The online proliferation of misinformation is pressing both society and business actors to provide effective mitigation strategies. The current study aims to bring more clarity to the common belief that engagement with reliable data will lead to disengagement from inaccurate information. The proposed experiment on 290 volunteers simulates the process of reading a narrative on terrorism — under misinformation or accurate information — followed by exposure to official data. Participants that consulted official data following exposure to misinformation (N = 43) did not differ from participants exposed solely to misinformation in regard to the level of worry about terrorism (N = 43). Similarly, there was no significant difference between misinformation (N = 43) and accurate (N = 47) information groups in the level of worry about terrorism after consulting official data. However, the role of media engagement has proven essential: when presented with accurate information followed by reliable data, participants’ worry about terrorism decreased significantly, but only for those highly engaged with news concerning terrorism. Similarly, when presented with misinformation followed by reliable data, participants’ worry significantly increased, but only for those not engaged with terrorism-related news. Further analyses revealed a polarization effect within the group of participants exposed to inaccurate information and reliable data, with the engagement with terrorism-related news and conservatism as predictors. These findings broaden the literature on strategies to tackle misinformation, by clarifying the role of fact-checking and narrative unpacking.

错误信息的在线扩散迫使社会和商业行为者提供有效的缓解策略。当前的研究旨在让人们更加清楚地认识到，使用可靠数据会导致脱离不准确信息。拟议的 290 名志愿者实验模拟了阅读恐怖主义叙事的过程——在错误信息或准确信息下——然后接触官方数据。该咨询暴露于误传（官方数据参与者ñ  = 43）没有参与者对于担心恐怖主义的水平（仅暴露误传有所不同ñ  = 43）。同样，错误信息（N = 43) 和准确 ( N = 47) 信息组在查阅官方数据后担心恐怖主义的程度。然而，媒体参与的作用已被证明是必不可少的：当提供准确的信息和可靠的数据时，参与者对恐怖主义的担忧显着减少，但仅限于那些高度关注恐怖主义新闻的人。同样，当出现错误信息和可靠数据时，参与者的担忧显着增加，但仅限于那些不参与恐怖主义相关新闻的人。进一步的分析揭示了暴露于不准确信息和可靠数据的参与者群体中的两极分化效应，将与恐怖主义相关的新闻和保守主义作为预测因素。这些发现拓宽了有关应对错误信息的策略的文献，