self-reports that are prone to measurement error 24,25 .
Second, although surveys find associations between holding polarized attitudes and reported consumption of like-minded news 26,27 , few studies provide causal evidence that consuming like-minded content leads to lasting polarization.These observed correlations may be spurious given that the people with extreme political views are more likely to consume like-minded content 28,29 .In addition, although like-minded information can polarize [30][31][32] , most experimental tests of theories about potential echo chamber effects are brief and use simulated content, making it difficult to know whether these findings generalize to real-world environments.Previous experimen tal work also raises questions about whether such polarizing effects are common 18,33 , how quickly they might decay 18,33 , and whether they are concentrated among people who avoid news and political content 28 .
Finally, reducing exposure to like-minded content may not lead to a corresponding increase in exposure to content from sources with different political leanings (which we refer to as cross-cutting) and could also have unintended consequences.Social media feeds are typically limited to content from accounts that users already follow, which include few that are cross-cutting and many that are non-political 22 .As a result, reducing exposure to like-minded sources may increase the prevalence of content from sources that are politically neutral rather than uncongenial.Furthermore, if content from like-minded sources is systematically different (such as in its tone or topic), reducing exposure to such content may also have other effects on the composition of social media feeds.Reducing exposure to like-minded content could also induce people to seek out such information elsewhere online (that is, not on Facebook 34 ).
In this study, we measure the prevalence of exposure to content from politically like-minded sources among active adult Facebook users in the US.We then report the results of an experiment estimating the effects of reducing exposure to content from politically like-minded friends, Pages and groups among consenting Facebook users (n = 23,377) for three months (24 September to 23 December 2020).By combining on-platform behavioural data from Facebook with survey measures of attitudes collected before and after the 2020 US presidential election, we can determine how reducing exposure to content from like-minded sources changes the information people see and engage with on the platform, as well as test the effects over time of reducing exposure to these sources on users' beliefs and attitudes.
This project is part of the US 2020 Facebook and Instagram Election Study.Although both Meta researchers and academics were part of the research team, the lead academic authors had final say on the analysis plan, collaborated with Meta researchers on the code implementing the analysis plan, and had control rights over data analysis decisions and the manuscript text.Under the terms of the collaboration, Meta could not block any results from being published.The academics were not financially compensated and the analysis plan was preregistered prior to data availability (https://osf.io/3sjy2);further details are provided in Supplementary Information, section 4.8.
We report several key results.First, the majority of the content that active adult Facebook users in the US see comes from like-minded friends, Pages and groups, although only small fractions of this content are categorized as news or are explicitly about politics.Second, we find that an experimental intervention reducing exposure to content from like-minded sources by about a third reduces total engagement with that content and decreases exposure to content classified as uncivil and content from sources that repeatedly post misinformation.However, the intervention only modestly increases exposure to content from cross-cutting sources.We instead observe a greater increase in exposure to content from sources that are neither like-minded nor cross-cutting.Moreover, although total engagement with content from like-minded sources decreased, the rate of engagement with it increased (that is, the probability of engaging with the content from like-minded sources that participants did see was higher).
Furthermore, despite reducing exposure to content from like-minded sources by approximately one-third over a period of weeks, we find no measurable effects on 8 preregistered attitudinal measures, such as ideological extremity and consistency, party-congenial attitudes and evaluations, and affective polarization.We can confidently rule out effects of ±0.12 s.d. or more on each of these outcomes.These precisely estimated effects do not vary significantly by respondents' political ideology (direction or extremity), political sophistication, digital literacy or pre-treatment exposure to content that is political or from like-minded sources.

Exposure to like-minded sources
Our analysis of platform exposure and behaviour considers the population of US adult Facebook users (aged 18 years and over).We focus primarily on those who use the platform at least once per month, who we call monthly active users.Aggregated usage levels are measured for the subset of US adults who accessed Facebook at least once in the 30 days preceding 17 August 2020 (see Supplementary Information, section 4.9.4 for details).During the third and fourth quarters of 2020, which encompass this interval as well as the study period for the experiment reported below, 231 million users accessed Facebook every month in the USA.
We used an internal Facebook classifier to estimate the political leaning of US adult Facebook users (see Supplementary Information, section 2.1 for validation and section 1.3 for classifier details; Extended Data Fig. 1 shows the distribution of predicted ideology score by self-reported ideology, party identification and approval of former president Donald Trump).The classifier produces predictions at the user level ranging from 0 (left-leaning) to 1 (right-leaning).Users with predicted values greater than 0.5 were classified as conservative and otherwise classified as liberal, enabling us to analyse the full population of US active adult Facebook users.A Page's score is the mean score of the users who follow the Page and/or share its content; a group's score is the mean score of group members and/or users who share its content.We classified friends, Pages or groups as liberal if their predicted value was 0.4 or below and conservative if it was 0.6 or above.This approach allows us to identify sources that are clearly like-minded or cross-cutting with respect to users (friends, Pages and groups with values between 0.4 and 0.6 were treated as neither like-minded nor cross-cutting).
We begin by assessing the extent to which US Facebook users are exposed to content from politically like-minded users, Pages and groups in their Feed during the period 26 June to 23 September 2020 (see Supplementary Information, section 4.2, for measurement details).We present estimates of these quantities among US adults who logged onto Facebook at least once in the 30 days preceding 17 August 2020.
We find that the median Facebook user received a majority of their content from like-minded sources-50.4% versus 14.7% from cross-cutting sources (the remainder are from friends, Pages and groups that we classify as neither like-minded nor cross-cutting).Like-minded exposure was similar for content classified as 'civic' (that is, political) or news (see Supplementary Information, section 4.3 for details on the classifiers used in this study).The median user received 55% of their exposures to civic content and 47% of their exposures to news content from like-minded sources (see Extended Data Table 1 for exact numbers and Supplementary Fig. 3 for a comparison with our experimental participants).Civic and news content make up a relatively small share of what people see on Facebook, however (medians of 6.9% and 6.7%, respectively; Supplementary Table 11).
However, patterns of exposure can vary substantially between users.Figure 1 provides the distribution of exposure to sources that were like-minded, cross-cutting or neither for all content, civic content and news content for Facebook users.
Despite the prevalence of like-minded sources in what people see on Facebook, extreme echo chamber patterns of exposure are infrequent.Just 20.6% of Facebook users get over 75% of their exposures from like-minded sources.Another 30.6% get 50-75% of their exposures on Facebook from like-minded sources.Finally, 25.6% get 25-50% of their exposures from like-minded sources and 23.1% get 0-25% of their exposures from like-minded sources.These proportions are similar for the subsets of civic and news content (Extended Data Table 1).For instance, like-minded sources are responsible for more than 75% of exposures to these types of content for 29% and 20.6% of users, respectively.
However, exposure to content from cross-cutting sources is also relatively rare among Facebook users.Only 32.2% have a quarter or more of their Facebook Feed exposures coming from cross-cutting sources (31.7% and 26.9%, respectively, for civic and news content).
These patterns of exposure are similar for the most active Facebook users, a group that might be expected to consume content from congenial sources more frequently than other groups.Among US adults who used Facebook at least once each day in the 30 days preceding 17 August 2020, 53% of viewed content was from like-minded sources versus 14% for cross-cutting sources, but only 21.1% received more than 75% of their exposures from like-minded sources (see Extended Data Fig. 2 and Extended Data Table 2).
These results are not consistent with the worst fears about echo chambers.Even among those who are most active on the platform, only a minority of Facebook users are exposed to very high levels of content from like-minded sources.However, the data clearly indicate that Facebook users are much more likely to see content from like-minded sources than they are to see content from cross-cutting sources.

Experiment reducing like-minded source exposure
To examine the effects of reducing exposure to information from like-minded sources, we conducted a field experiment among consenting US adult Facebook users.This study combines data on participant for all content, content classified as civic (that is, political) and news.b, Cumulative distribution functions of exposure levels by source type.Source and content classifications were created using internal Facebook classifiers (Supplementary Information, section 1.3).

Article
behaviour on Facebook with their responses to a multi-wave survey, a design that allows us to estimate the effects of the treatment on the information that participants saw, their on-platform behaviour and their political attitudes (Methods).Participants in the treatment and control groups were invited to complete five surveys before and after the 2020 presidential election assessing their political attitudes and behaviours.Two surveys were fielded pre-treatment: wave 1 (31 August to 12 September) and wave 2 (8 September to 23 September).The treatment ran from 24 September to 23 December.During the treatment period, 3 more surveys were administered: wave 3 (9 October to 23 October), wave 4 (4 November to 18 November) and wave 5 (9 December to 23 December).All covariates were measured in waves 1 and 2 and all survey outcomes were measured after the election while treatment was still ongoing (that is, in waves 4 and/or 5).Throughout the experiment, we also collected data on participant content exposure and engagement on Facebook.
In total, the sample for this study consists of 23,377 US-based adult Facebook users who were recruited via survey invitations placed at the top of their Facebook feeds in August and September 2020, provided informed consent to participate and completed at least one post-election survey wave (see Supplementary Information, sections 4.5 and 4.9).
For participants assigned to treatment, we downranked all content (including, but not limited to, civic and news content) from friends, groups and Pages that were predicted to share the participant's political leaning (for example, all content from conservative friends and groups and Pages with conservative audiences was downranked for participants classified as conservative; see Supplementary Information, section 1.1).
We note three important features of the design of the intervention.First, the sole objective of the intervention was to reduce exposure to content from like-minded sources.It was not designed to directly alter any other aspect of the participants' feeds.Content from likeminded sources was downranked using the largest possible demotion strength that a pre-test demonstrated would reduce exposure without making the Feed nearly empty for some users, which would have interfered with usability and thus confounded our results; see Supplementary Information, section 1.1.Second, our treatment limited exposure to all content from like-minded sources, not just news and political information.Because social media platforms blur social and political identities, even content that is not explicitly about politics can still communicate relevant cues 14,35 .Also, because politics and news account for a small fraction of people's online information diets 18,36,37 , restricting the intervention to political and/or news content would yield minimal changes to some people's Feeds.Third, given the associations between polarized attitudes and exposure to politically congenial content that have been found in prior research, we deliberately designed an intervention that reduces rather than increases exposure to content from like-minded sources to minimize ethical concerns.

Treatment effects on content exposure
The observed effects of the treatment on exposure to content from like-minded sources among participants are plotted in Fig. 2. As intended, the treatment substantially reduced exposure to content from like-minded sources relative to the pre-treatment period.During the treatment period of 24 September to 23 December 2020, average exposure to content from like-minded sources declined to 36.2% in the treatment group while remaining stable at 53.7% in the control group (P < 0.01).Exposure levels were relatively stable during the treatment period in both groups, except for a brief increase in treatment group exposure to content from like-minded sources on 2 November and 3 November, owing to a technical problem in the production servers that implemented the treatment (see Supplementary Information, section 4.11 for details).
Our core findings are visualized in Fig. 3, which shows the effects of the treatment on exposure to different types of content during the treatment period (Fig. 3a), the total number of actions engaging with that content (Fig. 3b), the rate of engagement with content conditional on exposure to it (Fig. 3c), and survey measures of post-election attitudes (Fig. 3d; Extended Data Table 3 reports the corresponding point estimates from Fig. 3; Supplementary Information, section 1. 4

provides measurement details).
As seen in Fig. 3a, the reduction in exposure to content from like-minded sources from 53.7% to 36.2% represents a difference of 0.77 s.d.(95% confidence interval: −0.80, −0.75).Total views per day also declined by 0.05 s.d.among treated participants (95% confidence interval: −0.08, −0.02).In substantive terms, the average control group participant had 267 total content views on a typical day, of which 143 were from like-minded sources.By comparison, 92 out of 255 total content views for an average participant in the treatment condition were from like-minded sources on a typical day (Supplementary Tables 33  and 40).
This reduction in exposure to information from like-minded sources, however, did not lead to a symmetrical increase in exposure to information from cross-cutting sources, which increased from 20 group saw a greater relative increase in exposure to content from sources classified as neither like-minded nor cross-cutting.Exposure to content from these sources increased from 25.6% to 35.9%, a change of 0.68 s.d.(95% confidence interval: 0.65, 0.71).Figure 3a also indicates that reducing exposure to content from like-minded sources reduced exposure to content classified as containing one or more slur words by 0.04 s.d.(95% confidence interval: −0.06, −0.02), content classified as uncivil by 0.15 s.d.(95% confidence interval: −0.18, −0.13), and content from misinformation repeat offenders (sources identified by Facebook as repeatedly posting misinformation) by 0.10 s.d.(95% confidence interval: −0.13, −0.08).Substantively, the average proportion of exposures decreased from 0.034% to 0.030% for content with slur words (a reduction of 0.01 views per day on average), from 3.15% to 2.81% for uncivil content (a reduction of 1.24 views per day on average), and from 0.76% to 0.55% for content from misinformation repeat offenders (a reduction of 0.62 views per day on average).Finally, the treatment reduced exposure to civic content (−0.05 s.d.; 95% confidence interval: −0.08, −0.03) and increased exposure to news content (0.05 s.d., 95% confidence interval: 0.02, 0.07) (see Supplementary Information, section 1.3 for details on how uncivil content, content with slur words and misinformation repeat offenders are measured).

Treatment effects on content engagement
We next consider the effects of the treatment (reducing exposure to content from like-minded sources) on how participants engage with content on Facebook.We examine content engagement in two ways, which we call 'total engagement' and 'engagement rate'.Figure 3b presents the effects of the treatment on total engagement with contentthe total number of actions taken that we define as 'passive' (clicks, reactions and likes) or 'active' (comments and reshares) forms of engagement.Figure 3c presents effects of the treatment on the engagement rate, which is the probability of engaging with the content that participants did see (that is, engagement conditional on exposure).These two measures do not necessarily move in tandem: as we report below, participants in the treatment group have less total engagement with content from like-minded sources (since they are by design seeing much less of it), but their rate of engagement is higher than that of the control group, indicating that they interacted more frequently with the content from like-minded sources to which they were exposed.
When people in the treatment group did see content from like-minded sources in their Feed, however, their rate of engagement was higher than in the control group.Figure 3c shows that, conditional on exposure, passive and active engagement with content from like-minded sources increased by 0.04 s.d.(95% confidence interval: 0.02, 0.06) and 0.13 s.d.(95% confidence interval: 0.08, 0.17), respectively.Furthermore, although treated participants saw more content from cross-cutting sources overall, they were less likely to engage with the content that they did see: passive engagement decreased by 0.06 s.d.(95% confidence interval: −0.07, −0.04) and active engagement decreased by 0.02 s.d.

Treatment effects on attitudes
Finally, we examine the causal effects of reducing exposure to like-minded sources on Facebook on a range of attitudinal outcomes measured in post-election surveys (Fig. 3d).As preregistered, we apply survey weights to estimate PATEs and adjust P values for these outcomes to control the false discovery rate (see Supplementary Information, sections 1.5.4 and 4.7 for details).We observe a consistent pattern of precisely estimated results near zero (open circles in Fig. 3d) for the outcome measures we examine: affective polarization; ideological extremity; ideologically consistent issue positions, group evaluations and vote choice and candidate evaluations; and partisan-congenial beliefs and views about election misconduct and outcomes, views toward the electoral system and respect for election norms (see Supplementary Information, section 1.4 for measurement details).In total, we find that 7 out of the 8 point estimates for our primary outcome measures have values of ±0.03 s.d. or less and are precisely estimated (exploratory equivalence bounds: ±0.1 s.d.; Supplementary Table 60), reflecting high levels of observed power.For instance, the minimum detectable effect in the sample for affective polarization is 0.019 s.d.The eighth result is a less precise null for ideologically consistent vote choice and candidate evaluations (0.056 s.d., equivalence bounds: 0.001, 0.111.) We also tested the effects of reducing exposure to content from like-minded sources on a variety of attitudinal measures for which we had weaker expectations.Using an exploratory equivalence bounds test, we can again confidently rule out effects of ±0.18 s.d. for these preregistered research questions across 18 outcomes, which are reported in Extended Data Fig. 5 and Supplementary Table 47.An exploratory equivalence bounds analysis also rules out a change in self-reported consumption of media outlets outside of Facebook that we categorized as like-minded of ±0.07 s.d.(Supplementary Tables 59 and 67).
Finally, we examine heterogeneous treatment effects on the attitudes reported in Fig. 3d and the research questions across a number of preregistered characteristics: respondents' political ideology (direction or extremity), political sophistication, digital literacy, pre-treatment exposure to content that is political, and pre-treatment levels of like-minded exposure both as a proportion of respondents' information diet and as the total number of exposures (see Supplementary Information, section 3.9).None of the 272 preregistered subgroup treatment effect estimates for our primary outcomes are statistically significant after adjustment to control the false discovery rate.Similarly, an exploratory analysis finds no evidence of heterogeneous effects by age or number of years since joining Facebook (see Supplementary Information, section 3.9.5).

Discussion
Many observers share the view that Americans live in online echo chambers that polarize opinions on policy and deepen political divides 6,7 .Some also argue that social media platforms can and should address this problem by reducing exposure to politically like-minded content 38 .However, both these concerns and the proposed remedy are based on largely untested empirical assumptions.
Here we provide systematic descriptive evidence of the extent to which social media users disproportionately consume content from politically congenial sources.We find that only a small proportion of the content that Facebook users see explicitly concerns politics or news and relatively few users have extremely high levels of exposure to like-minded sources.However, a majority of the content that active adult Facebook users in the US see on the platform comes from politically like-minded friends or from Pages or groups with like-minded audiences (mirroring patterns of homophily in real-world networks 15,39 ).

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This content has the potential to reinforce partisan identity even if it is not explicitly political 14 .
Our field experiment also shows that changes to social media algorithms can have marked effects on the content that users see.The intervention substantially reduced exposure to content from like-minded sources, which also had the effect of reducing exposure to content classified as uncivil and content from sources that repeatedly post misinformation.However, the tested changes to social media algorithms cannot fully counteract users' proclivity to seek out and engage with congenial information.Participants in the treatment group were exposed to less content from like-minded sources but were actually more likely to engage with such content when they encountered it.
Finally, we found that reducing exposure to content from like-minded sources on Facebook had no measurable effect on a range of political attitudes, including affective polarization, ideological extremity and opinions on issues; our exploratory equivalence bounds analyses allow us to confidently rule out effects of ±0.12 s.d.We were also unable to reject the null hypothesis in any of our tests for heterogeneous treatment effects across many distinct subgroups of participants.
There are several potential explanations for this pattern of null results.First, congenial political information and partisan news-the types of content that are thought to drive polarization-account for a fraction of what people see on Facebook.Similarly, social media consumption represents a small fraction of most people's information diets 37 , which include information from many sources (for example, friends, television and so on).Thus, even large shifts in exposure on Facebook may be small as a share of all the information people consume.Second, persuasion is simply difficult-the effects of information on beliefs and opinion are often small and temporary and may be especially difficult to change during a contentious presidential election 33,[40][41][42][43] .Finally, we sought to decrease rather than increase exposure to like-minded information for ethical reasons.Although the results suggest that decreasing exposure to information from like-minded sources has minimal effects on attitudes, the effects of such exposure may not be symmetrical.Specifically, decreasing exposure to like-minded sources might not reduce polarization as much as increasing exposure would exacerbate it.
We note several other areas for future research.First, we cannot rule out the many ways in which social media use may have affected participants' beliefs and attitudes prior to the experiment.In particular, our design cannot capture the effects of prior Facebook use or cumulative effects over years; experiments conducted over longer periods and/or among new users are needed (we note, however, that find no evidence of heterogeneous effects by age or years since joining Facebook).Second, although heterogeneous treatment effects are non-existent in our data and rare in persuasion studies in general 44 , the sample's characteristics and behaviour deviate in some respects from the Facebook user population.Future research should examine samples that more closely reflect Facebook users and/or oversample subgroups that may be particularly affected by like-minded content.Third, only a minority of Facebook users occupy echo chambers yet the reach of the platform means that the group in question is large in absolute terms.Future research should seek to better understand why some people are exposed to large quantities of like-minded information and the consequences of this exposure.Fourth, our study examines the prevalence of echo chambers using the estimated political leanings of users, Pages, and groups who share content on social networks.We do not directly measure the slant of the content that is shared; doing so would be a valuable contribution for future research.Finally, replications in other countries with different political systems and information environments will be essential to determine how these results generalize.
Ultimately, these findings challenge popular narratives blaming social media echo chambers for the problems of contemporary American democracy.Algorithmic changes that decrease exposure to like-minded sources do not seem to offer a simple solution for those problems.The information that we see on social media may be more a reflection of our identity than a source of the views that we express.

Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-023-06297-w.within 2-4 weeks of submission.To access the data, the home institution of the academic making the request must complete ICPSR's Restricted Data Agreement.Source data are provided with this paper.The denominator for these percentages is all respondents or all monthly active users.

Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.

A description of all covariates tested
A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g.means) or other basic estimates (e.g.regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g.confidence intervals) For null hypothesis testing, the test statistic (e.g.F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.
For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g.Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection Data collection was carried out by Meta and NORC, an independent survey research organization at the University of Chicago.Meta recruited most participants and collected on-platform data.NORC carried out all surveys associated with the project, recruited additional survey panelists, collected all supplemental data outside of the Facebook/lnstagram on-platform data, and removed any direct identifiers before linking to the survey data and sharing with the research team.
On-platform behavioral data were collected via Meta's internal systems for logging user behavior.Survey data were collected by NORC using their existing survey infrastructure.To collect the passive measurement data, NORC partnered with two vendors: MDI Global and RealityMine.
Users who consented to passive data tracking were asked to install an app and use a virtual private network (VPN) on their mobile or desktop devices to collect data about the number of visits and time spent on different web domains as well as usage and time spent on apps on their mobile device.The app was developed by MDI Global and the VPN was developed and maintained by RealityMine.Both firms collected the passive tracking data and sanitized, truncated, and/or categorized the URLs to minimize the risk of sharing any additional personally identifiable information (PII).

Data analysis
Analysis code from this study is archived in the Social Media Archive (SOMAR) at ICPSR (https://socialmediaarchive.org) and made available in the ICPSR virtual data enclave for university IRB-approved research on elections or to validate the findings of this study per the data availability statement above.The data in this study was analyzed using R (version 4.1.1),which was executed via R notebooks on JupyterLab For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers.We strongly encourage code deposition in a community repository (e.g.GitHub).See the Nature Portfolio guidelines for submitting code & software for further information.

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March 2021

Data exclusions
Data from 25 users (0.1% of the sample) for whom no classifier prediction for ideology was available were excluded from the analyses because it would not be possible to determine whether certain sources of online content were congenial or cross-cutting for those participants.Details on this exclusion are available in SI Section S1.3, "Classifiers."This exclusion criteria was preregistered.

Non-participation
We detail information about recruitment and response rates for the collaboration in the Supplementary Information S9.4.In total, 75,318 participants were randomized into one of the experimental conditions within the collaboration.Of these, 8 (0.01%) withdrew from the study after completing a post-treatment wave, and 1,369 (1.8%) deleted or deactivated their Facebook account since the study was completed.Data from these participants are not included in the analyses in this paper.This information is detailed in S2.1.3 of the SI, "Deleted Accounts and Study Withdrawals."

Randomization
Respondents were randomly assigned to treatment or control with probabilities that maximized statistical power using block randomization.A combination of survey-based pre-treatment outcomes and Facebook data were used to define the blocks in the sample of interest.The full details are available in Section S9.3 of the SI, "Randomization." Reporting for specific materials, systems and methods We require information from authors about some types of materials, experimental systems and methods used in many studies.Here, indicate whether each material, system or method listed is relevant to your study.If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

Fig. 1 |
Fig. 1 | The distribution of exposure to content among Facebook users.a, The distribution of the exposure of monthly active adult Facebook users in the USA to content from like-minded sources, cross-cutting sources, and those that fall into neither category in their Facebook Feed.Estimates are presented

Fig. 2 |
Fig. 2 | Day-level exposure to content from like-minded sources in the Facebook Feed by experimental group.Mean day-level share of respondent views of content from like-minded sources by experimental group between 1 July and 23 December 2020.Sources are classified as like-minded on the basis of estimates from an internal Facebook classifier at the individual level for users and friends, and at the audience level for Pages and groups.W1-W5indicate survey waves 1 to 5; shading indicates wave duration.Extended Data Fig.3provides a comparable graph of views of content from cross-cutting sources.Note: exposure levels increased briefly on 2 and 3 November owing to a technical problem; details are provided in Supplementary Information, section 4.11.

. 1 |. 2 |
Distribution of predicted ideology score by selfreported ideology, party identification, and approval of former president Trump.Each histograms displays the distribution of respondents' predicted ideology score according to Meta's classifier for Facebook U.S. adult users (see Supplementary Iinformation, section 1.3) by subsets defined by their self-reported political characteristics.The histograms have bins of width equal to 0.10.Pre-treatment exposure to Facebook Feed content by source type: Study participants and daily Facebook users.Pre-treatment distribution of Facebook Feed exposure to content from like-minded sources (left column), cross-cutting sources (center column), and those that fall into neither category (right column).Estimates presented for all content (top row) and for content classified as civic (i.e., political; center row) and news (bottom row).Source and content classifications were created using internal Facebook classifiers (see Supplementary Information, section 1.3).The graph includes the distribution of exposure for both study participants and the Facebook population of users age 18+ who logged into Facebook each day in the month prior to August 17, 2020, when the study sampling frame was constructed.Extended DataFig. 3 | Day-level exposure to content from cross-cutting sources in the Facebook Feed by experimental group.Mean day-level share of respondent views of content from cross-cutting sources by experimental group July 1-December 23, 2020.Sources classified as cross-cutting based on estimates from an internal Facebook classifier at the individual level for users and friends and at the audience level for Pages and groups (see Supplementary Information, section 1.3).W1-W5 indicate survey Waves 1-5; shading indicates wave duration.(Note: Exposure levels briefly decreased on November 2-3 due to a technical problem; see Supplementary Information, section 4.11 for details).Extended Data Fig. 4 | Treatment effects on outcomes for primary hypotheses.Average treatment effects of reducing exposure to like-minded sources in the Facebook Feed from September 24-December 23, 2020.The figure shows OLS estimates of sample average treatment effects (SATE) as well as population average treatment effect (PATE) using survey weights and HC2 robust standard errors.Exposure and engagement outcome measures were measured using Feed behavior by participants.Survey outcome measures are standardized scales averaged across surveys conducted November 4-18, 2020 and/or December 9-23, 2020.Sample size and P values for each estimate are reported in Supplementary Distribution of exposure by source type (like-minded and cross-cutting) for study participants in the treatment and control groups as well as for monthly active users in both the pre-treatment and treatment periods 151 observations (0.65%) dropped by listwise deletion.US monthly users indicates the set of US adults who logged onto Facebook at least once in the 30 days preceding 17 August 2020.The last column reports the unadjusted P value from a two-sided test of the hypothesis of no difference between treatment and control groups on each metric, computed using the baseline OLS model (see Section 1.5.1).

| Effects of reducing Facebook Feed exposure to like-minded sources.
Average treatment effects of reducing exposure to like-minded sources in the Facebook Feed from 24 September to 23 December 2020.a-c,Sampleaveragetreatmenteffects (SATE) on Feed exposure and engagement.b,Totalengagement(forcontent, the total number of engagement actions).c,Engagementrate(theprobability of engaging conditional on exposure).d,Outcomes of surveys on attitudes, with population average treatment effects (PATEs) estimated using survey weights.Supplementary Information 1.4 provides full descriptions of all outcome variables.Non-bolded outcomes that appear below a bolded header are part of that category.For example, in d, 'issue positions', 'group evaluations' and 'vote choice and candidate evaluations' appear below 'ideologically consistent views', indicating that all are measured such that higher values indicate greater ideological consistency.Survey outcome measures are standardized scales averaged across surveys conducted between 4 November and 18 November 2020 and/or 9 December and 23 December 2020.Point estimates are provided in Extended Data Table3.Sample average treatment effect estimates on attitudes are provided in Extended Data Fig.4.All effects estimated using ordinary least squares (OLS) with robust standard errors and follow the preregistered analysis plan.Points marked with asterisks indicate findings that are significant (P < 0.05 after adjustment); points marked with open circles indicate P > 0.05 (all tests are two-sided).P values are false-discovery rate (FDR)-adjusted (Supplementary Information, section 1.5.4).
.7% in the control group to 27.9% in the treatment group, a change of 0.43 s.d.(95% confidence interval: 0.40, 0.46).Rather, respondents in the treatment

Table 47 . Extended Data Fig. 5 | Treatment effects on outcomes for research questions.
Average treatment effects of reducing exposure to like-minded sources in the Facebook Feed from September 24-December 23, 2020.The figure shows OLS estimates of sample average treatment effects (SATE) as well as population average treatment effect (PATE) using survey weights and HC2 robust standard errors.Engagement outcome measures were measured using Feed behavior by participants.Survey outcome measures are standardized scales averaged across surveys conducted November 4-18, 2020 and/or December 9-23, 2020, unless indicated otherwise.Sample size and P values for each estimate are reported in Supplementary Table47.

Table 1 | Pre-treatment exposure to Facebook Feed content by source type: Study participants and monthly Facebook users
Pre-treatment exposure by source type among study participants and U.S. adults who logged into Facebook at least once in the month prior to August 17, 2020.The first four columns in each panel of the table report the percentage of users (i.e., respondents or monthly active users) for whom the proportion of content viewed from a given source type (i.e., like-minded, cross-cutting, and sources that fall into neither category) is in the stated range (column).Estimates presented for all content (top set of rows), content classified as civic (i.e., political; center set of rows), and news (bottom set of rows).The final column reports the median (p50), which is approximated to the nearest percentage point among Facebook monthly users for computational efficiency.

Table 2 | Pre-treatment exposure to Facebook Feed content by source type: Study participants and daily Facebook users
Pre-treatment exposure by source type among study participants and U.S. adults who logged into Facebook every day in the 30 days preceding August 17, 2020.The first four columns in each panel of the table report the percentage of users (i.e., respondents or daily active users) for whom the proportion of content viewed from a given source type (i.e., like-minded, cross-cutting, and sources that fall into neither category) is in the stated range for (right column).Estimates presented for all content (top set of rows), content classified as civic (i.e., political; center set of rows), and news (bottom set of rows).The final column reports the median (p50), which is approximated to the nearest percentage point among Facebook daily users for computational efficiency.The denominator for these percentages is all respondents or all daily active users.