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Study Highlights Connection Between Your Personal Values And Fake News

A new study reveals how personal values significantly influence the content people share on social media, potentially driving the spread of misinformation. The research, published in the Journal of Experimental Psychology: General, explores the link between a user's moral values and their likelihood of sharing news stories, whether true or false. Researchers, led by Suhaib Abdurahman from the University of Southern California, focused on the difference between individualising values (care for others, equality) typically associated with liberals and binding values (loyalty, patriotism, authority) often held by conservatives. They created social media posts with links to news articles, some containing misinformation, framed with either individualising, binding, or neutral values. While conducting the study, participants were shown these posts and asked if they would share them. Results showed people were more inclined to share articles when the values expressed in the post aligned with their own, and this effect was stronger for misinformation. This suggests that a match between personal values and the message's framing can override critical evaluation, leading to the dissemination of false information. Interestingly, the study found that this effect wasn't due to increased deliberation or analytical thinking. People didn't necessarily think more or less about posts that aligned with their values; they were simply more likely to share them. Analysing real Twitter posts, researchers classified users as liberal or conservative based on their follows and used machine learning to identify the presence of binding, individualising, or neutral values in the content. Results paralleled the experimental findings, reinforcing the idea that value alignment drives sharing behaviour, regardless of the content's accuracy. This sheds light on why misinformation spreads so readily on social media, highlighting the powerful role of personal values in shaping online behaviour.