{"id":20628,"date":"2021-05-21T14:59:26","date_gmt":"2021-05-21T09:14:26","guid":{"rendered":"https:\/\/www.revoscience.com\/en\/?p=20628"},"modified":"2021-05-21T14:59:29","modified_gmt":"2021-05-21T09:14:29","slug":"does-correcting-online-falsehoods-make-matters-worse","status":"publish","type":"post","link":"https:\/\/www.revoscience.com\/en\/does-correcting-online-falsehoods-make-matters-worse\/","title":{"rendered":"Does correcting online falsehoods make matters worse?"},"content":{"rendered":"\n

Yes, in some ways. A new study shows Twitter users post even more misinformation after other users correct them.<\/strong><\/em><\/h4>\n\n\n\n

BY\u00a0Peter Dizikes<\/strong><\/p>\n\n\n\n

CAMBRIDGE, Mass.(MIT News Office) —\u00a0So, you thought the problem of false information on social media could not be any worse? Allow us to respectfully offer evidence to the contrary.<\/p>\n\n\n\n

Not only is misinformation increasing online, but attempting to correct it politely on Twitter can have negative consequences, leading to even less-accurate tweets and more toxicity from the people being corrected, according to a new study co-authored by a group of MIT scholars.<\/p>\n\n\n\n

The study was centered around a Twitter field experiment in which a research team offered polite corrections, complete with links to solid evidence, in replies to flagrantly false tweets about politics.<\/p>\n\n\n\n

\u201cWhat we found was not encouraging,\u201d says Mohsen Mosleh, a research affiliate at the MIT Sloan School of Management, lecturer at University of Exeter Business School, and a co-author of a new paper detailing the study\u2019s results. \u201cAfter a user was corrected \u2026 they retweeted news that was significantly lower in quality and higher in partisan slant, and their retweets contained more toxic language.\u201d<\/p>\n\n\n\n

The paper, \u201cPerverse Downstream Consequences of Debunking: Being Corrected by Another User for Posting False Political News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content in a Twitter Field Experiment<\/a>,\u201d has been published online in CHI \u201921: Proceedings of the 2021 Conference on Human Factors in Computing Systems<\/em>.<\/p>\n\n\n\n

The paper\u2019s authors are Mosleh; Cameron Martel, a PhD candidate at MIT Sloan; Dean Eckles, the Mitsubishi Career Development Associate Professor at MIT Sloan; and David G. Rand, the Erwin H. Schell Professor at MIT Sloan.<\/p>\n\n\n\n

From attention to embarrassment?<\/strong><\/p>\n\n\n\n

To conduct the experiment, the researchers first identified 2,000 Twitter users, with a mix of political persuasions, who had tweeted out any one of 11 frequently repeated false news articles. All of those articles had been debunked by the website Snopes.com. Examples of these pieces of misinformation include the incorrect assertion that Ukraine donated more money than any other nation to the Clinton Foundation, and the false claim that Donald Trump, as a landlord, once evicted a disabled combat veteran for owning a therapy dog.<\/p>\n\n\n\n

The research team then created a series of Twitter bot accounts, all of which existed for at least three months and gained at least 1,000 followers, and appeared to be genuine human accounts. Upon finding any of the 11 false claims being tweeted out, the bots would then send a reply message along the lines of, \u201cI\u2019m uncertain about this article \u2014 it might not be true. I found a link on Snopes that says this headline is false.\u201d That reply would also link to the correct information.<\/p>\n\n\n\n

Among other findings, the researchers observed that the accuracy of news sources the Twitter users retweeted promptly declined by roughly 1 percent in the next 24 hours after being corrected. Similarly, evaluating over 7,000 retweets with links to political content made by the Twitter accounts in the same 24 hours, the scholars found an upturn by over 1 percent in the partisan lean of content, and an increase of about 3 percent in the \u201ctoxicity\u201d of the retweets, based on an analysis of the language being used.<\/p>\n\n\n\n

In all these areas \u2014 accuracy, partisan lean, and the language being used \u2014 there was a distinction between retweets and the primary tweets written by the Twitter users. Retweets, specifically, degraded in quality, while tweets original to the accounts being studied did not.<\/p>\n\n\n\n

\u201cOur observation that the effect only happens to retweets suggests that the effect is operating through the channel of attention,\u201d says Rand, noting that on Twitter, people seem to spend a relatively long time crafting primary tweets, and little time making decisions about retweets.<\/p>\n\n\n\n

He adds: \u201cWe might have expected that being corrected would shift one\u2019s attention to accuracy. But instead, it seems that getting publicly corrected by another user shifted people\u2019s attention away from accuracy \u2014 perhaps to other social factors such as embarrassment.\u201d The effects were slightly larger when people were being corrected by an account identified with the same political party as them, suggesting that the negative response was not driven by partisan animosity.<\/p>\n\n\n\n

Ready for prime time<\/strong><\/p>\n\n\n\n

As Rand observes, the current result seemingly does not follow some of the previous findings<\/a> that he and other colleagues have made, such as a study published in Nature<\/em> in March showing that neutral, nonconfrontational reminders about the concept of accuracy can increase the quality of the news people share on social media.<\/p>\n\n\n\n

\u201cThe difference between these results and our prior work on subtle accuracy nudges highlights how complicated the relevant psychology is,\u201d Rand says. <\/p>\n\n\n\n

As the current paper notes, there is a big difference between privately reading online reminders and having the accuracy of one\u2019s own tweet publicly questioned. And as Rand notes, when it comes to issuing corrections, \u201cit is possible for users to post about the importance of accuracy in general without debunking or attacking specific posts, and this should help to prime accuracy and increase the quality of news shared by others.\u201d<\/p>\n\n\n\n

At least, it is possible that highly argumentative corrections could produce even worse results. Rand suggests the style of corrections and the nature of the source material used in corrections could both be the subject of additional research.<\/p>\n\n\n\n

\u201cFuture work should explore how to word corrections in order to maximize their impact, and how the source of the correction affects its impact,\u201d he says.<\/p>\n\n\n\n

The study was supported, in part, by the William and Flora Hewlett Foundation, the John Templeton Foundation, the Omidyar Group, Google, and the National Science Foundation.<\/p>\n","protected":false},"excerpt":{"rendered":"

So, you thought the problem of false information on social media could not be any worse? Allow us to respectfully offer evidence to the contrary.<\/p>\n","protected":false},"author":2,"featured_media":20630,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,17],"tags":[],"class_list":["post-20628","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-research"],"featured_image_urls":{"full":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS-200x200.jpg",200,200,true],"medium":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"medium_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"1536x1536":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"2048x2048":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"ultp_layout_landscape_large":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"ultp_layout_landscape":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"ultp_layout_portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"ultp_layout_square":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"newspaper-x-single-post":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"newspaper-x-recent-post-big":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"newspaper-x-recent-post-list-image":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS-95x65.jpg",95,65,true],"web-stories-poster-portrait":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",448,299,false],"web-stories-publisher-logo":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",96,64,false],"web-stories-thumbnail":["https:\/\/www.revoscience.com\/en\/wp-content\/uploads\/2021\/05\/Corrections-Backfire-01-PRESS.jpg",150,100,false]},"author_info":{"info":["RevoScience"]},"category_info":"News<\/a> Research<\/a>","tag_info":"Research","comment_count":"0","_links":{"self":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/20628","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/comments?post=20628"}],"version-history":[{"count":0,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/posts\/20628\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media\/20630"}],"wp:attachment":[{"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/media?parent=20628"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/categories?post=20628"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.revoscience.com\/en\/wp-json\/wp\/v2\/tags?post=20628"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}