Algorithms actually create political polarization – and this AI tool lets users avoid it
Researchers used a browser extension to reorder people’s X feeds, reducing their polarizing effect.
People often blame social media algorithms that prioritize extreme content for increasing political polarization, but this effect is difficult to prove. Only platform owners have access to their algorithms, so researchers cannot identify potential changes in the behavior of products without the (increasingly rare) cooperation of the platforms.
a search In Science Not only does this provide strong evidence that these algorithms cause polarization, but it also shows that this trend can be bucked without getting a platform’s approval or removing posts.
Researchers have created a browser extension that can move posts down or up in users’ X feeds that display attitudes associated with polarization, such as partisan hostility and support for undemocratic practices. The tool uses a large language model (LLM) to analyze and reorder posts in real time.
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“Only the platforms have the power to shape and understand these algorithms,” says Martin Sevski, a study co-author and information scientist at the University of Washington. “This tool gives that power to independent researchers.”
The team conducted an experiment for 10 days before the 2024 US elections. More than 1,200 volunteer participants viewed feeds in which polarizing content was either ranked significantly lower, making users less likely to see it before they stopped scrolling, or ranked slightly higher.
Regardless of political orientation, those who were not exposed to polarizing posts felt more warmly toward a group that opposed their viewpoint (based on short surveys) than those exposed to the unchanged feed, while those who saw increased polarizing posts felt cooler.
The difference on a 100-degree “feeling thermometer” was two to three degrees. That may not seem big, but “this is equivalent to an average of three years of historical change in the US,” says co-author Chenyan Jia, a communications scientist at Northeastern University. The manipulation also affected how much sadness and anger participants felt while scrolling.
According to Victoria Oldembourgo de Mello, a psychologist at the University of Toronto who studies how technology shapes behavior and society, the study authors effectively combined strict controls with a real-world setting. “And they do it in a clever way that bypasses (platform) approval. No one has done this before.” She says the persistence of the effects is unclear — they may fade or compound over time. The researchers say this is an important direction for future work and have made their code freely available so other scientists can explore it.
The current version of the tool only works for browser-based social media sites. “Creating something that can be used with apps is technically more difficult given the way[they]work, but it’s something we’re researching,” Sevski says.
The researchers also plan to study other interventions for social media feeds, taking advantage of the flexibility offered by LLM analysis, says Sevski. “Our framework is very general, and anyone can think about wellness, mental health, etc.”
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