Journal of Experimental Psychology: General
Moral Deliberation Reduces People’s Intentions to Share Headlines They Recognize as “Fake News”
Daniel A. Effron, Judy Qiu, and Deborah Shulman
Online First Publication, January 5, 2026. https://dx.doi.org/10.1037/xge0001887
CITATION
Effron, D. A., Qiu, J., & Shulman, D. (2026). Moral deliberation reduces people’s intentions to share headlines they recognize as “fake news”. Journal of Experimental Psychology: General. Advance online publication. https://dx.doi.org/10.1037/xge0001887
Journal of Experimental Psychology: General

© 2026 The Author(s)
ISSN: 0096-3445

https://doi.org/10.1037/xge0001887

Moral Deliberation Reduces People’s Intentions to Share
Headlines They Recognize as “Fake News”
Daniel A. Effron1, Judy Qiu2, and Deborah Shulman3
1	Organisational Behaviour Subject Area, London Business School
2	Department of Management, ESSEC Business School
3		School of Psychology, University of East Anglia
Ranked among the most serious global threats, misinformation spreads in part because people share it on social media. Based on theories that people usually share misinformation unintentionally, interventions typically aim to curb misinformation’s spread by helping people distinguish fact from falsehood. However, people sometimes intentionally spread misinformation despite recognizing its falsity. Understanding and curbing this type of sharing requires new theory and tools. Leveraging insights from moral psychology, the present research examines whether people will be more reluctant to share misinformation when they think carefully about its moral implications. Engaging in such moral deliberation, we theorize, leads people to judge misinformation as more unethical to share, which inhibits them from forming intentions to share it. Five experiments (four preregistered, N = 2,509 U.S. and U.K. social media users, including a demographically representative U.S. sample) tested a moral-deliberation procedure in which participants list reasons why it would be ethical or unethical to share different news headlines on social media. This procedure—relative to control conditions that prompted nonmoral deliberation, prompted nondeliberative thinking about morality, or included no prompt—reduced intentions to share fake news about business, health, and politics, even when the news was flagged as false. These effects were (a) larger when the fake news aligned with participants’ politics, (b) reversed for real news, (c) still observed after a delay, and (d) mediated by moral judgments. Our results offer a theoretical foundation for new tools to fight society’s “infodemic” of misinformation.
Public Significance Statement
People sometimes intentionally spread misinformation they do not believe. We find that encouraging social media users to think more carefully about morality—using reason rather than intuition—increases how unethical they think it is to share specific “fake news” headlines and reduces their intentions to share those headlines, particularly when the fake news aligns with users’ politics. Our results shed new light on why misinformation spreads and offers a theoretical foundation for new tools to fight society’s “infodemic” of misinformation. We argue that antimisinformation interventions should not only nudge people to recognize misinformation but also to carefully consider its moral implications.
Keywords: misinformation, fake news, moral psychology, deliberative versus intuitive thinking, social media
Supplemental materials: https://doi.org/10.1037/xge0001887.supp

Kimberly Fenn served as action editor.
Daniel A. Effron https://orcid.org/0000-0002-4122-6175
Judy Qiu https://orcid.org/0000-0002-0933-7086
Deborah Shulman https://orcid.org/0000-0002-1347-7962
Data, analysis code, verbatim study materials, and pre-registrations are posted at https://osf.io/2rtqn. Some of the ideas and data in this article have been presented at the Society of Experimental Social Psychology (2021), the European Association for Consumer Research (2023), the International Association of Conflict Management (2024), the London Business School Moral Psychology and Behavioral Ethics Conference (2024), the Cambridge Disinformation Summit (2025), and several universities’ departmental colloquia.
The authors have no conflicts of interest to disclose. This research was supported by a grant awarded to Daniel A. Effron from the Center for the Science of Moral Understanding at University of North Carolina at Chapel Hill. The authors thank Raunak Pillai, David Rand, and Niro Sivanathan for helpful feedback on earlier drafts and Stephanie Rodriguez and the London Business School Behavioural Lab for research assistance.
Open Access funding provided by London Business School: This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; https://creativecommons.org/licenses/by/4.0). This license permits copying and redistributing the work in any medium or format, as well as adapting the material for any purpose, even commercially.
Daniel A. Effron played a lead role in conceptualization, formal analysis, funding acquisition, methodology, project administration, resources, supervision, and writing–original draft and an equal role in data curation, investigation, and writing–review and editing. Judy Qiu played a supporting role in conceptualization, data curation, investigation, methodology, writing–original draft, and writing–review and editing. Deborah Shulman played a supporting role in data curation, investigation, methodology, and writing–review and editing.
Correspondence concerning this article should be addressed to Daniel A. Effron, Organisational Behaviour Subject Area, London Business School, Regent’s Park, London, NW1 4SA, United Kingdom. Email: deffron@london.edu
Misinformation is ranked among the most serious contemporary global threats (World Economic Forum, 2024) and can influence political behavior, stymie public health efforts, damage corporate reputations, spark violence, and undermine democracy (Cantarella et al., 2023; Di Domenico et al., 2021; Kozyreva et al., 2020; Lewandowsky et al., 2023; Loomba et al., 2021; McKay & Tenove, 2021; Müller & Schwarz, 2021; van der Linden, 2022; Zimmermann & Kohring, 2020). Given misinformation’s potential for societal harm, social scientists have been working to understand its proliferation and potential remedies (e.g., Kozyreva et al., 2024; Lazer et al., 2018; Reyna, 2021).
Misinformation spreads in large part because when people encounter it, they decide to amplify it—for example, by sharing “fake news” they see on social media (Vosoughi et al., 2018). Contemporary theories suggest that people spread misinformation because they either believe it is accurate (for reviews, see Bryanov & Vziatysheva, 2021; Ecker et al., 2022; Lewandowsky et al., 2012) or pay insufficient attention to accuracy (Pennycook & Rand, 2021). In other words, when people spread misinformation, they may not intend to do so. Accordingly, psychologists have designed interventions to help people distinguish fact from fiction and prevent the unintentional spread of misinformation (e.g., Pennycook, Binnendyk, et al., 2021; Roozenbeek & van der Linden, 2019).
Sometimes, however, people do intend to spread misinformation. For example, despite recognizing a news headline as false, people may decide to share it because they find it interesting or politically appealing (Altay et al., 2022; Osmundsen et al., 2021). Understanding and curbing this type of sharing requires new theory and tools. The present research establishes the theoretical foundations of a new approach to fighting intentional misinformation sharing. Five experiments test whether people will be more reluctant to share misinformation when they think carefully about its moral implications. Engaging in such moral deliberation, we theorize, leads people to judge misinformation as more unethical to share, which inhibits them from forming intentions to share it. Thus, to understand why misinformation spreads and what to do about it, we need more than the psychology of belief and attention (Lewandowsky et al., 2012; Pennycook & Rand, 2021); we need the “moral psychology of misinformation” (Effron & Helgason, 2022).
Definitions
Our research investigates people’s inclination to share misinformation, defined by an American Psychological Association consensus statement as “any information that is demonstrably false or otherwise misleading, regardless of its source or intention” (van der Linden et al., 2023, p. 7). By this definition, misinformation encompasses disinformation, which refers to false information intended to deceive (Wardle & Derakhshan, 2017), also known as lies or deception (Levine & Schweitzer, 2015)—but because we lack access to misinformation creators’ intentions, our research does not distinguish between mis- and disinformation. The specific type of misinformation we investigate is fake news, “news content published on the internet that aesthetically resembles actual legitimate mainstream news content, but that is fabricated or extremely inaccurate” based on fact checks (Pennycook & Rand, 2021; van der Linden, 2024). Some examples of fake news constitute propaganda, “news stories which are created by a political entity to influence public perceptions” (Tandoc et al., 2018, p. 10), but other examples do not meet this definition because they are created by private entities for financial gain (e.g., to attract advertising revenue), to create confusion (“troll”), or for other nonpolitical reasons. Moreover, like advertising, some propaganda aims to influence the public by presenting (a biased selection of) facts rather than falsehoods (Tandoc et al., 2018) and thus would not count as fake news.
Limitations of Current Psychological Approaches to Fighting Misinformation
Psychologists have studied three types of interventions to discourage people from spreading misinformation like fake news. Debunking corrects misinformation people have already encountered (Chan et al., 2017; Colliander, 2019; Lazer et al., 2018; Lewandowsky & van der Linden, 2021; Nyhan et al., 2020; Swire et al., 2017). Training teaches people about manipulative techniques, often with the goal of “inoculating” them against misinformation they have yet to encounter (Basol et al., 2020; Guess, Lerner, et al., 2020; Leder et al., 2024; Maertens et al., 2021; Roozenbeek et al., 2022). Accuracy nudges remind people to consider accuracy before deciding to share information (Pennycook, Binnendyk, et al., 2021; Pennycook, McPhetres, et al., 2020; Pennycook & Rand, 2022). These interventions are all based on the idea that people inadvertently share misinformation because they fail to recognize it—whether because they lack the relevant knowledge (addressed by debunking), are unaware of manipulative techniques (addressed by training), or are inattentive to accuracy (addressed by accuracy nudges).
Sometimes, however, people intentionally share misinformation they already recognize as false (Effron & Raj, 2020). Between 14% and 20% of U.S.-based social media users admit to sharing political news they knew was fake at the time (Buchanan, 2020; Littrell et al., 2023; Mitchell et al., 2019), and 17.5% of U.K. social-media users said they would consider sharing a headline they had just identified as false (see Supplemental Material, Vaccari et al., 2023; see also Pennycook & Rand, 2021). Existing psychological interventions, designed to help people recognize misinformation, will be insufficient to curb intentional fake-news sharing.
To address this limitation, we offer a new theoretical perspective on why people intentionally share misinformation, and we test a procedure designed to curb their intentions to do so. Our results shed light on the psychological inhibitors of misinformation sharing and can inform the design of interventions in the fight against society’s current “infodemic” of misinformation (World Health Organization, n.d.).
Theory Development
In overview, we propose that (a) people’s intentions to share misinformation depend in part on how morally wrong they think it is to share (i.e., the severity of their moral judgments), (b) people judge the morality of misinformation sharing less severely when they rely on intuition (their default) than when they deliberate about its moral implications, so (c) prompting moral deliberation should reduce people’s intentions to share misinformation they recognize as false (see Figure 1). Drawing on insights from moral psychology and

Figure 1
Conceptual Model of Moral Judgments and Intentions to Share Misinformation

Note. Intentions to share a piece of misinformation depend in part on the severity of moral judgments, which are formed using two modes of processing: intuition and deliberation. Compared to intuition, deliberation is a less dominant process by default (illustrated by the dotted lines). Encouraging deliberation focuses people on easy-to-articulate reasons why misinformation is unethical to share (examples in bold, orange font), rather than some of the considerations that make misinformation intuitively appealing (examples in nonbold, blue font), resulting in more severe moral judgments of sharing (illustrated by the double vs. single “+” in the arrows) and ultimately reducing sharing intentions. See the online article for the color version of this figure.

behavioral ethics, we explain each proposition in turn, starting on the right side of Figure 1.
Intentions to Share Misinformation Depend on Moral Judgments
When people encounter a piece of misinformation they recognize as false, they may feel torn. On one hand, they may be tempted to share it, perhaps to advance a political agenda (Osmundsen et al., 2021), sow chaos (Petersen et al., 2023), signal their social identity (Van Bavel et al., 2021), or obtain social rewards (Lawson et al., 2023; Ren et al., 2023). On the other hand, they may hesitate to spread the misinformation because they are motivated to preserve a moral self-image and are reluctant to engage in behavior they judge to be unethical (e.g., Bryan et al., 2013; Effron & Conway, 2015; Effron & Helgason, 2023; Mazar et al., 2008; Monin & Jordan, 2009; Shalvi et al., 2011). For example, when they have a financial incentive to lie, people will lie a little bit, but not as much as they could get away with, presumably because they feel that lying is morally wrong (Abeler et al., 2019; Gerlach et al., 2019). People’s willingness to act on a temptation to share misinformation should therefore depend in part on the severity of their moral judgments (e.g., Shalvi et al., 2015). On average, people tend to judge misinformation sharing as moderately unethical, so there is scope to increase the severity of these judgments (Effron & Raj, 2020). For example, when English social media users judged the moral acceptability of sharing COVID-19 news, identified to them as fake, their average rating was 2.98 (SD = 1.82) on a scale from 1 = extremely unacceptable to 7 = extremely acceptable (Joyner et al., 2023, Study 1).
Prior research shows that it is possible to influence how severely people judge misinformation. For example, shifting the attributions people make about the source of a lie (Hildreth & Anderson, 2018; Huppert et al., 2023; Levine & Schweitzer, 2014), or altering various characteristics of the lie itself (Bocian & Wojciszke, 2014; Levine, 2022; Schweitzer & Hsee, 2002), can affect how much moral condemnation people express. Manipulations that reduce moral condemnation of misinformation sharing, in turn, increase people’s intentions to share it themselves (Effron & Raj, 2020; Helgason & Effron, 2022; Joyner et al., 2023).
Thus, theory and research in moral psychology raise the possibility that increasing how severely people judge the morality of sharing misinformation could decrease their inclination to share such content (see Figure 1’s right side). We next consider one approach for increasing these moral judgments’ severity: prompting people to think carefully about the moral implications of sharing.
Moral Deliberation Encourages More Severe Judgments of Misinformation
Moral judgments depend on two modes of processing (Greene et al., 2001; Greene & Haidt, 2002): intuition and deliberation (see Figure 1’s left side). Moral judgments based on intuition depend on how people feel, even if people do not know why they feel that way (Avramova & Inbar, 2013; Haidt, 2001). Moral judgments based on deliberation instead involve explicit reasoning (e.g., Monin et al., 2007), “controlled, conscious, reflective processes” that require cognitive effort (Amit et al., 2014, p. 341). Intuition exerts the dominant influence over moral judgments by default (Gray et al., 2022; Haidt, 2001; Haidt & Kesebir, 2010), and the contexts in which people encounter misinformation are unlikely to push them out of their default intuitive mode (Wahl-Jorgensen, 2018). For example, people are more likely to share moralized content on social media when it appeals to their emotions, suggesting that intuitive responses to such content drive its spread (Brady et al., 2017). However, people can be nudged to reason about morality. Because different moral judgments can result from intuitive versus deliberative processing (Conway & Gawronski, 2013; Cushman et al., 2010; Greene et al., 2008; Slovic & Västfjäll, 2010), encouraging deliberative thinking can alter moral judgments (Paxton et al., 2012; Pizarro et al., 2003; Suter & Hertwig, 2011).
When people encounter a piece of misinformation and are not explicitly prompted to deliberate, their moral judgments about sharing it will likely depend on their feelings. On one hand, their dominant feeling may be negative. After all, given that they have identified the misinformation as false, no deep thinking is required to recognize that sharing it would be dishonest. On the other hand, their feelings may not be strongly negative. One reason is that the misinformation may have characteristics that make it intuitively appealing. For example, the misinformation may be aligned with one’s politics (Galak & Critcher, 2023; Joyner et al., 2023; Mueller & Skitka, 2018), easy to imagine (Effron, 2018; Helgason & Effron, 2022), familiar (Effron, 2022; Effron & Raj, 2020), or “interesting if true” (Altay et al., 2022). Another reason is that sharing the misinformation may seem harmless, because there is rarely an identifiable victim who would suffer if the misinformation were shared (Schein & Gray, 2018; Small et al., 2007). Thus, when people base their moral judgments on intuition, as they are likely to do by default, their feelings may produce the sort of moral judgment expressed by participants in prior research (Effron & Raj, 2020; Joyner et al., 2023): that sharing misinformation would be moderately, but not extremely, unethical (illustrated in Figure 1 with a single “+” in the arrow connecting intuition to perceptions of unethicality).
However, we expect that shifting the basis of these moral judgments from intuition to deliberation will lead people to judge misinformation as even more unethical to share (illustrated in Figure 1 with a double “+” in the arrow connecting deliberation to perceptions of unethicality). The characteristics that make a piece of misinformation intuitively appealing should exert less influence on moral judgments when those judgments result from deliberation. When people deliberate, they pay less attention to their feelings and increase their focus on easy-to- articulate reasons for judgments (Simonson, 1989; Slovic, 1975; Tversky & Shafir, 1992; Wilson et al., 1989). In general, we suggest, people will find it easier to articulate reasons why intentionally sharing misinformation would be unethical versus ethical. One easy-to-artic- ulate reason is that sharing known misinformation is unethical simply because it is dishonest. Deliberation should not be necessary for intentional misinformation sharing to feel dishonest and thus somewhat unethical; rather, deliberation may increase people’s focus on dishonesty, reduce the influence of any intuitively appealing characteristics, and ultimately make the misinformation seem even more unethical to share. Another easy-to-articulate reason is that sharing misinformation is unethical because it could cause harm. Whereas behaviors feel most intuitively immoral when they cause concrete harm to identifiable victims (Schein & Gray, 2018; Small et al., 2007), moral deliberation may help people recognize that misinformation sharing can still cause abstract harm to unknown others, for example, by eroding democratic institutions or reducing trust in health authorities.
Of course, people are also capable of generating reasons why sharing misinformation could be morally justified, and these reasons may be more salient for some pieces of misinformation than for others. However, we suggest that for most misinformation people encounter— particularly among fake news on social media—the easiest-to-artic- ulate reasons tend to be about the unethical aspects of sharing. It is usually simpler and more straightforward to argue that intentionally spreading misinformation is dishonest and harmful than to argue that it upholds a moral duty, benefits society, or is ethical for other reasons.
To summarize, we argue that people are less inclined to knowingly share misinformation when they form more severe moral judgments about it and that more severe moral judgments of misinformation tend to be produced by deliberation rather than intuition.
Prompting Moral Deliberation Will Reduce Intentions to Share Misinformation
This theorizing suggests that encouraging moral deliberation— reasoning about why a behavior is ethical or unethical—should make misinformation seem more unethical to share and constrain people from acting on any sharing temptations. Our theorizing is distinct from the claim in the behavioral ethics literature that simply reminding people about morality can reduce unethical behavior (Ayal et al., 2015; Mazar et al., 2008; Welsh & Ordóñez, 2014; but see Schild et al., 2019; Verschuere et al., 2018). Because people default to basing moral judgments on intuition (Haidt, 2001; Pizarro et al., 2003), moral reminders should be insufficient to prompt moral deliberation. Our hypothesis is also distinct from the idea that deliberative thinking may help people identify misinformation when fake and real news are easily confused (Bago et al., 2020; Martel et al., 2020; Pennycook & Rand, 2019), which may stop them from unintentionally sharing fake news. We propose that when people have already identified misinformation (e.g., because fact-checkers have flagged it), deliberating about morality in particular—as opposed to deliberative thinking in general—will reduce intentions to knowingly share it. When moral intuition is a cause of intentional misinformation sharing, moral deliberation may be a cure.
Predictions
We test our theorizing with a light-touch moral-deliberation procedure that asks participants to list one or two reasons why it would be ethical or unethical to share a particular fake-news headline on social media. To avoid reactance, this procedure does not impose an ethical viewpoint, leaving participants free to decide how ethical or unethical sharing would be. In practice, however, we expect most participants to identify intentional fake-news sharing as unethical after deliberation. As noted, our theorizing predicts that when people know a headline is false, deliberation will focus them on easy-to-articulate reasons why sharing it would be unethical. In this sense, although the wording of the task is ethically neutral, we do not expect participants to take ethically neutral positions in response. Note that our theorizing is agnostic about the philosophical style of reasoning people will engage in when prompted to deliberate. Adopting a deontological style, they could reason that fake news sharing is dishonest, and dishonesty is always wrong—or adopting a utilitarian style, they could reason that sharing causes more harm than benefits. Either way, we expect that people’s deliberation will focus on reasons why fake news is unethical to share.
Reason-listing is a standard manipulation for activating deliberative thinking and reducing intuitive thinking (Albrechtsen et al., 2009; Ambady, 2010; Effron, 2022; Millar & Tesser, 1989; Nordgren & Dijksterhuis, 2009; Wilson et al., 1993; Wilson & Schooler, 1991). The manipulation has high face validity: Deliberation has been conceptualized as “verbal reasoning” (Epstein, 2010), and reason-listing literally encourages people to verbalize reasons. It also has high construct validity: For example, participants performed better on a judgment task that required analytic thinking, but worse on one that required intuition, when they were versus were not instructed to list reasons for their judgment (McMackin & Slovic, 2000).
Our central prediction is that people will be less inclined to knowingly share misinformation when asked to articulate why sharing it would be ethical or unethical, even when they already recognize the misinformation as false. Our experiments compare sharing intentions expressed by participants who complete this moral-deliberation procedure with participants who do nothing (pure control condition), deliberate about a nonmoral topic (control-deliberation condition), or are simply reminded to form a moral judgment without being prompted to deliberate (moralreminder condition). None of these control conditions include both key features of our moral-deliberation procedure—deliberating about morality—so we expect weaker intentions to share fake content in the moral-deliberation condition than any of the other conditions.
Our theorizing also suggests that two key factors will moderate this effect of moral deliberation. First, moral deliberation should not reduce inclinations to share information people regard as true. We propose that when people carefully think about the moral implications of sharing fake news, they will judge such sharing as more unethical and be less inclined to share it. By contrast, this process should not play out with real news, because people usually perceive truth-sharing as morally unproblematic. If anything, moral deliberation might increase real news sharing by fostering feelings of moral obligation to spread the truth. By reducing fake-news sharing and not real-news sharing, moral deliberation should thus increase sharing discernment, defined as inclinations to share true relative to false information (Pennycook, McPhetres, et al., 2020). Such a finding would be important, because an intervention that inhibits the spread of true information could do more harm than good (Guay et al., 2023; Modirrousta-Galian & Higham, 2023).
Second, our theorizing suggests that moral deliberation should be most effective at reducing people’s inclination to share misinformation that feels intuitively appealing (vs. unappealing), such as fake news that aligns with their politics. Encouraging moral deliberation should have little effect on intuitively unappealing misinformation (e.g., politically misaligned fake news), as people should not be particularly tempted to share such content to begin with. Support for these predictions would be consequential, because people tend to encounter and spread politically congenial misinformation (Guess, Nyhan, & Reifler, 2020; Osmundsen et al., 2021).
Alternative Predictions
It is important to test whether moral deliberation can reduce intentions to share misinformation, because alternative results are theoretically plausible. One alternative is that the reasons people generate when prompted to deliberate could simply reflect their intuitions without influencing their moral judgments or sharing intentions (see Haidt, 2001). However, this result seems unlikely because, although intuition may drive moral judgments by default, shifting people between intuitive and deliberative modes of thinking has been shown to affect their moral judgments (e.g., Effron, 2022; Paxton et al., 2012; Pizarro et al., 2003; Suter & Hertwig, 2011).
Another alternative is that, when prompted to deliberate, people could rationalize spreading intuitively appealing misinformation, thereby increasing their intentions to share such misinformation. For example, adopting a utilitarian perspective, people might reason that sharing fake news constitutes “harmless fun” or communicates a “broader truth” (Hahl et al., 2018; Langdon et al., 2024) and is thus morally acceptable. Or, adopting a deontological perspective and prioritizing the moral value of ingroup loyalty, people might reason that they have a duty to share fake news that advances a favored political cause regardless of its consequences. However, the possibility that deliberating about misinformation could increase motivated reasoning (Kahan, 2013; Kahan et al., 2017) lacks evidence (see Pennycook, 2023). On the contrary, deliberating about misinformation appears to increase people’s efforts to form accurate beliefs instead of to defend views they find politically congenial (Bago et al., 2020, 2023; Strömbäck et al., 2024; see also Pennycook & Rand, 2019; Tappin et al., 2021). For these reasons, we did not predict this alternative pattern of results.
Overview of Studies
Five experiments suggest that moral deliberation reduces inclinations to share fake news, but not real news, particularly when it is aligned (vs. misaligned) with one’s politics (see Table 1). Studies 1a and 1b tested the main effect of our moral-deliberation procedure on intentions to share fake business news. Study 2 examined whether these findings would generalize to fake news about COVID-19, recruited a demographically representative U.S. sample, and tested whether moral deliberation had a larger effect on intentions to share content that was aligned (vs. misaligned) with participants’ politics. This study also explored whether more severe moral judgments mediate moral deliberation’s effect on sharing intentions. Using different control conditions and different sets of political news, Studies 3 and 4 examined whether moral deliberation would increase sharing discernment (i.e., inclinations to share real news relative to fake news). Study 3 also examined whether the effect of moral deliberation could endure for a short duration. The fake-news headlines in all studies were sampled from fact-check websites, ensuring that the stimuli our participants judged reflect the types of misinformation that people actually encounter on social media (see Pennycook, Binnendyk, et al., 2021, for a discussion).
Because our theorizing pertains to social media users’ inclinations to knowingly share fake news, most of our studies explicitly informed participants which headlines were fake. Thus, unlike studies on accuracy nudges, which manipulate accuracy salience (Pennycook, Binnendyk, et al., 2021), our studies make inaccuracy salient to all participants. The exception is Study 3, which tested whether moral deliberation would reduce sharing of news that, though easily identifiable as fake, was not explicitly labeled as such. We also tested in each study whether our findings were robust when we excluded participants who misjudged the news’ accuracy. After Study 1a, we
Study	Sample N (country)	News topic	News veracity	Fake news label?	Political news type	Control conditions	Dependent measures	Other feature	Key result	
la	105 (United Kingdom)	Corporate wrongdoing	Fake	Yes	n/a	Control deliberation	Sharing intentions	n/a	• Moral deliberation reduced sharing intentions	
lb	311 (United Kingdom)	Corporate wrongdoing	Fake	Yes	n/a	Control deliberation No deliberation	Sharing intentions	n/a	• Moral deliberation reduced sharing intentions relative to both controls	
2	861 (United States; demographically representative)	COVID-19	Fake	Yes	Aligned versus misaligned with participants’ politics versus politically neutral	Control deliberation Moral reminder	Sharing intentions Moral judgments	Tests mediation	•		Moral deliberation reduced sharing intentions relative to each control...
•		... especially when news aligned with participants’ politics
•		Moral judgments mediated moral deliberation’s effect	
3	634 (United States)	U.S. politics (stimulus
Set 1)	Fake + real	No	Aligned versus misaligned with participants’ politics	Control deliberation	Sharing intentions “Liking” intentions	Tests whether effects endure after the deliberation procedure	•		Moral deliberation reduced intentions to share fake news ...
•		... not real news
•		... especially when the news aligned with participants’ politics
•		These effects endured over a short interval
•		Measure of “liking” intentions showed similar results	
4	598 (United States)	U.S. politics (stimulus
Set 2)	Fake + real	Yes	Aligned versus misaligned with participants’ politics	No deliberation	Sharing intentions	Tests moderation by individual differences	•		Moral deliberation reduced intentions to share fake news ...
•		... not real news
•		... especially when the news aligned with participants’ politics
•		No moderation by cognitive reflection or need for chaos	
Note.	Each study included a	moral-deliberation condition.	“News	veracity” column indicates whether the study included just fake news	or both fake and real	news as stimuli, n/a = not applicable.	
Table 1
Overview of the Present Research
EFFRON, QIU, AND SHULMAN

exclusively sampled the relevant population: people who sometimes share news on social media.
Like most contemporary misinformation research, our studies examine sharing intentions in lab-style experiments rather than sharing behaviors in the field. Assessing sharing intentions is an important first step for testing our theorizing about why people intentionally share misinformation. Although people do not always act on intentions (Ajzen, 1991, 2020; Sheeran, 2002), intentions are stronger and more proximal predictors of behavior than are other psychological constructs like attitudes, social norms, and personality (Fishbein & Ajzen, 2011; Sheeran & Webb, 2016). Moreover, sharing intentions expressed in surveys correlate with actual sharing behavior on social media (Arechar et al., 2024; Mosleh et al., 2020). Thus, sharing intentions are a useful, if imperfect, proxy for online sharing behavior, and studying them can offer psychological insights into how people make decisions about spreading misinformation.
Transparency and Openness
We preregistered all studies after Study 1a, flag the handful of minor deviations from the preregistered plans, and report all conditions, measures, a priori sample-size targets, and data exclusions. Data, analysis code, verbatim study materials, and preregistrations are posted on our Open Science Framework page at https://osf.io/ 2rtqn (Effron et al., 2025). The studies appear in non-chronological order for narrative clarity, but we report the date on which each study was conducted. All studies received approval from the Research Ethics Committee at London Business School (Protocol No. 722).
Study 1a
Study 1a tested whether encouraging deliberation about morality versus about a control topic would reduce inclinations to share fake business news.
Method
This study was not preregistered.
Participants
In August of 2019, we recruited 105 participants on Prolific (https://www.prolific.com/) who provided 1,098 responses in the repeated-measures design (72 women, 21 men, and two nonbinary people provided demographics; Mage = 35 years, SD = 13). For sample-size justification and details of a priori data exclusions, see Supplemental Material.
Stimuli
Study 1a’s stimuli were 11 fake-news headlines, accompanied by relevant pictures, about corporate wrongdoings, such as, “A United Airlines flight attendant slapped a 7-month-old baby in the face for crying during a flight.” These headlines, which had actually circulated on social media, were originally sampled from a fact-checking website for prior research (Effron, 2022). See Supplemental Material for details.

Procedure and Manipulation
To test our hypothesis that moral deliberation would reduce people’s inclination to intentionally share misinformation they recognize as false at the time, the study began by warning participants that all the news in the study was fake, debunked by third-party fact-checkers. Participants then viewed the 11 headlines in a fixed order.
Participants randomly assigned to the moral-deliberation condition were asked for each headline to write two reasons why they “believe sharing this headline on social media would be ethical or unethical” before rating how unethical such sharing would be (0 = not at all unethical to 100 = extremely unethical). Note this task did not state an ethical viewpoint, instead allowing participants to judge the ethics of sharing for themselves. The writing task is theoretically important because it encourages people to think about and articulate reasons for a moral judgment (i.e., to deliberate). The rating task was part of the manipulation, not a dependent measure.
Participants in the control-deliberation condition were instead asked to write, for each headline, two reasons why they “believe sharing this headline on social media would be interesting or boring,” before rating how boring such sharing would be (0 = not at all boring to 100 = extremely boring). Thus, both conditions involved deliberation, but only the first prompted deliberation about morality.
Dependent Measure
Participants completed the manipulation and the dependent measure for each headline before moving to the next headline. The dependent measure assessed sharing intentions: “If you came across this headline, how likely would you be to share it on social media (e.g., through Facebook or Twitter)?” (1 = not at all likely to 7 = extremely likely, with the midpoint labeled somewhat likely).
At the end of the study, participants provided demographics and read a debriefing form, which reminded them that all the news stories had been fake.
Results and Discussion
We expected that participants in the control condition would be at least somewhat inclined to share these headlines, perhaps because the corporate wrongdoings seemed “interesting-if-true” (Altay et al., 2022), and that moral deliberation would reduce this inclination. Consistent with this expectation, participants were less inclined to share the headlines in the moral-deliberation condition than in the control-deliberation condition (Ms = 1.75 and 2.68, respectively; 95% CIs [1.36, 2.14] and [2.31, 3.05]), b = -0.93, t(103.23) = -3.99, p < .001, d = -.78 in a mixed regression model (see Figure 2, Table 2, and Supplemental Material’s section “General Approach to Data Analysis”). These results are consistent with our main hypothesis but cannot identify which condition drives the effect. To resolve this ambiguity and replicate Study 1a’s results, we next conducted a preregistered study that added a neutral control condition.
Study 1b
Method
We preregistered Study 1b’s hypotheses, design, and analyses in August 2019 at https://aspredicted.org/5VZ_MJH.

Figure 2
Study 1a: Mean Sharing Intentions (± Standard Errors) and Distributions by Condition

Note. See the online article for the color version of this figure.

Control Deliberation Moral Deliberation
Participants
Participants were 311 Prolific users who indicated on a pretest that they share news on social media at least once per month and who provided 3,326 total observations. Of the 297 participants who provided demographic information, 207 were women, 87 were men, two were nonbinary, and one did not report gender (Mage = 35 years, SD = 12). (For exclusions and sample-size justification, see Supplemental Material.)
Stimuli
The stimuli were 22 fake-news headlines about corporate wrongdoing: the same 11 headlines used in Study 1a, plus an additional 11 that we added to improve generalizability (see https://osf.io/2rtqn).
Procedure
Study 1b followed the same procedure as Study 1a but added a no-deliberation condition. All participants first saw a warning that the headlines in the study were “fake news” and were then shown 11 headlines randomly selected from the bank of 22 (presented in random order). For each headline, participants randomly assigned to the moral-deliberation and the control-deliberation conditions completed the relevant rating and writing tasks from Study 1a and indicated their intention to share the headline on social media (dependent variable). Those randomly assigned to the no-deliberation (neutral control) condition simply indicated their intention to share each headline without completing any rating or writing task. At the end of the study, participants were asked whether they thought the headlines were all true, all false, or a mix of true and false and indicated which they thought were true (if any). Finally, they provided demographics and read a reminder that all headlines had been fake.
Analytic Approach
We submitted the sharing-intentions measure to a mixed regression model with planned, orthogonal contrasts for condition as fixed effects: contrast1 compares the moral-deliberation condition (coded 2) to the average of the two control conditions (both coded -1), whereas contrast2 compares the two control conditions to each other (controldeliberation coded 1, no-deliberation coded -1). We predicted that sharing intentions would be significantly lower in the moral-deliberation condition than in the other two conditions, which would be supported by a significant negative coefficient on contrast1. We were agnostic about any difference between the two other conditions. The model’s random-effect structure was determined following the “General Approach to Data Analysis” in the Supplemental Material. To improve statistical power, we preregistered and report our directional hypothesis test as one-tailed (Hales, 2024), but a two-tailed test produced identical conclusions.
Results and Discussion

Table 2
Study 1a: Regression Results

Effect	b	SE	t	p	
Fixed effects
Condition	-0.93
Intercept	2.68
Random effects
Participant intercept	variance	1.25
Headline intercept variance	0.10
Residual variance	1.58	0.23	-3.99	<.001
0.19	14.47	<.001
1.12
0.32
1.26	

Note. Condition is coded as 1 = moral-deliberation, 0 = control-deliberation. The model specifies the maximal random-effects structure that would converge. SE = standard error.

Tables 3 and 4 present the regression results and estimated marginal means. Supporting our preregistered prediction (Figure 3), participants were less inclined to share fake news in the moraldeliberation condition than in the two control conditions (averaged), b = -0.19, t(239.36) = -3.60, p < .001, d = -.44, for contrast1. In contrast, the no-deliberation and control-deliberation conditions did not differ significantly from each other, b = –0.04, t(240.67) = –0.48, p = .629, d = –.06, for contrast2. Additional analyses showed significantly lower sharing intentions in the moraldeliberation condition compared to each of the control conditions individually (see Supplemental Material). The study’s conclusions were identical when we excluded the 4% of observations where people misidentified a headline as true (preregistered robustness check; see Supplemental Material).

Effect	Main analysis bSEt	p	
Fixed effects
Contrast 1
Contrast 2
Intercept
Random effects
Participant intercept variance
Headline intercept variance
By-headline slope variance for Contrast 1
By-headline slope variance for Contrast 2
Residual variance	–0.19	0.05	–3.60	<.001
–0.04	0.09	–0.48	.629
2.60	0.12	21.80	<.001
1.44	1.20
0.20	0.44
0.003	0.06
0.05	0.07
2.09	1.45	

Note. Contrast 1 compares moral deliberation (=2) to the average of control deliberation and no deliberation (=-1). Contrast 2 compares control deliberation (=1) to no deliberation (=-1). p-value in bold reflects a preregistered onetailed test but remains significant with a two-tailed test. The random-effects structure reported is the maximal one that would converge. SE = standard error.

Along with Study 1a, Study 1b thus suggests that moral deliberation can reduce people’s inclination to share misinformation that they know is false. These findings cannot be explained by the possibility that our control-deliberation task increased inclinations to share misinformation, because that task did not significantly affect sharing intentions relative to a neutral control condition.
Study 2
To test generalizability, Study 2 recruited a demographically representative U.S. sample to rate fake headlines about health and tested whether the effect of moral deliberation would be more pronounced among headlines aligned with participants’ politics.
We also tested the alternative explanation that our procedure reduced intentions to share fake news, not because it prompted deliberation but because it reminded people about the moral relevance of misinformation sharing. Asking participants about morality could act as a behavioral prime (Ayal et al., 2015; Mazar et al., 2008) or signal that the researchers found misinformation sharing immoral (i.e., demand characteristics). Study 2 added a moral-reminder condition, which the alternative explanation predicts will have the same effect on sharing intentions as moral deliberation.
According to our theorizing, moral deliberation affects sharing intentions by making fake news seem less morally acceptable to spread. We thus tested whether moral judgments would mediate the effect of moral deliberation.

Method
We preregistered Study 2’s hypotheses, design, and analyses in October 2021 at https://aspredicted.org/8SH_B6J.
Participants
We recruited 861 participants (440 Democrats and 421 Republicans; 415 women, 443 men, two nonbinary people, one unknown gender) from Lucid, a panel company, and used quota matching so that participants’ age, race, and geographical dispersion approximated the U.S. population. (For details about data exclusions, quality controls, and sample-size justification, see Supplemental Material).
Stimuli

Condition	Main analysis	Robustness check
M	95% CI	M	95% CI	
Moral deliberation Control deliberation
No deliberation	2.21a	[1.91,	2.51]	2.15a	[1.83,	2.47]
2.75b	[2.44,	3.07]	2.76b	[2.45,	3.06]
2.84b	[2.52,	3.15]	2.80b	[2.49,	3.10]	

Table 4
Study 1b: Estimated Marginal Means

Note. Means within the same column that have different subscripts differ at p < .05, two-tailed. We preregistered some tests as one-tailed and report these in the main text. CI = confidence interval.

Study 2’s participants rated 12 fake-news headlines about COVID-19, randomly selected for each participant from a bank of 18, in which pretesting showed six appealed to Democrats, six appealed to Republicans, and six were politically neutral (see Supplemental Material). To make inaccuracy salient, we labeled each headline with a red exclamation mark and “Disputed by Multiple, Independent Fact-Checkers.” These headlines, which had actually circulated on social media, were originally sampled by a different research team using methods to “allow generalization from the study to the real-world phenomenon of online misinformation” (Pennycook, Binnendyk, et al., 2021, p. 3).

Figure 3
Study 1b: Mean Sharing Intentions (± Standard Errors) and Distributions by Condition

Note. See the online article for the color version of this figure.

Control Deliberation
No Deliberation
Moral Deliberation
Procedure
Participants first provided demographic information, including political affiliation, and indicated how frequently they shared news on social media. Then, as in Studies 1a and 1b, they were warned that all the headlines they would rate in the study were fake. Next, under one of three randomly assigned conditions, participants saw 12 headlines presented in randomized order.
In the moral-deliberation condition, participants wrote two reasons why it would be ethical or unethical to share each headline and then rated how ethical or unethical such sharing would be (1 = completely unethical, 7 = completely ethical). Note again that this procedure does not impose an ethical viewpoint on participants, who are free to identify sharing a headline as ethical. The rating task reminds people about morality by prompting them to form a moral judgment, and the writing task prompts deliberation by requiring them to articulate reasons for their judgment. To disentangle the reminder from deliberation, the moral-reminder condition asked participants to rate how ethical or unethical it would be to share each headline without writing reasons why. Lacking an explicit prompt to deliberate, participants in this condition are likely to base these judgments more on intuition than on deliberative thinking (Haidt, 2001; Haidt & Kesebir, 2010). Note that the moral-reminder condition provides a conservative test of our predictions because it does not prevent people from deliberating. Because both the moral-deliberation condition and the moral-reminder condition measured moral judgments, we could also test mechanism by examining whether such judgments mediate the effect of moral deliberation (vs. a moral reminder) on sharing intentions. In the control condition, participants did not deliberate about or rate the ethicality of sharing.
At the end of the study, participants were asked whether they thought the headlines were all true, all false, or a mix of true and false and indicated which they thought were true (if any). Last, they provided demographics and read a final reminder that all the headlines were false.
Dependent Measure
Participants were exposed to the manipulation and completed a sharing-intentions measure for each headline before moving to the next one. The wording was slightly different than in Studies 1a and 1b: “If one of your acquaintances shared this headline on social media, how likely would you be to share it yourself?” (1 = not at all likely to 7 = extremely likely, with midpoint 4 = somewhat likely).
Analytic Approach
As preregistered, we submitted the dependent measure to a mixed model including dummy-coded fixed effects for the three between- participants conditions with the moral-deliberation condition as the reference group. (We computed the dummy codes as preregistered, but the preregistration document mislabeled the control condition as the reference group). We coded a headline as “aligned” (1) if it appealed to the political party with which participants identified (i.e., Democrat vs. Republican), “misaligned” (-1) if it appealed to the other party, and “neutral” (0) if it appealed to neither party. We controlled for alignment in an initial model testing the main effect of moral deliberation and then added interaction terms to test moderation by alignment. The model’s random-effect structure was determined following the “General Approach to Data Analysis” in the Supplemental Material. To improve statistical power, we preregistered and report one-tailed tests of directional predictions (Hales, 2024), but except where noted, two-tailed tests support identical conclusions. Tests of nonpreregistered predictions are flagged and reported as two-tailed.
Results
Moral Deliberation Reduced Sharing Intentions More Than a Moral Reminder
As predicted, participants said they were less likely to share the fake news in the moral-deliberation condition (M = 2.63, 95% CI [2.39, 2.87]) than in the control condition (M = 3.06, 95% CI [2.83, 3.30]), b = 0.44, t(859.44) = 2.83, p = .002, d = -0.24, and— contrary to the alternative explanations—the moral-reminder condition (M= 2.89, 95% CI [2.66, 3.13]), b = 0.27, t(861.05) = 1.74, p = .041, d = –0.15 (see Table 5, Model 1). (Note that the latter effect was only significant by our preregistered one-tailed test, but as described below, was significant by both one- and two-tailed tests when the news aligned with participants’ politics.
Moral Deliberation Hada Larger Effect Among Politically Aligned Headlines
Also as predicted, the effects of moral deliberation were larger when the fake news was aligned, versus misaligned, with participants’ politics (see Figure 4 and Table 6). Relative to the control condition, moral deliberation reduced sharing intentions more effectively among aligned headlines (d = -.29, p < .001; one-tailed) than among misaligned headlines (d = -.17, p = .035; two-tailed), as shown by a significant interaction (Table 5, Model 2) between headline alignment and the dummy code comparing moral deliberation to control, b = 0.10, t(849.40) = 2.41, p = .008 (one-tailed). Relative to the moral-reminder condition, moral deliberation also reduced sharing intentions more effectively among aligned headlines (d = –.18, p = .014; one-tailed) than among misaligned headlines (d = -.09, p = .251; two-tailed), as shown by a significant

Effect	Model 1: Main effect	Model 2: Interaction	
	b	SE	t	p	b	SE	t	p	
Fixed effects									
Control condition	0.44	0.15	2.83	.002	0.44	0.15	2.83	.005	
Moral reminder condition	0.27	0.15	1.74	.041	0.27	0.15	1.73	.084	
Political alignment	0.12	0.15	7.65	<.001	0.06	0.04	1.42	.165	
Alignment × Control					0.10	0.04	2.41	.008	
Alignment × Moral Reminder					0.08	0.04	2.02	.022	
Intercept	2.63	0.12	21.42	<.001	2.63	0.12	21.44	<.001	
Random effects									
Participant intercept variance	3.23	1.80			3.23	1.80			
Headline intercept variance	0.05	0.23			0.05	0.23			
By-participant slope variance for alignment					0.04	0.20			
By-headline slope variance for alignment					0.01	0.11			
Residual variance	1.49	1.22			1.46	1.21			

Table 5
Study 2: Regression Results

Note. Condition is dummy coded with moral deliberation as the reference group. p-values in bold reflect preregistered one-tailed tests. The model specifies the maximal random-effects structure that would converge. SE = standard error.

interaction between headline alignment and the dummy code comparing the moral deliberation and reminder conditions, b = 0.08, t(843.39) = 2.02, p = .022 (one-tailed; for additional analyses of simple slopes, see Supplemental Material). Contrary to the alternative explanations, exploratory analyses showed that a moral reminder was not sufficient to significantly reduce sharing intentions relative to control even when the news fit with participants’ politics (d = -.10, p = .235). (As noted, the one-tailed tests were preregistered to assess directional predictions, whereas the two-tailed tests assessed comparisons about which we did not make directional predictions; adopting only one-tailed or only two-tailed tests did not alter these analyses’ conclusions.)
Moral Judgments Mediated Moral Deliberation’s Effect
Our theorizing predicts that moral deliberation reduces fake-news sharing by making people judge fake news as more unethical to share. Study 2 can test this prediction because both the moral-

Figure 4
Study 2: Mean Sharing Intentions, ± Standard Errors, by Conditions and Alignment With Participant’s Politics

Note. See the online article for the color version of this figure.

Condition	Aligned	Neutral	Misaligned
M	95% CI	M	95% CI	M	95% CI	
Moral deliberation
Moral reminder
Control	2.69a	[2.43,	2.95]	2.63a	[2.39,	2.87]	2.57a	[2.32,	2.82]
3.04b	[2.78,	3.29]	2.89ab	[2.66,	3.13]	2.75ab	[2.51,	2.99]
3.22b	[2.96,	3.48]	3.06b	[2.83,	3.30]	2.91b	[2.66,	3.15]	
Table 6
Study 2: Estimated Marginal Means by Political Alignment
Note. Means within the same column differ at p < .05, two-tailed, unless they share a subscripted letter. We preregistered some tests as one-tailed and report these in the main text. CI = confidence interval.
deliberation and the moral-reminder conditions measured moral judgments before participants indicated their intentions to share each headline. Consistent with our theorizing, participants thought it was less ethical to share the headlines in the moral-deliberation condition (coded 0; M = 2.91, 95% CI [2.67, 3.15]) than in the moral-reminder condition (coded 1; M = 3.24, 95% CI [3.00, 3.48]), b = -0.33 [-0.39, -0.27], and these moral judgments significantly mediated the effect of moral deliberation (vs. moral reminder) on sharing intentions (b = -0.21, 95% CI [-0.25, -0.17]; see Figure 5; see Supplemental Material for more details about this nonpreregistered analysis).
Robustness Check
The results were substantively similar when we reran the analyses retaining only the 80% of observations where participants correctly identified headlines as false (see Supplemental Material). That is, we still observed significantly lower sharing intentions in the moraldeliberation condition than in either the moral-reminder or control conditions when headlines were aligned with participants’ politics, and these effects were significantly attenuated among misaligned headlines.
No Moderation by Political Party
Exploratory analysis found no evidence that participants’ political party moderated the effect of moral deliberation on sharing intentions (see Supplemental Material).
Figure 5
Study 2: Moral Judgments Mediating the Effect of Moral Deliberation (vs. a Moral Reminder) on Intentions to Share Fake News
–.33
[–.39, –.27]
Perceived
ethicality of
sharing
.64
[.62, .66]
Moral
deliberation (1)
vs. moral
reminder (0)
Direct effect:
.06
[.006, .102]
Intention to
share
Indirect effect: -0.21 [-0.251, -0.17]
Note. The direct effect is the residual effect of moral deliberation (vs. a moral reminder) on sharing intentions controlling for the perceived ethicality of sharing. See the online article for the color version of this figure.
Discussion
Extending Studies 1a and 1b, Study 2 showed that encouraging people to deliberate about morality—above and beyond merely reminding them about morality—reduced their intentions to share fake news, especially when it was aligned (vs. misaligned) with their politics. Consistent with our model, moral deliberation prompted more severe moral judgments, which mediated the effect of deliberation versus a reminder. These findings reduce concerns that moral deliberation merely signals that the researchers find misinformation sharing immoral (i.e., demand characteristics), because both the moral deliberation and moral reminder conditions instructed participants to consider morality before indicating their sharing intentions.
Study 3
Together, Studies 1a–2 show that completing our moral-deliberation procedure can reduce inclinations to knowingly share fake news, relative to completing a control-deliberation task, to seeing a reminder about the moral relevance of fake news, and to doing nothing—especially when the news is aligned with one’s politics. For an antimisinformation intervention to be effective, however, it must selectively reduce people’s inclinations to share false content rather than all content (Guay et al., 2023; Modirrousta-Galian & Higham, 2023; for a nuanced discussion, see van der Linden, 2024). Studies 3–4 thus tested whether moral deliberation would reduce intentions to share fake news more than real news (i.e., increase sharing discernment).
Because we theorized that moral deliberation reduces people’s inclination to share news they know is fake, Studies 1a–2 explicitly flagged the news as fake. To test generalizability to environments in which false content is not labeled, Study 3 omitted such labels. We still expected the moral-deliberation procedure to be effective, however, because people are adept at distinguishing between unlabeled real and fake news (Pennycook, Binnendyk, et al., 2021). Given that people recognize unlabeled fake news as such, moral deliberation should focus on them on easy-to-articulate reasons why sharing would be unethical.
Study 3 also addressed a limitation of our sharing-intentions measures. Perhaps some participants intended to share fake news with the goal of condemning it or alerting people to its falsity. Study 3 thus measured intentions to “like” a fake-news post on social media. Similar to sharing, “liking” misinformation can contribute to its spread, because it signals approval of the post to other users and because social media algorithms preferentially display widely liked content. At the same time, clicking the “like” button is a less-ambiguous indication than

sharing that the user approves of posting or spreading the content. We expected moral deliberation to affect “liking” intentions in the same way as sharing intentions.
Finally, Study 3 tested whether moral deliberation’s effect could endure for a short period.
Method
We preregistered Study 3’s hypotheses, design, and analyses in February 2020 at https://aspredicted.org/171_LJH.
Participants
We recruited U.S.-based participants from Prolific who reported in a pretest that they shared news on social media at least once per month. Our final sample contained 634 participants (324 Democrats and 310 Republicans) and 15,216 observations. Of the participants who reported demographic information, 302 were women, 277 were men, and six were nonbinary (Mage = 38 years, SD = 13). (See Supplemental Material for exclusion criteria and sample-size justification.)
Stimuli
The stimuli were 24 news headlines about U.S. politics, originally sampled by a different research team from actual news headlines to reflect the type of content people encounter on social media (Pennycook, Binnendyk, et al., 2021). Half of the headlines were real news and half were fake news; half aligned with Democrats’ views and half aligned with Republicans’ views. In addition to the headline text and a relevant photograph, each stimulus identified the news source. The fake news was not labeled, but we still expected participants to accurately distinguish between real and fake news, in part because the true news was all from mainstream sources (e.g., https://reuters.com), whereas the fake news was all from nonmainstream sources (e.g., https://americadaily.com), and in part because the true news was more plausible than the fake news (e.g., “President Trump pledges ‘strong action today’ on immigration” [true] vs. “Trump wants to deport all American Indians to India” [false]).
Procedure
Study 3 used a 2 (condition: moral-deliberation vs. controldeliberation; between-participants) × 2 (real vs. fake news headline; within-participants) × 2 (political alignment; within-participants) design. Participants saw the 24 headlines divided and counterbalanced across two blocks containing 12 headlines each (six real and six fake; six politically aligned and six misaligned).
For each headline in the first block, participants responded to either the moral-deliberation or the control-deliberation prompt from Studies 1a and 1b (randomly assigned at the participant level), followed by the dependent measures (see below). Then in the second block, participants saw the remaining set of 12 headlines and responded again to the dependent measures without any additional deliberation prompts. The purpose of the second block was to test whether the effect of moral deliberation could last over a short duration and generalize to a new set of headlines.
Finally, participants indicated whether they thought each of the headlines was accurate using a forced-choice measure. Then participants reported on their demographics, social media usage, and political preferences. The debrief showed participants the true headlines again and noted that any other headlines shown in the study were false.
Dependent Measures
The main measure was the same sharing-intentions item described in Study 2. Participants also indicated their inclination to “like” each headline on social media if an acquaintance shared it on social media (1 = not at all likely to 7 = extremely likely, with midpoint 4 = somewhat likely).
Results
Moral Deliberation Increased Sharing Discernment
As predicted, moral deliberation increased sharing discernment, as indicated by a significant interaction between condition (moral deliberation = 1, control-deliberation = -1) and news type (fake = 1, real = -1) collapsed across the two headline blocks, b = -0.19, t(79.40) = -7.75, p < .001, in a mixed regression model, with random effects determined following “General Approach to Data Analysis” in the Supplemental Material (see Table 7, Model 1). Simple slopes showed that moral deliberation reduced fake-news sharing intentions, b = -0.23, t(370.99) = 3.97, p < .001, d = -.34, but increased real news sharing intentions, b = 0.14, t(328.77) = 2.59, d = .25, p = .010 (see Table 8 for estimated marginal means). (Because we only preregistered a directional prediction about the former test, the latter test is reported two-tailed.) The effect on real news sharing may reflect moral deliberation making people feel obligated to spread truth.
Moral Deliberation Hada Larger Effect Among Politically Aligned Headlines
Consistent with Study 2, these effects were significantly more pronounced when the headlines were aligned with participants’ politics, b = -0.06, t(606.95) = -3.93, p < .001, for the three-way interaction between political alignment (aligned = 1, misaligned = -1), news type (real vs. fake), and deliberation condition (see Table 7, Model 2). We did not preregister predictions about this interaction or the simple slopes we report next, and thus the significance tests are two-tailed.
Simple slopes showed that, among politically aligned headlines, moral deliberation decreased fake-news sharing, b = -0.31, t(485.05) = -4.57, p < .001, but increased real news sharing, b = 0.18, t(480.71) = 2.58, p = .010 (see Table 9). Among politically misaligned headlines, moral deliberation decreased fake-news sharing about half as much as among politically aligned headlines, b = -0.16, t(378.60) = -2.86, p = .004, and only marginally increased real news sharing, b = 0.10, t(337.76) = 1.84, p = .067.
Effect	Model 1: Two-way interaction	Model 2: Three-way interaction	
	b	SE	t	P	b	SE	t	P	
Fixed effects									
Fake news	-0.15	0.07	-2.17	.040	-0.14	0.07	-2.14	.042	
Political alignment					0.35	0.04	9.46	.001	
Moral deliberation condition	-0.04	0.05	-0.87	.384	-0.05	0.05	-0.98	.327	
Fake X Moral Deliberation	-0.19	0.02	-7.75	<.001	-0.19	0.02	-7.80	<.001	
Alignment Χ Moral Deliberation					-0.02	0.02	-0.81	.418	
Fake Χ Alignment					-0.04	0.03	-1.33	.197	
Fake Χ Alignment Χ Moral Deliberation					-0.06	0.01	-3.93	<.001	
Intercept	2.30	0.08	28.47	<.001	2.30	0.08	28.47	<.001	
Random effects									
Participant intercept variance	1.43	1.20			1.45	1.20			
Headline intercept variance	0.10	0.31			0.10	0.31			
By-participant slope variance for news type	0.17	0.41			0.19	0.43			
By-participant slope variance for alignment					0.23	0.48			
By-Participant Slope Variance for					0.06	0.24			
Alignment Χ News Type									
By-headline slope variance for condition	0.004	0.06			0.004	0.06			
By-headline slope variance for alignment					0.02	0.15			
Residual variance	2.04	1.43			1.57	1.25			
Table 7
Study 3: Regression Results for Sharing Discernment
Note. News type coded as fake = 1, real = —1. Condition coded as moral deliberation = 1, control = -1. Political alignment coded as aligned = 1, misaligned = —1. p-values in bold reflect preregistered one-tailed tests. The model specifies the maximal random-effects structure that would converge. SE = standard error.
EFFRON, QIU, AND SHULMAN

Fake	Real	
Condition	M	95% CI	M	95% CI	
Moral deliberation	1.93a	[1.69, 2.16]
Control	2.38b	[2.15, 2.62]	2.59a	[2.36, 2.82]
2.30b	[2.07, 2.54]	
Note. Means within the same column that have different subscripts differ at p < .05, two-tailed. We preregistered some tests as one-tailed and report these in the main text. CI = confidence interval.

Moral Deliberation’s Effect Endured
Study 3 also offers evidence that the effect of moral deliberation can endure over a short interval: We found support for our hypotheses in analyses of the first block of headlines, in which participants were exposed to the manipulation before each headline, and in the second block, in which they were no longer exposed to the manipulation (see Supplemental Table S3). Furthermore, in each block, moral deliberation decreased intentions to share fake news that was aligned with participants’ politics but had different effects on news that was real or aligned with participants’ politics (see Figure 6 and Supplemental Table S4), in Blocks 1 and 2, respectively, ps = .004 and .033 for the three-way interaction between political alignment, news type (real vs. fake), and deliberation condition.
Robustness Checks
Even in the absence of explicit “fake news” labels, participants in Study 3 correctly identified fake and real news headlines most of the time (78% and 87%, respectively). The hypothesized results were mostly robust after excluding observations where participants misidentified the accuracy of the headline. The only exception was that, with less statistical power, the three-way interaction among political alignment, news type, and deliberation condition was no longer significant for Block 2 (Supplemental Table S6), although we still observed that moral deliberation increased sharing discernment overall in Block 2 (Supplemental Table S5).
Liking Intentions
The data supported near-identical conclusions for the “liking” and sharing measures (see Supplemental Material), suggesting that the results for the sharing measure do not reflect intentions to condemn fake news or warn others of its falsity.
Discussion
Study 3 suggests that, relative to a control-deliberation task, a moral-deliberation task can selectively decrease people’s intentions to share fake news, and not real news, especially when the news aligns with one’s politics, even when the fake news is not explicitly flagged, and even for a short duration after people complete the procedure. Moral deliberation had similar effects on people’s intentions to “like” news on social media, a behavior that is a less ambiguous expression of approval than sharing.
Study 4
Study 4 aimed to replicate Study 3’s findings with a different set of real and fake headlines about U.S. politics and to address some limitations. First, Study 3 cannot rule out the possibility that the control-deliberation task decreased sharing discernment as opposed to the moral-deliberation task increasing such discernment. Although this alternative explanation is unlikely given that the control-deliberation task had no effect on intentions to share fake news in Study 1b (see Figure 3), Study 4 directly examined whether the moral-deliberation task could increase sharing discernment relative to no task.
Second, we labeled the fake news in Study 4 as such. Although Study 3’s omission of such labels increases generalizability, including the labels provides a more-direct test of our claim that moral deliberation reduces people’s inclination to intentionally share fake news.
Study 4 also explores individual differences in deliberative thinking, as prior research finds that habitual deliberative thinkers are better at distinguishing between true and false news (Pennycook & Rand, 2019).

Table 9
Study 3: Estimated Marginal Means by Political Alignment and News Type
Condition	Aligned	Misaligned
Fake	Real	Fake	Real
M	95% CI	M	95% CI	M	95% CI	M	95% CI	
Moral deliberation Control	2.16a	[1.86, 2.45]	3.02a	[2.73, 3.32]	1.68a	[1.46, 1.91]	2.15a	[1.93, 2.36]
2.78b	[2.49, 3.07]	2.67b	[2.38, 2.96]	2.01b	[1.79, 2.24]	1.95a	[1.73, 2.17]	
Note. Means within the same column that have different subscripts differ at p < .05, two-tailed. We preregistered some tests as one-tailed and report these in the main text. CI = confidence interval.

Figure 6
Study 3: Effect of Moral Deliberation on Intentions to Share Fake Versus Real News That Was Aligned Versus Misaligned With Participants’ Politics
Note. Panel A: First block of headlines, in which participants completed the moral or control-deliberation task about each headline. Panel B: Second block of headlines, in which participants did not complete either deliberation task. See the online article for the color version of this figure.

Method
We preregistered Study 4’s hypotheses, design, and analyses in May 2023 at https://aspredicted.org/V4Y_LL8.
Informed by pilot studies that revealed lower intentions to share fake news than we had observed in Studies 1a–3, we took three steps to avoid floor effects. First, we only recruited participants who had indicated on a pretest an intention to share fake news (see the Participants section). Second, we used a contemporary set of headlines that people in an earlier study had been particularly willing to share (see Supplemental Material). Last, we used a revised measure of sharing intentions from previous research that we expected would capture more variance than the measure in our previous studies (see the Measures section).
Participants
We recruited U.S.-based political partisans from Prolific who demonstrated an inclination to share fake news on a pretest (see Supplemental Material for details, exclusions, and sample-size justification). Our final sample contained 598 participants (297 in the moral-deliberation condition, 301 in the control condition; 311 Republicans, 282 Democrats, and five unknown party) and 5,527 observations. Of the participants who reported gender, 214 were women, 320 were men, and 10 were nonbinary (Mage = 41 years, SD = 15).
Stimuli
The stimuli were 20 headlines about U.S. politics, different than the ones in Study 3, selected based on a pretest from a larger set (see Supplemental Material). A different research team originally sampled this larger set of headlines from actual content that had circulated widely on social media (Chen et al., 2023). Half appealed to Democrats and half to Republicans; half were real and half were fake. The fake headlines were labeled, “Disputed by third-party fact-checkers,” an ecologically valid way of flagging false content (Mosseri, 2016).
Procedure
Study 4 employed a 2 (moral deliberation vs. control; between- participants) × 2 (real vs. fake news headline; within-participants) × 2 (politically aligned vs. misaligned headline; within-participants) factorial design. Each participant rated one set of 10 counterbalanced headlines selected from the 20 headlines in our bank (see Supplemental Material).
The study followed the same basic procedure as our other prior studies. Participants randomly assigned to the moral-deliberation condition wrote one reason why sharing each headline would be ethical or unethical and rated the ethicality of such sharing. This time, to guide the deliberation, we also instructed them to “consider what ethical principles you would be upholding or violating if you shared it—and what ethically positive or negative consequences you might cause if you shared it.” Participants in the control condition did not rate or write anything.
Measures
The dependent variable was a single-item measure of sharing intentions (Pennycook, Binnendyk, et al., 2021): “If one of your acquaintances shared this headline on social media, how likely would you be to consider sharing it yourself?” (1 = definitely would NOT consider it to 7 = definitely WOULD consider it, with midpoint 4 = might consider it).
As exploratory measures, administered at the end of the study, participants completed the nine-item “need for chaos” scale, which assesses desire to sow discord (Petersen et al., 2023), and the

Note. News type coded as fake = 1, real = -1. Condition coded as moral deliberation = 1, control = -1. Political alignment coded as aligned = 1, misaligned = -1. p-values in bold reflect preregistered one-tailed tests. The model specifies the maximal random-effects structure that would converge. SE = standard error.

three-item cognitive reflection test (CRT; Frederick, 2005), which measures individual differences in deliberative thinking. In prior research, both measures were significant predictors of fake-news sharing (Lawson & Kakkar, 2022; Pennycook & Rand, 2019).
Results and Discussion
Except where noted, all results are from preregistered one-tailed tests of directional hypotheses, although two-tailed tests would have produced identical conclusions. The random-effect structures of the mixed models were the maximal ones that would converge (see “General Approach to Data Analysis” in Supplemental Material).
Moral Deliberation Increased Sharing Discernment
Consistent with predictions, a mixed model showed that moral deliberation, relative to no deliberation, increased sharing discernment, as shown by a significant interaction between condition (moraldeliberation = 1, control = -1) and news type (fake = 1, real = -1), b = -0.17, t(46.48) = -4.99, p < .001 (Table 10, Model 1). Simple slopes revealed that, also as predicted, moral deliberation decreased fake-news sharing, b = -0.23, t(85.14) = -1.99, p = .0249, d = –0.17 (see Table 11). We also found that, as in Study 3, moral deliberation increased real news sharing, b = 0.45, t(135.67) = 3.43, p < .001, d = 0.30 (two-tailed because we did not have directional predictions).
Because all fake news headlines in this study were clearly labeled, Study 4 offers further support for our theorizing that moral deliberation reduces people’s intentions to knowingly share fake (and not real) news.
Moral Deliberation Had a Larger Effect Among Politically Aligned Headlines
Next, we examined whether the effect of moral deliberation on sharing discernment differed depending on whether the headlines were aligned or misaligned with participants’ politics. As predicted, the results differed by political alignment, as shown by a significant three-way interaction when we added alignment to the mixed model (see Table 10, Model 2, and Figure 7), b = -0.05, t(3882.70) = 2.72, p = .003.
Specifically, moral deliberation decreased intentions to share fake news only when the headlines were aligned with participants’ politics, b = –0.20, t(136.87) = -3.16, p < .001, d = -0.29, but not when they were misaligned, b = -0.03, t(149.68) = -0.53, p = .299, d = -0.02 (see Table 12). This finding is consistent with our theorizing that moral deliberation should better reduce sharing for fake news that people find intuitively appealing. Even without moral deliberation, people have little reason to share content that they find intuitively unappealing.
Exploratory two-tailed tests showed that moral deliberation increased intentions to share real news both when the headlines were politically aligned, b = 0.25, t(200.56) = 3.54, p < .001, d = 0.32, and misaligned, b = 0.21, t(206.91) = 2.91, p =.004, d = 0.27.
In short, moral deliberation decreased intentions to share fake (not real) news when the news was aligned (but not misaligned) with participants’ politics.

Condition	Fake	Real
M	95% CI	M	95% CI	
Moral deliberation Control	2.19a	[1.92, 2.46]	3.82a	[3.53, 4.10]
2.42b	[2.15, 2.69]	3.37b	[3.08, 3.66]	
Note. Means within the same column that have different subscripts differ at p < .05, two-tailed. We preregistered some tests as one-tailed and report these in the main text. CI = confidence interval.

6
5
4
3
2

Exploratory Tests of Individual Differences
We found no evidence that need for chaos or political party (Democrat vs. Republican) moderated these results (see Supplemental Material).
We have argued that deliberative thinking about morality—and not deliberative thinking in general—reduces people’s inclination to knowingly share fake news. Supporting this argument, individual differences in habitual deliberative thinking (i.e., CRT scores) did not significantly predict sharing discernment in the control condition, b = -0.004, t(293.92) = -0.14, p = .892, in an exploratory analysis. People who think more deliberatively (as measured by the CRT) tend to be better at identifying fake news (Pennycook & Rand, 2019), but they are apparently not more inclined to share fake news that has already been identified as false, as it was in Study 4. In this context, moral deliberation may be more effective than deliberation in general.
General Discussion
Five studies reveal that encouraging moral deliberation can decrease intentions to knowingly share misinformation. These studies used 62 unique fake-news headlines—which had circulated widely on social media—about business organizations, COVID-19, and U.S.
Figure 7
Study 4: Effect of Moral Deliberation on Intentions to Share Fake Versus Real News That Was Aligned Versus Misaligned With Participants’ Politics
News Aligned with Politics News Misaligned with Politics
News Type
Fake
Real
1
No	Moral	No	Moral
Deliberation Deliberation	Deliberation Deliberation
Condition
Note. See the online article for the color version of this figure. politics. Our results emerged in convenience samples of participants in the United Kingdom and the United States, and a demographically representative U.S. sample, and the results replicated regardless of whether participants were explicitly informed the news was fake or disputed by independent fact-checkers or whether they instead had to discern real from fake news themselves. The effects were still apparent a short duration after people completed the moral deliberation procedure and were limited to fake news: Moral deliberation increased people’s inclination to share real news, perhaps because they felt they had a moral obligation to spread the truth. Thus, moral deliberation increased sharing discernment, people’s inclination to share real relative to fake news.
Our theorizing suggests that switching the basis of moral judgments away from intuition toward more deliberative thinking should have the biggest effect on inclinations to share intuitively appealing misinformation. Accordingly, moral deliberation had the biggest and most reliable effect on participants’ intentions to share fake news that was aligned (vs. misaligned) with their politics—an intuitively appealing type of misinformation that social media users are particularly likely to encounter and spread (Guess, Nyhan, & Reifler, 2020; Osmundsen et al., 2021). By contrast, even without being prompted to engage in moral deliberation, participants showed little inclination to share fake news that was misaligned with their politics.
Exposure to misinformation is not a new phenomenon; fake news is probably as old as news itself (Tucher, 2022). Long before the rise of social media, social scientists studied propaganda’s influence on attitudes (Katz et al., 1954; Lasswell, 1927; Lee, 1945; Silverstein, 1987), why people form false memories (Belli, 1989; Loftus & Hoffman, 1989), and how to “inoculate” against persuasion (Lumsdaine & Janis, 1953; McGuire & Papageorgis, 1961). This past research reflected societal concerns—such as how to increase support for the war effort (Hovland et al., 1949) or how to help war prisoners resist “brainwashing” (McGuire, 1970)—in an era when ordinary citizens were primarily consumers of misinformation. What is different about the present moment is that, using digital technology, ordinary citizens can now create and spread misinformation on an unprecedented scale, which can harm individuals and society. Thus, whereas classic work focused on how misinformation can influence attitudes, beliefs, and memories, the present work reflects a more contemporary concern that people will spread misinformation they do not believe. For this reason, our experiments examined people’s intentions to knowingly share false content on social media. However, we see no reason why our theorizing would not generalize to other contexts in which people knowingly repeat misinformation they have encountered, such as in conversations, via email or text, or in print. Carefully considering the ethical implications of sharing misinformation should reduce people’s inclination to share it.

Condition	Aligned	Misaligned
Fake	Real	Fake	Real
M	95% CI	M	95% CI	M	95% CI	M	95% CI	
Moral deliberation Control	2.25a	[1.95, 2.55]	4.00a	[3.68, 4.32]	2.13a	[1.78, 2.48]	3.66a	[3.29, 4.02]
2.64b	[2.34, 2.94]	3.50b	[3.18, 3.82]	2.20a	[1.87, 2.52]	3.24b	[2.91, 3.58]	

Table 12
Study 4: Estimated Marginal Means by Political Alignment and News Type

Note. Means within the same column that have different subscripts differ at p < .05, two-tailed. We preregistered some tests as one-tailed and report these in the main text. CI = confidence interval.

Mechanism and Alternative Explanations
We theorize that moral deliberation works by making moral judgments of fake-news sharing more severe. Consistent with this possibility, Study 2 found that prompting moral deliberation—above and beyond merely reminding people about morality—increased how unethical people thought it was to share fake news and that this shift in moral judgment statistically mediated the effect of moral deliberation on sharing intentions.
This finding also reduces concern about demand characteristics. Asking participants about morality could have signaled that the researchers moralized fake-news sharing, which perhaps made participants feel pressured not to share it. Contrary to this possibility, however, our moral-reminder condition and moral-deliberation conditions both asked participants about the morality of news sharing, but only the moral-deliberation condition significantly reduced intentions to share fake news.
The results also address a potential alternative explanation that deliberation in general—and not moral deliberation in particular— increased sharing discernment (Fazio, 2020; Pillai & Fazio, 2023), because careful thinking helps people recognize misinformation as false (Bago et al., 2020; Martel et al., 2020; Pennycook & Rand, 2019). First, we ensured through fake-news warnings and labels, as well as poststudy checks, that participants recognized misinformation as false. Prior theorizing would not predict a relationship between general deliberative thinking and sharing intentions when falsity is already salient, and indeed, supplemental results in Study 4 showed no evidence that individual differences in deliberative thinking (i.e., participants’ scores on the cognitive reflection test [CRT]) predicted sharing intentions (see Supplemental Material; cf. Pennycook & Rand, 2019). Second, we observed less fake news sharing and greater sharing discernment in our moral-deliberation condition than in our control-deliberation condition, even though both conditions prompted deliberative thinking.
A final alternative explanation could be that prompting people to deliberate about morality simply nudged them to consider accuracy. Prior work suggests that people will share content they are capable of recognizing as false in part because they fail to consider whether it is false, so highlighting the issue of accuracy reduces fake-news sharing (Pennycook, Epstein, et al., 2021). However, this inattention- to-accuracy explanation struggles to account for our results. Studies 1a, 1b, 2, and 4 explicitly highlighted the headlines’ inaccuracy to all participants, and moral deliberation still reduced their intentions to share fake headlines. Moral deliberation is not simply an accuracy nudge.
Theoretical and Practical Contributions
Our research deepens understanding of why misinformation spreads and what can be done about it. First, we develop new theory by suggesting that one reason people intentionally share misinformation is that they do not think carefully about the morality of doing so. Prior theories attribute misinformation sharing to false beliefs or inattention to accuracy (Ecker et al., 2022; Pennycook & Rand, 2021). By contrast, our theorizing helps explain why people are inclined to spread misinformation when they are already aware of its inaccuracy. More broadly, our results support the view that understanding why misinformation spreads requires understanding the “moral psychology of misinformation” (Effron & Helgason, 2022). In this way, our research connects the misinformation literature to scholarship in moral psychology and behavioral ethics about when and why people lie or tolerate others’ dishonesty (e.g., Galak & Critcher, 2023; Hilbig & Hessler, 2013; Levine, 2022; Mueller & Skitka, 2018; Shalvi et al., 2011). We hope future research will expand beyond our focus on moral deliberation and discover other ways in which moral psychological insights can be leveraged to tackle the spread of fake news and other misinformation.
Second, we discover a new phenomenon: Moral deliberation can reduce intentions to share misinformation. That our moral-reminder condition did not have the same effect challenges the influential claim that merely “priming” the concept of morality can increase honesty, the evidence for which has not reliably replicated (Schild et al., 2019; Verschuere et al., 2018). When people’s intuitive response is to act dishonestly, reminding them about morality may only reduce dishonesty if simultaneously prompting them to think deliberatively about it. When people intentionally share fake news, the reason may not be insufficient attention to morality, but insufficient deliberation about morality.
Third, we contribute to a debate in the literatures on moral psychology and behavioral ethics about the relationship between deliberative thinking and dishonesty (e.g., Gunia et al., 2012; Moore & Tenbrunsel, 2014; Shalvi et al., 2012). Some theory and research suggests that deliberative (vs. intuitive) thinking can increase dishonesty, which seems to predict the opposite of our findings (Lohse et al., 2018; Moore & Tenbrunsel, 2014; Van’t Veer et al., 2014). Two considerations, however, suggest that this prior theory and research do not apply to intentional misinformation sharing. One is that deliberative thinking is theorized to increase dishonesty because inventing lies uses cognitive resources that are unavailable when thinking intuitively (Capraro, 2017; Köbis et al., 2019). By contrast, sharing misinformation that one has encountered (e.g., “retweeting” fake news 
on social media, repeating a false rumor in a gossip network) does not require inventing lies, but rather spreading lies that others have invented. The other consideration is that, consistent with our findings, a meta-analysis finds that deliberation (vs. intuition) decreases dishonesty when lying would harm abstract others (Köbis et al., 2019; Pitesa et al., 2013)—a situation in which lying presumably feels less intuitively wrong than when the harm is to specific others. Sharing misinformation, particularly on social media, tends to harm abstract or anonymous victims (e.g., democratic institutions; unknown social media users) more than specific individuals. Thus, when it comes to misinformation sharing, moral deliberation may increase honesty.
Fourth, we contribute to a related debate about whether deliberating about misinformation increases motivated reasoning (Kahan, 2013; Kahan et al., 2017) or rational thinking (Pennycook, 2023; Pennycook & Rand, 2019). A priori, it was plausible that our participants would use the moral deliberation prompt as an opportunity to rationalize why it is morally acceptable to share politically congenial misinformation. If they had done so, moral deliberation should have increased participants’ intentions to share fake news that was aligned with their politics, but we observed the opposite result. Although our studies were not designed to definitively test the relationship between deliberative thinking and motivated reasoning, they do add evidence from the moral domain to a growing body of work suggesting that deliberative thinking does not exacerbate motivated reasoning (Bago et al., 2020, 2023; Strömbäck et al., 2024; Tappin et al., 2021).
Fifth, our theorizing offers a potential synthesis for scattered findings about why people share misinformation they do not believe. We speculate that people will share misinformation that advances a political agenda (Osmundsen et al., 2021), signals their social identity (Van Bavel et al., 2021), helps them obtain social rewards (Lawson et al., 2023; Ren et al., 2023), or feels familiar (Effron & Raj, 2020) because each of these factors fosters less severe moral intuitions. Designing an intervention to address each factor individually seems harder than intervening to reduce the overall influence of moral intuitions on moral judgments and sharing decisions.
Finally, by identifying a procedure that reduces intentional misinformation sharing, our findings have the potential for societal impact. When people repeat or reshare misinformation they have encountered, they amplify its potential to cause harm. Even if these people recognize the misinformation as false themselves, those they share it with may not. Moreover, contributing to the prevalence of blatantly false claims may increase others’ belief in different, more ambiguously false claims (Levari et al., 2024; Pennycook, Bear, et al., 2020). Prior interventions (debunking, training, and accuracy nudges) are designed to stop people from inadvertently spreading falsehoods but have neglected the problem our research highlights: intentional misinformation sharing. Thus, we hope that our findings will inform the development of scalable antimisinformation interventions that promote greater moral deliberation.
Theorized Boundary Conditions
We theorized (and found evidence in Study 2) that moral deliberation reduces intentions to knowingly share fake news by increasing the severity of moral judgments. This theorizing implies that encouraging moral deliberation will not reduce intentions to share misinformation when deliberation and intuition point to similar moral judgments. One relevant situation could be when the content is unambiguously harmful. For example, we expect most people would have the intuition that sharing faked pornographic images of a coworker is extremely immoral—a judgment they would continue to hold even after deliberating. Another situation could be one in which the relevant content is innocuous. For example, around Christmas, many news outlets instruct readers how to track Santa’s sleigh by radar (https://www.noradsanta.org/en/). We expect that most people who incorporate Santa into their holiday traditions would perceive this fake news as harmless fun, resulting in permissive moral judgments regardless of whether they relied on intuition or deliberation. In both examples, encouraging moral deliberation should have no effect on sharing intentions because we would expect it to have no effect on moral judgments.
Our theorizing also implies that moral deliberation could increase intentions to share misinformation if it decreased the severity of moral judgments. We have argued that prompting moral deliberation decreases intentions to share misinformation because deliberation increases the influence of easy-to-articulate reasons on judgment (Simonson, 1989; Slovic, 1975; Tversky & Shafir, 1992; Wilson et al., 1989) and because it is generally easier to articulate reasons why sharing misinformation would be unethical (e.g., because it is dishonest or harmful) than why it would be ethical. In some situations, however, people may find it easier to articulate reasons why sharing would be ethical. For example, someone who perceives loyalty as their primary moral duty might find it easy to argue for the ethicality of sharing misinformation from a political ingroup. Someone whose main moral concern is to reduce societal harm might likewise find it easy to explain why it would be ethical to use misinformation to scare the public into protecting themselves from a deadly disease. In situations like these, prompting moral deliberation might increase how ethical people think it is to share the misinformation, thereby increasing their intentions to share it.
Constraints on Generalizability
Researchers interested in studying moral deliberation and misinformation should bear in mind several constraints on generalizability.
Generalizability Across Stimuli
Our stimulus-sampling procedures suggest that our results generalize broadly across the type of fake-news headlines about business, health, and politics that people encounter on social media (see Pennycook, Binnendyk, et al., 2021). The 62 unique fake-news headlines in our studies were originally collected from fact-checking websites for prior research because of their ecological validity. Study 2 found that prompting moral deliberation increased how unethical people thought it was to share these headlines, consistent with our theory that people generally find it easier to articulate reasons why sharing would be unethical than why it would be ethical. However, people will sometimes encounter misinformation they find more morally ambiguous, such that articulating reasons why sharing is ethical feels just as easy (or difficult) as articulating reasons why it would be unethical. Our theorizing does not predict that moral deliberation would reduce intentions to share such headlines.
Recall that our stimuli were fake-news headlines that the average participant showed some inclination to share (without moral deliberation). We have argued that this is because a critical mass of participants found the headlines intuitively appealing even though they knew the headlines were false. Some fake-news headlines, however, will not feel intuitively appealing to many individuals at all, and thus few would be inclined to share them even without deliberating about morality. Our theorizing would not generalize to such headlines because they would show floor effects on sharing-intention measures at baseline. We encourage researchers to pretest headlines to avoid such floor effects.
Generalizability Across People
Our studies recruited a relevant population—participants who sometimes share news on social media—and our results did not depend on participants’ political identification (i.e., Democrat vs. Republican), desire to sow discord (i.e., “need for chaos;” Lawson & Kakkar, 2022; Petersen et al., 2023), or habitual tendency to think deliberatively (unlike accuracy nudges; Pennycook & Rand, 2022). We thus expect our results to generalize broadly to ordinary social media users who encounter misinformation, recognize it as such, and are still tempted to share it. We are less certain that our results would generalize to originators of misinformation or “trolls” who may have financial or political motives for creating fake news. Such individuals may intentionally share fake news even after moral deliberation.
On the other end of the spectrum, some individuals will never share content even without moral deliberation. Recall that most of studies sampled participants who at least sometimes share news on social media. Our results would presumably not generalize to users who “lurk” on social media without sharing content themselves, because they would show floor effects on sharing-intention measures at baseline.
Finally, our studies recruited participants from the United States and the United Kingdom, including a demographically representative U.S. sample, but antimisinformation interventions could have different effects in different global contexts (Badrinathan & Chauchard, 2024; Blair et al., 2024).
Limitations
An important limitation is that, like most research examining misinformation interventions, our research examined sharing intentions in lab-style surveys rather than actual sharing behavior on social media. On one hand, intentions are an imperfect proxy for behavior (Ajzen, 1991). On the other hand, however, intentions are a useful proxy for behavior. Overall, intentions significantly predict behavior (Sheeran, 2002), with bigger effect sizes when the intentions are easy to remember and when the behavior feels and is volitional (Ajzen, 1991; Sheeran & Webb, 2016). These factors characterize real social media contexts: When users form an intention to share a news item they have just encountered, they have control over whether to act on this intention, and they would typically act immediately, before they have time to forget. Additionally, measuring intentions to share specific articles, as we did, is likely more predictive of behavior than measuring intentions toward general behaviors or goals (Ajzen, 2020; Fishman et al., 2020). Suggesting that intervening to reduce intentions to share fake news could curb intentional fake-news sharing on social media, meta-analytic evidence reveals that manipulating intentions can induce behavior change (Webb & Sheeran, 2006). Most relevant for the present research, self-reported intentions to share social media content correlates meaningfully with actual shares on social media (Mosleh et al., 2020), and people who share lower quality (vs. higher quality) news on social media are more likely to express intentions to share fake news on surveys (Arechar et al., 2024). Thus, examining sharing intentions can offer insights about sharing behavior.
Another limitation is that we tested only one procedure for encouraging moral deliberation. This procedure has several practical advantages: It is very brief, does not impose a moral viewpoint on others, and can be instantiated online. One could imagine exposing social media users to ads, tweets, pop-ups, or surveys online that encourage them to think more carefully about morality before sharing, although social media companies themselves may not wish to interrupt their users’ browsing to ask them to write about ethics. However, other procedures for encouraging moral deliberation might be appropriate in other contexts. For example, digital literacy trainings could incorporate a module that encourages the habit of engaging in moral deliberation before sharing content online.
Future Research Directions
The aim of our research was to test whether moral intuitions contribute to the spread of misinformation and to provide a proof-of- concept that encouraging moral deliberation can reduce intentions to share fake news. Before translating the moral-deliberation procedure into a full-scale intervention, future research should address the limitations and constraints on generalizability discussed above. Studies should examine how different moral-deliberation prompts affect sharing intentions, which individuals show the biggest effects, whether the results generalize across different cultural contexts, and how long the effects endure. Future studies should also test whether moral deliberation affects actual sharing behavior on social media, although access to social media platforms is not always feasible (see Arechar et al., 2024), particularly considering restrictive pricing introduced in 2023 to access data from X, formerly Twitter (Murtfeldt et al., 2024).
As noted, our theorizing predicts that moral deliberation would not reduce intentions to share morally ambiguous fake news. Future research could test this boundary condition by sampling only morally ambiguous fake news, although given that our studies examined diverse headlines that had circulated widely enough to catch the attention of fact-checkers, morally ambiguous headlines may be relatively rare. It may also be possible to create fake-news stimuli that the average participant would find morally ambiguous, although this approach would have lower external validity than our approach of sampling actual fake-news headlines.
Future work should also examine potential synergies between prior work, which aims to help people identify misinformation, and our work, which aims to reduce people’s moral tolerance of misinformation they have already identified. Intervening to reduce misinformation sharing at scale may require combining interventions to target different psychological processes (Pennycook et al., 2024). For example, a single intervention that trains people to recognize manipulative techniques (van der Linden, 2024), nudges them to consider accuracy (Pennycook, Epstein, et al., 2021), and encourages them to deliberate about the morality of sharing might help people both recognize misinformation and increase their compunction about sharing it. Future research should test the efficacy of such a combined intervention.
Conclusion
In the fight against misinformation, current tools like debunking, training, and accuracy nudges are appropriate when people fail to recognize the misinformation. New tools are needed when people do recognize misinformation but share it anyway. Interventions that prompt moral deliberation hold promise.
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Received August 15, 2024
Revision received October 9, 2025
Accepted October 26, 2025 ▪