Timing matters when correcting fake news

• fake news
• misinformation
• correction
• fact-checking
• memory

Concern about fake news escalated during the run-up to the 2016 US presidential election, when an estimated 44% of Americans visited untrustworthy websites (1). Faced with mounting public pressure, social media companies enlisted professional fact-checkers to flag misleading content. However, misconceptions often persist after people receive corrective messages (continued influence effect; ref. 2). Detailed corrections increase the likelihood of knowledge revision (3), but social media platforms prioritize user experience and typically attach simple tags (e.g., “disputed”) to posts. Can we optimize the longer-term impact of these brief fact-checks by presenting them at the right time?

There are arguments for placing fact-checks before, during, or after disputed information. Presenting fact-checks before headlines might confer psychological resistance. Inoculating people to weakened arguments makes them less vulnerable to persuasion (4). As examples, reading about “fake experts” protects people from climate science myths (5), and playing a game involving common disinformation tactics (e.g., faking an official Twitter account) helps people detect fake news (6). Prebunking could direct attention to a headline’s questionable features (e.g., sensational details). On the other hand, people might ignore the content entirely and miss an opportunity to encode it as “false.”

Alternatively, reading fact-checks alongside news could facilitate knowledge revision. Encoding retractions requires building a coherent mental model (7), which is easiest when misinformation and its correction are coactive (8). This mechanism explains why corrections rarely reinforce the original false belief (i.e., do not “backfire”) (9)—it is actually best to restate a myth when retracting it (10, 11). Thus, labeling a headline as “true” or “false” could increase salience and updating.

Finally, providing fact-checks after people process news could act as feedback, boosting long-term retention of the tags. Corrective feedback facilitates learning (12), especially when errors are made with high confidence (13). Prediction error enhances learning of new facts that violate expectations (14). Surprise also occurs when low-confidence guesses turn out to be right, improving subsequent memory (15). Debunking after readers form initial judgments about headlines could boost learning, even if they did not make an error.

Despite the extensive previous work on corrections, no study has directly compared the efficacy of equivalent corrections delivered before, during, or after exposure. In two nearly identical experiments (total N = 2,683), we tested whether the timing of corrections to fake news impacts discernment 1 wk later. Participants were exposed to 18 true and 18 false news headlines taken from social media (Fig. 1); they saw “true” and “false” tags immediately before (prebunking), during (labeling), or immediately after (debunking) reading and rating the accuracy of each headline. In a control condition, participants received no veracity information. One week later, they rated the accuracy of the 36 headlines again. To maximize power, we analyzed the final accuracy ratings from the two experiments together using linear regression with robust SEs clustered on subject and headline. We included dummies for each treatment condition, headline veracity (0 = false, 1 = true), and study. We also included the interaction between veracity and the treatment dummies, and the interaction between veracity and the study dummy.

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Fig. 1.

Sample true and false headlines, as shown in the before, during, and after conditions. Fact-checks appeared on separate screens in the before and after conditions.

Results

Fig. 2A shows the distribution of accuracy ratings for false headlines after 1 wk. Presenting corrections after, b = −0.123, F(1, 96587) = 16.34, P < 0.001, P_stan < 0.001, and during, b = −0.081, F(1, 96587) = 7.45, P = 0.006, P_stan = 0.033, exposure to each headline decreased belief in false headlines relative to the control condition (to a similar extent, F(1, 96587) = 2.03, P = 0.154). Presenting corrections before exposure, conversely, did not significantly reduce belief in false headlines, b = 0.042, F(1, 96587) = 1.74, P = 0.188, and was less effective than presenting corrections after, F(1, 96587) = 25.39, P < 0.001, P_stan < 0.001, or during, F(1, 96587) = 15.11, P < 0.001, P_stan < 0.001, reading.

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Fig. 2.

Distribution of accuracy ratings for false (A) and true (B) headlines and discernment (C) 1 wk after exposure, by treatment. Error bars indicate 95% CIs.

Fig. 2B shows the distribution of accuracy ratings for true headlines after 1 wk. While all three treatments significantly increased belief in true headlines relative to the control condition (F(1, 96587) > 6.75, P < 0.01, P_stan < 0.05 for all), presenting corrections after exposure was significantly more effective than during, F(1, 96587) = 65.53, P < 0.001, P_stan < 0.001, or before, F(1, 96587) = 47.02, P < 0.001, P_stan < 0.001, exposure.

Fig. 2C shows that this leads to significantly greater truth discernment (the difference in belief between true and false headlines) when corrections appeared after compared to during, F(1, 96587) = 37.74, P < 0.001, P_stan < 0.001, or before, F(1, 96587) = 65.08, P < 0.001, P_stan < 0.001, exposure (and during was marginally more effective than before, F(1, 96587) = 6.33, P = 0.012, P_stan = 0.062). Although before was more effective in Experiment 2 than in Experiment 1, after is still more effective than during or before exposure when considering each experiment separately (P < 0.001, P_stan < 0.01 for all comparisons).

Interestingly, neither analytic thinking, as measured by the Cognitive Reflection Test, nor political knowledge moderated the treatment effects (Ps > 0.421), despite both measures being associated with better baseline discernment (P < 0.001, P_stan < 0.001 for both). Lastly, providing corrections after reading may have been less effective for headlines that aligned with participants’ partisanship than for headlines that did not, F(1, 96587) = 5.06, P = 0.025, P_stan = 0.129, while the effectiveness of during and before did not differ based on partisan alignment (Ps > 0.30). Nonetheless, after was more effective than before or during exposure even for politically aligned headlines (P < 0.001, P_stan < 0.001, for all comparisons).

For regression tables and separate analyses of each experiment, see Open Science Framework (OSF, https://osf.io/bcq6d/).

• Copyright © 2021 the Author(s). Published by PNAS.