Another algorithm is possible
How social media could easily be changed to stop us hating each other so much
We all know that social media drives people crazy, but how much of that is because, en masse, other people are infuriating, and how much is because the social media companies design the platform to surface the most infuriating content?
Since they emerged in the 2000s, the major platforms like Facebook and X have switched, one by one, to algorithmic feeds. Now their calculations decide what you see, rather than your choices of who to follow delivering a chronological list of updates from those accounts. New social media platforms have emerged, like TikTok, where the algorithm is everything. With these platforms you don’t need to make any active decision on who to follow. The algorithm is just tuned by what you and others engage with to provide content to keep you scrolling.
These engagement algorithms provide a range of bad incentives for people to be the worst, most provocative, versions of themselves online. No wonder that a variety of ills have been attributed to social media. The lines of causality are complex, but one of the effects of social media that I find more plausible is that it affects our view of other people, and particularly people of different political orientations. Social scientists call the feeling that people with different views are not just wrong, but hateful ‘affective polarisation’. You won’t be surprised to learn that as well as various other forms of polarisation, affective polarisation has been rising in the US, and only accelerated since the dawn of the social media age.
If the particular algorithms of social media are causing social harm, perhaps different algorithms could do good, or at least less harm?
Jonathan Stray reports on a new study looking at just this. Stray and team recruited volunteers, who had different content injected into their social media feeds over a period of six months. The nearly ten thousand volunteers installed a browser extension which the research team, used to change what appeared their feeds, assigning them to a control group (standard algorithm) or one of five alternative algorithms designed to encourage prosocial responses.
The alternative algorithms were:
“Add news” - adding personally relevant news, from “factual and ideologically diverse” sources
“Diverse approval” - replacing political content with posts predicted to be interesting and engaging to both Republicans and Democrats
“Challenging Stereotypes” - replacing political content with “ideologically surprising posts that challenge dominant stereotypes of Democrats and Republicans”
“Uprank bridging” - a bridging algorithm which tries to identify content which has cross-partisan endorsement
“Uprank bridging, Downrank Toxic” - which was the bridging algorithm again with additional provision to remove toxic or outrage-inducing content
Users were recruited who used Facebook, Twitter/X and Reddit and the experiment was run from July to October 2024.
§
The results suggest the choice of algorithm mattered:
Most striking is that all the algorithms they tested reduced affective polarisation compared to the control condition (albeit, only two of them statistically significantly). This suggests to me that the algorithms on these platforms tend to be polarising by default, and most alternatives would be an improvement.
The authors summarise:
The most depolarizing algorithms are Add News (-0.044 SD) and Uprank Bridging, Downrank toxic (-0.042 SD), though we don’t actually have enough statistical power to distinguish the rankers.
Moreover, these results are sustainable. We ran the experiment for six months, which should be enough time for the novelty to wear off. “One shot” or “few shot” polarization reduction interventions (like watching an ad or talking to someone or taking a quiz) reduce polarization by an average of 5.4 points, but the effects wear off. The effects of an algorithmic change are permanent, and potentially compounding.
Although these are small effects, the authors use the context of historically increasing affective polarisation to equate their intervention to reversing around two and a half years of average polarisation increase.
There’s a reason all the social media companies have converged on the same family of algorithms which maximise engagement (and produce polarisation as a side effect). Previous studies which looked at attempts to alter the algorithm have tended to reduced engagement too - people spent less time on the apps when they were presented with things like strictly chronological feeds.
Despite the headline pushed by the paper, this also seems to be the effect for this study - for two out of the three platforms studied, engagement was reduced:
Twitter is the exception to the other two, meaning that across all three platforms the average engagement is constant despite the change of algorithms. Stray is direct: “No, we don’t know why we see this particular pattern.”
It’s speculation, but it may be that Facebook and Reddit, at the time of study, were on one side of the engaging-infuriating trade-off, while Twitter was over other side, having juiced the algorithm (and trained its base of users who post) to such an extent that those in the experiment experienced the alternative algorithms as a respite and increased their engagement.
§
Overall, the results show two things. First, this is evidence of modest but real benefits for these alternative content algorithms. The “add news” algorithm suggests that personalisation can be done without fuelling outgroup antipathy. The “bridging algorithm” result is another success for this method, which has been incorporated into the Community Notes system, as well as digital democracy tools like pol.is.
Secondly, and more broadly, the research shows that other algorithms are possible. Online and offline, the world is made according to our collective choices, and it could be remade. Nothing is inevitable about how these platforms work and they could be changed. As we increasingly use social media to know ourselves and each other, a choice of different algorithms wouldn’t just tweak a website, but would change which aspects of our nature are reflected back at us.
This newsletter is free for everyone to read and always will be. If you can afford it, feel free to chip in to help me keep writing for everyone: upgrade to a paid subscription (more on why here).
See below for references, and other things I’ve noticed.
References
Jonathan Stray: It’s Possible to Reduce Polarization without Reducing Engagement
Paper: Stray, J., Baker, I., Beknazar-Yuzbashev, G., Budak, C., Kamin, J., Rutherford, K., ... & Zheng, S. (2026). The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement. arXiv preprint arXiv:2603.19626.
Me on bridging algorithms: The algorithmic heart of Community Notes
More on Community Notes by me: Community Notes require a Community
Other things…
PODCAST: C. Thi Nguyen - Measurement, Meaning, and Play
There was a moment—as with a lot of other moments, I was pretty drunk—where I realized what games are. They are like governments; they are rule systems, but for fun. Games are art-governments.
Dialectic: C. Thi Nguyen - Measurement, Meaning, and Play
Baumol’s Cost Disease
Ben Vincent has an economic simulation of Baumol’s Cost Disease for you. It explains “why healthcare and education get more expensive while televisions get cheaper, and why this is nobody’s fault.”
https://drbenvincent.github.io/posts/baumol-cost-disease.html
Possibly the most-important-yet-least-known theory from economics?
Emily Alagha: What News and the Web Are Telling Research About Trust
I enjoyed this piece that put trust (and trust signals) in academic research in the context of the same global trend: decentralisation of authority, and used the experiences of trust in the web and in news to diagnose the near-future of trust in scholarly publishing.
It would be a better article if it wasn’t co-written with AI, but I endorse the basic idea of the need to investigate and invest in alternative authority systems.
Link: What News and the Web Are Telling Research About Trust
PODCAST: Change Beliefs, Change Opinions? with Yamil Velez
“Yamil shares his work using large language models to generate personalized arguments, showing that people will update their beliefs pretty readily—but only some of those belief changes actually translate into attitude change.”
So the distinction is between basic beliefs and attitudes toward specific issues. Velez’s research finds that by intervening on beliefs you can affect attitudes. Note this is exactly how a reasonable model of attitudes should work (and contra the despairing “people don’t do what they believe in they do what’s most convenient and then they repent” school of thought).
A recent paper from him is
VELEZ, Y. R., LIU, P., & CLIFFORD, S. (2026). Beyond Belief Change: The Persuasive Returns of Targeting Attitude–Relevant Beliefs. American Political Science Review, 1-21. https://doi.org/10.1017/S0003055426101622
In this they showed that attitudes were moved by providing counterarguments to people’s “focal beliefs”, which bore on the attitude, but less by providing counterarguments to “distal beliefs” (they used LLMs to personalise the participant interactions). Exemplars of each of these, from the paper:
Elicited Attitude: Reducing inflation is important for economic growth.
Focal Belief: Reduced inflation leads to more consumer spending, thus boosting the economy.
Distal Belief: A stronger dollar resulting from reduced inflation benefits the U.S. economy by increasing purchasing power abroad.
Opinion Science: Change Beliefs, Change Opinions? with Yamil Velez
…And finally
END
Comments? Feedback? Personalised content? I am tom@idiolect.org.uk and on Mastodon at @tomstafford@mastodon.online
AI declaration: I write all the words and think all the thoughts myself. I asked Gemini to check for spelling and grammar.
Surely if someone made a social media platform specifically geared around a non polarisation algorithm people would flock to it?