AI tools have reduced political polarization caused by social media algorithms

Algorithms actually create political polarization, but this AI tool allows users to avoid it

Written by Simon Makin Edited by Sarah Lewin Frazier

Social media algorithms that prioritize extremist content are often blamed for increasing political polarization, but this effect is difficult to prove. Because only the platform owner has access to the algorithm, researchers are unable to identify possible tweaks to the product’s behavior without the platform’s (increasingly rare) cooperation.

research in science Not only do we provide convincing evidence that these algorithms cause polarization, we also show that this trend can be mitigated without the need for platform approval or removal of posts.

The researchers created a browser extension that can push down or move up posts in a user’s X feed that display attitudes associated with polarization, such as partisan hostility or support for undemocratic practices. This tool uses large-scale language models (LLM) to analyze and sort posts in real-time.

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“Only platforms have the power to shape and understand these algorithms,” said study co-author Martin Savesky, an information scientist at the University of Washington. “This tool gives independent researchers that power.”

The research team conducted an experiment over 10 days in preparation for the 2024 US election. More than 1,200 volunteer participants saw feeds in which polarizing content was either significantly down-ranked, less likely to be seen by users before they stopped scrolling, or slightly up-ranked.

Regardless of political orientation, people who had polarizing posts de-emphasized felt warmer (based on a short survey) toward groups that opposed their views than groups whose feeds had not been changed, while people whose polarizing posts were highlighted felt cooler.

When using a 100 degree “feeling thermometer”, the difference was 2 to 3 degrees. This may not seem like a big deal, but “this is equivalent to an average historical change of three years in the United States,” says co-author Chengyang Jia, a communications scientist at Northeastern University. This manipulation also affected the degree of sadness and anger that participants reported feeling while scrolling.

Victoria Oldenburgo de Mello, a psychologist at the University of Toronto who studies how technology shapes behavior and society, said the study authors successfully combined tight control with a real-world setting. “And they do it in clever ways that bypass it.” [platform] Approved. No one has ever done this before. ” The durability of the effects is unknown and may disappear or worsen over time, she added. The researchers say this is an important direction for future research and have made the code freely available so that other scientists can study it as well.

The current version of this tool only works with browser-based social media sites. Creating something that can be used in an app is “technically more difficult, [they] It worked, but that’s something we’re exploring,” Saveski said.

The researchers also plan to use the flexibility provided by LLM analysis to study other interventions on social media feeds, Saveski added. “Our framework is very general and allows us to think about things like well-being and mental health.”

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