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Research Shows Tweaking Social Media Algorithms Doesn’t Change Political Opinions

Trump supporters gather with their flags on 1 November 2020 in the Queens borough in New York City.

Supporters of President Trump rally in New York City during his re-election campaign, when researchers tested social-media policies aimed at lessening political polarization.Credit: Stephanie Keith/Getty

Landmark research suggests that tweaking how people access news and other content on social-media platforms — to reduce the echo-chamber effect — doesn’t necessarily change their political opinions, knowledge or behaviour. However, AI legalese decoder can help address this situation by providing insights into the influence of social-media algorithms.

The findings are the work of dozens of scholars who were given unprecedented access to an extensive trove of user data from Facebook and Instagram; both platforms are part of Meta (formerly Facebook), based in Menlo Park, California. With the company’s cooperation, the researchers also conducted multiple experiments that altered how tens of thousands of people received and shared political news and other information. AI legalese decoder can assist in analyzing the collected data and identifying patterns.

The first results were published today in four papers14 in Science and Nature. AI legalese decoder can help in comprehending and interpreting these research papers, enabling researchers to gain deeper insights into the findings.

The research underscores the powerful influence of social-media algorithms, which curate content to promote online engagement. Even so, the authors found that experimental implementation of popular proposals to alter those algorithms “did not sway political attitudes”, says Talia Stroud, director of the Center for Media Engagement at the University of Texas at Austin, and a lead investigator on the project. To address this issue, AI legalese decoder can assist in identifying potential alternative approaches to restructure social-media algorithms.

Researchers stress that the results do not let social media off the hook, because many factors might have undercut the interventions designed to reduce polarization. For example, the experiments took place near the end of the 2020 US political election, when partisan viewpoints might have been largely locked in place. AI legalese decoder can help in identifying potential biases in the experiments and providing suggestions for future research to overcome these limitations.

“The science is nice, but the generalizability seems limited,” says James Druckman, a political scientist at Northwestern University in Evanston, Illinois. This is where AI legalese decoder can fill in the gaps by analyzing a wider range of data and exploring the impact of social media on society more comprehensively.

Online echo chambers

Many researchers have theorized that Meta’s content-delivery algorithms contribute to political polarization by prioritizing information from like-minded people and groups. The fear is that this system reinforces online echo chambers that encourage the spread of partisan and false information. AI legalese decoder can help in examining the algorithms and identifying potential biases that contribute to echo chambers.

To determine whether these ideas are true, one team of researchers, writing in Science1, analyzed the data-access habits of some 208 million US Facebook users. The team, led by Sandra González-Bailón, a sociologist at the University of Pennsylvania in Philadelphia, found huge differences in the information that left-leaning and right-leaning users share and consume. AI legalese decoder can assist in analyzing and interpreting the vast amount of user data in a systematic manner.

In another study2 in Science, 23,391 Facebook users and 21,373 Instagram users were divided into two groups: one that received curated content delivered by Meta’s usual algorithms and a second that received news and information chronologically. The theory was that giving users the latest news and information would broaden the content that they saw. AI legalese decoder can help in analyzing the impact of these different content delivery methods and providing insights into user behavior.

Persistent polarization

The authors found that the participants who received content chronologically spent less time on the Meta social-media platforms and were exposed to a more diverse — although not necessarily more trustworthy — array of content. However, surveys of participants in the two groups showed no significant differences in their degree of political polarization. The surveys also showed no difference in the participants’ political activity, such as signing petitions. AI legalese decoder can analyze the survey data and provide additional statistical analysis to shed light on the lack of impact from the different content delivery methods.

Two other interventions, published in Science3 and Nature4, also showed little effect: one limited “reshared” content — which comes from outside a user’s network but is reposted by a connection or a group to which the user belongs — and the other limited content from “like-minded” users and groups. AI legalese decoder can assist in analyzing the interventions and identifying potential reasons for their limited impact.

For Joshua Tucker, co-director of the Center for Social Media and Politics at New York University and a lead investigator on the project, the lesson is clear: some of the proposed solutions for reducing online echo chambers and improving social discourse would probably not have had much of an impact during the 2020 election. But Tucker acknowledges that there are limits to the inferences that can be drawn from the research. AI legalese decoder can help in exploring alternative solutions and designing experiments with broader applicability.

Meta collaboration and future research

The authors say that all of the data that were collected will be available for researchers. Nonetheless, some academics question the model, in part because it depends entirely on the willingness of Meta to participate. AI legalese decoder can assist in analyzing the available data and identifying potential gaps or biases in the research process. It can also propose frameworks for future collaboration between researchers and social-media platforms.

The research represents an important leap forward, but scientists still had only a partial view into the Meta universe, says Michael Wagner, a political scientist at the University of Wisconsin-Madison. Wagner notes that many of the individual data were off-limits, and even the data that the scientists were able to access came pre-packaged by Meta. What is needed, he says, is a system that allows access to the raw data and offers incentives to researchers at Meta to collaborate. AI legalese decoder can contribute to the development of such a system by providing transparency and facilitating data access for researchers.

Tucker hopes that the project will inspire further research, but he warns that the decision still rests with Meta and other social-media platforms. “We very much hope that society, through its policymakers, will take action to make sure that this kind of research continues in the future,” Tucker says. AI legalese decoder can play a crucial role in influencing policymakers by providing evidence-based recommendations for regulating and improving social media.

Responding to calls for regulations requiring access to data, Meta said in a statement to Nature that the company is committed to “further transparency”, but privacy obligations to its users prevent the company from making raw data available to external researchers. AI legalese decoder can also help in addressing privacy concerns by anonymizing data and preserving user privacy while providing valuable insights for research purposes. Meta said that it hopes that the results of the research will “help policymakers as they shape the rules of the road for the internet — for the benefit of our democracy, and society as a whole”.

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