Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention — TEI-tagged view

Gordon Pennycook, Jonathon McPhetres, Yunhao Zhang, Jackson G. Lu, David G. Rand · 2020 · social-psychology · doi:10.1177/0956797620939054 · TEI xml · tagged edition · index · concepts · authors
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truth discernment2 inattention accuracy nudge2 CRT demand characteristics2 sharing intentions partisanship quota sampling2 behavioural-experiment117
Method, as described: two preregistered survey experiments on quota-matched US samples, 853 and 856 participants, 15 true and 15 false COVID-19 headlines in Facebook-post format, analysed at the rating level with standard errors clustered on participant and headline

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenontruth discernment that is present when people are asked about accuracy and nearly absent when the same people are asked what they would share— “We began by comparing discernment—the difference between responses to true headlines and false headlines—across conditions. As predicted, we observed a significant interaction between headline veracity and condition, β = −0.126, F(1, 25586) = 42.24, p < .0001, indicating that discernment was higher for accuracy judgments than sharing…
claim-typemechanism— “Theoretically, our findings shed new light on the perspective that inattention plays an important role in the sharing of misinformation online. By demonstrating the role of inattention in the context of COVID-19 misinformation (rather than politics), our results suggest that partisanship is not, apparently, the key factor distracting people…
method — evidence— “Lucid uses quota sampling to provide a sample that is matched to the U.S. public on age, gender, ethnicity, and geographic region. We selected Lucid because it provides a sample that is reasonably representative of the U.S. population while being affordable for large samples.”
object of study — evidence— “A key outcome from the news task is truth discernment—that is, the extent to which individuals distinguish between true and false content in their judgments. Discernment is defined as the difference in accuracy judgments (or sharing intentions) between true and false headlines. For example, an individual who shared 9 out of 15 true headlines…
research question — evidence— “It was unclear, however, how (or whether) these results would generalize to COVID-19. First, it may be that a greater level of specialized knowledge is required to correctly judge the accuracy of health information relative to political information. Thus, participants may be unable to discern truth from falsehood in the context of COVID-19…
contribution — evidence— “As predicted, we observed a significant positive interaction between headline veracity and treatment, β = 0.039, F(1, 25623) = 17.88, p < .0001; the treatment increased sharing discernment (i.e., participants were more likely to share true headlines relative to false headlines after they rated the accuracy of a single non-COVID-related…
transferfrom cognitive psychology: priming - raising the accessibility of a concept with an unrelated task - carried from cognitive psychology into a platform-deployable intervention against misinformation— “Prior to deciding which headlines they would share on social media, participants were subtly primed to think about accuracy by being asked to rate the accuracy of a single non-COVID-related news headline. This minimal, content-neutral intervention nearly tripled participants’ level of discernment between sharing true and sharing false…transfers[0]
transferfrom behavioural economics: the nudge frame from behavioural economics: change what is salient at the moment of choice rather than what the chooser knows or is allowed to do— “Such interventions are easily scalable and do not require platforms to make decision about what content to censor.”transfers[1]
mechanisma preference that is never consulted the account is not that people want to spread falsehood, nor that they cannot tell the difference. It is that the sharing context puts other things in front of accuracy - social approval, partisan alignment, the fact that news sits between baby photos - so the preference exists and is simply not applied at the moment of decision— “According to this account, people generally wish to avoid spreading misinformation and, in fact, are often able to tell truth from falsehood; however, they nonetheless share false and misleading content because the social media context focuses their attention on factors other than accuracy (e.g., partisan alignment). As a result, they get…mechanisms[0]
mechanisma prime that carries no content the treatment is one accuracy judgement about a politically neutral headline unrelated to the topic, framed as a pretest. It supplies no information about COVID-19 and no instruction about sharing; what it does is raise the accessibility of the concept of accuracy, and the effect is a change in what the participant attends to rather than in what they know— “Each participant was randomly assigned to one of two conditions. In the control condition, they began the news-sharing task as in Study 1. In the treatment condition, they rated the accuracy of a single headline (unrelated to COVID-19) before beginning the news-sharing task; following, we framed this as being for a pretest. Each participant…mechanisms[1]
mechanismthe item-level signature of an attention effect if the nudge works by directing attention to accuracy, then its effect on a headline should track how accurate that headline is perceived to be - pushing sharing up for the clearly true and down for the clearly false. Measured across the thirty items using Study 1's ratings as the accuracy scale, r(28) = .76— “As shown in Figure 3, there was indeed a strong positive correlation between a headline’s perceived accuracy and the impact of the treatment, r(28) = .76, p < .0001. Headlines that were more likely to be identified as true (on the basis of Study 1 data) were more strongly positively impacted (sharing increases) by nudging people to…mechanisms[2]
extends the inattention-based account of political fake-news sharing, carried here to COVID-19 misinformation where the content is largely apolitical and the stakes are health— “According to this account, people generally wish to avoid spreading misinformation and, in fact, are often able to tell truth from falsehood; however, they nonetheless share false and misleading content because the social media context focuses their attention on factors other than accuracy (e.g., partisan alignment). As a result, they get…claim_relations[0]
critiques the view that partisan motivation is what distracts people from accuracy on social media - the effect appears on apolitical content and is not moderated by partisanship
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claim_relations[1]
qualifies its own intervention - accuracy nudges are far from a complete solution, and the sharing measure is hypothetical
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claim_relations[2]
confirms the analytic-cognitive-style literature on truth discernment - CRT scores relate to discernment here in the same pattern previously seen with political headlines
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claim_relations[3]
sensetruth discernment - WHEN THE QUESTION IS ACCURACY: veracity moves the answer at Cohen's d = 0.657
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senses[0]
sensetruth discernment - WHEN THE QUESTION IS SHARING: d = 0.121 from the same people on the same headlines, and 32.4% more would share a false headline than call it accurate
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senses[1]
sensesharing discernment - IN THE CONTROL CONDITION OF STUDY 2: not significantly different from zero, d = 0.050, p = .24
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senses[2]
sensesharing discernment - AFTER ONE ACCURACY JUDGEMENT ABOUT AN UNRELATED HEADLINE: d = 0.142, p = .003, and 2.8 times the control level
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senses[3]
sensethe sample - AS DESIGNED: quota matched to the US public on age, gender, ethnicity and region
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senses[4]
sensethe sample - AS THE AUTHORS QUALIFY IT: not obtained by probability sampling and therefore not to be treated as nationally representative
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senses[5]
controlthe accuracy induction disguised as a pretest, using a single politically neutral headline unrelated to the topic of the sharing task — held
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— rules out demand characteristics and social desirability as the explanation of the treatment effect - a participant who cannot see what the manipulation is for cannot comply with it
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controls[0]
controlapolitical COVID-19 content used in place of the political headlines the prior literature used, with partisanship measured and tested as a moderator — held
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— rules out partisan motivation as the thing distracting people from accuracy - the effect survives when the content gives partisanship nothing to work with
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controls[1]
controla post hoc item-level analysis correlating each headline's treatment effect against its perceived accuracy from the other study's participants — held
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— rules out a general suppression of sharing as the explanation - an attention effect must push true headlines UP and false ones down, and the correlation is r(28) = .76
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controls[2]
controlall participants retained in the main analyses regardless of failing the three attention screeners, per the preregistered plan, with the key result reported across levels of attentiveness — held
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— rules out the effect being an artefact of who was excluded - dropping inattentive respondents would trade representativeness for a cleaner sample and the authors show the result does not depend on the trade
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controls[3]
controlone false headline reclassified after data collection as misleading rather than untrue, and the analysis rerun without it — held
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— rules out a defective stimulus carrying the result - the item is disclosed, the removal is tested, and it is retained on the strength of the test rather than quietly
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controls[4]
controllinear probability models used for interpretability, with logistic regression run alongside — held
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— rules out the functional form as the source of the result - the readable model and the conventional one are reported to agree
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controls[5]
controlthe self-reported sharing measure checked against a headline-level relationship between reported willingness and actual shares on Twitter — partial
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— rules out a hypothetical intention being read as behaviour with no evidence at all - the link is external and at the level of the item
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controls[6]
controlresults also reported excluding the few headlines that were political or that did not contain a clear claim of fact — held
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— rules out a handful of atypical items driving the interaction
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controls[7]
controla preregistration deviation declared in the paper - the plan specified clustering on participant only - with the preregistered version run — held
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— rules out an undisclosed analytic choice; the deviation is named and the preregistered specification is reported not to change the conclusion
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controls[8]
controlthree screener questions embedding a subtle instruction inside a block of text, placed before each of the main tasks — held — rules out participants who click through without reading - on a task about whether people can tell true headlines from false ones
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controls[9]
controla leave-one-out check on the stimulus set: one false headline judged after the fact to be misleading rather than untrue was removed and the analysis re-run — held — rules out that the result rests on one badly-classified item - the paper re-ran without it, found no change, and retained it while saying so
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controls[10]
weightthe inattention-based account of political fake-news sharing, carried here to COVID-19 misinformation where the content is largely apolitical and the stakes are health — 0.95
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claim_relations[0]
weightthe view that partisan motivation is what distracts people from accuracy on social media - the effect appears on apolitical content and is not moderated by partisanship — 0.95
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claim_relations[1]
weightits own intervention - accuracy nudges are far from a complete solution, and the sharing measure is hypothetical — 0.95
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claim_relations[2]
weightthe analytic-cognitive-style literature on truth discernment - CRT scores relate to discernment here in the same pattern previously seen with political headlines — 0.9
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claim_relations[3]
auditpartisanship —distinct-from→ inattention — Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span says partisanship is not the key factor; distinct-from is loose.
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concept_edges[2]
differs in (stated by the tagger, no quotation)partisanship —distinct-from→ inattention — whether the thing pulling attention from accuracy is political identity or the sharing context itselfconcept_edges[2]
relationpartisanship —distinct-from→ inattention — the effect appears on apolitical COVID-19 content, so partisanship is not the key factor distracting people from accuracy
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concept_edges[2]
weightpartisanship —distinct-from→ inattention — 0.95
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concept_edges[2]
outcomecontrol — held
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controls[0]
outcomecontrol — held
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controls[1]
outcomecontrol — held
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controls[2]
outcomecontrol — held
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controls[3]
outcomecontrol — held
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controls[4]
outcomecontrol — held
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controls[5]
outcomecontrol — partial
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controls[6]
outcomecontrol — held
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controls[7]
outcomecontrol — held
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controls[8]
outcome (stated by the tagger, no quotation)control — held
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controls[9]
outcome (stated by the tagger, no quotation)control — held
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controls[10]
limitationthe evidence is restricted to the United States, and the sample is quota matched rather than probability sampled so it is not nationally representative
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limitations[0]
limitationone particular set of thirty headlines was used, and misinformation in other forms - e-mails, text posts, memes - is untested
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limitations[1]
limitationsharing intentions were hypothetical and the induction was performed in a lab context, so extension to naturalistic social media is an inference
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limitations[2]
limitationaccuracy nudges are described by the authors as far from a complete solution
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limitations[3]
limitationone false headline turned out on inspection to be misleading or unverified rather than untrue, and was retained after checking that removing it changed nothing
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limitations[4]
limitationthe preregistration specified clustering standard errors on participant only, and the published analysis clusters on participant and headline
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limitations[5]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
finding
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content_tags.findings[0]
finding
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content_tags.findings[1]
finding
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content_tags.findings[2]
finding
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content_tags.findings[3]
finding
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content_tags.findings[4]
finding
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content_tags.findings[5]
finding
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content_tags.findings[6]
quantitymore people were willing to share a false headline than rated it accurate - the size of the gap between the two questions
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content_tags.quantities[0]
quantityCohen's d for the effect of veracity on accuracy judgments, against 0.121 for sharing intentions
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content_tags.quantities[1]
quantitytimes higher sharing discernment in the treatment condition than the control, from a single accuracy judgement about an unrelated headline
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content_tags.quantities[2]
quantitythe item-level correlation between a headline's perceived accuracy and the size of the treatment's effect on sharing it
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content_tags.quantities[3]
variabletruth discernment
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content_tags.variables[0]
variablesharing intentions
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content_tags.variables[1]
definitiontruth discernment
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content_tags.definitions[0]
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
concepttruth discernment — not defined in this paper's own words; the difference in response between true and false headlines - defined here so that it is a within-person contrast rather than an accuracy rate, which is what lets the two questions be compared
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concepts[0]
conceptinattention — not defined in this paper's own words; the proposed cause - people prefer accuracy and are often able to tell truth from falsehood, and the sharing context focuses attention elsewhere so the preference is never applied
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concepts[1]
conceptaccuracy nudge — not defined in this paper's own words; a single accuracy judgement about an unrelated headline, framed as a pretest, before the sharing task - subtle enough that the participant does not connect it to what follows
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concepts[2]
conceptCRT — not defined in this paper's own words; the Cognitive Reflection Test - six items each built so the intuitive answer is wrong; a measure of the propensity to check an intuition rather than of knowledge
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concepts[3]
conceptdemand characteristics — not defined in this paper's own words; the rival explanation a visible manipulation invites - participants inferring the purpose and complying; the reason this treatment is disguised as a pretest
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concepts[4]
conceptsharing intentions — not defined in this paper's own words; a self-reported willingness to share, validated at headline level against actual Twitter shares and still hypothetical, which the authors list as a limitation
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concepts[5]
conceptpartisanship — not defined in this paper's own words; the distractor the political-misinformation literature had implicated - ruled out here by using apolitical content and finding the same effect
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concepts[6]
conceptquota sampling — not defined in this paper's own words; matching a sample to population margins without random selection - representative on the matched variables and not a probability sample, which the authors say plainly
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concepts[7]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
Cohen
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
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When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

Tag layer © the Meta-Tagging Project, CC BY 4.0. The paper text is © its authors/publisher and is linked, not reproduced. Excerpts shown are short quotations for identification and verification. · back to index · concepts · authors · עברית
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