Posted by Mandi Goodsett
https://www.inthelibrarywiththeleadpipe.org/2026/moving-beyond-willpower/
https://www.inthelibrarywiththeleadpipe.org/?p=12228
In brief
Academic librarians and others often engage with media literacy instruction by promoting fact-checking strategies, such as lateral reading or Mike Caulfield’s SIFT. Evidence shows that these strategies are valuable and can be effective, but they all ultimately rely on individual students to use willpower to overcome cognitive habits, biases, strong parasocial relationships with content creators, the power of algorithms, and other challenges to fact-checking content in the moment. This paper offers an alternative approach that instead encourages librarians to support students in intentionally redesigning their information environments to improve the quality of information that they encounter in the first place.
By Mandi Goodsett
“The task of breaking a bad habit is like uprooting a powerful oak within us. And the task of building a good habit is like cultivating a delicate flower one day at a time.” – James Clear
In a 2024 study conducted by the News Literacy Project, the organization found that 80% of the teen participants believed that journalists fail to produce more impartial information than other online content creators, and 69% said that news organizations intentionally make their content biased to advance a particular viewpoint. When the News Literacy Project followed up with these young adults a year later, they found that most of them believe that trustworthy, unbiased news is rare or maybe doesn’t even exist (2025).
Pew Research found, through a series of focus groups, that Americans don’t always agree on what constitutes a “journalist” or “news media,” and young adults are more likely than older adults to call “new media” platforms hosts, such as podcasters and social media creators, “journalists” (Eddy et al., 2025). Overall, younger participants were less likely than older adults to even care whether the news they consume comes from a journalist. The investigation found that Americans are concerned that, besides maybe a few reliable ones, journalists are concerned with “clicks, eyeballs, money, things like that, and they don’t necessarily mind tweaking the truth to suit their audience or their advertisers” (quoted in Pew Research, 2025).
These statistics are significant because cynicism about standards-based news and other traditionally authoritative institutions has many negative impacts. First, news cynicism can lead to news disengagement, which pushes information consumers to less reliable platforms (Ahmed et al., 2025; Fletcher, et al., 2024; Mont’Alverne, 2022) and contributes to erosion of trust more broadly in institutions like voting (Park, et al., 2025; Raffio, 2025). When people disengage, news sources themselves are threatened by obsolescence, and this threatens their role as a watchdog and a keystone of democratic societies (Haider & Sundin, 2022). News cynicism makes it difficult for accurate information to reach people and, paradoxically, makes people more vulnerable to misinformation (Ahmed et al., 2025; Hasell & Halversen, 2024). Individuals may feel anxious, depressed, and helpless about their world, leading to a spiral of disengagement (Hasell & Halversen, 2024). News cynicism also fuels societal division and threatens democracy (Cappella & Jamieson, 1996; Valgarðsson et al., 2025). Widespread distrust in institutions such as the government, science, public authorities, and the press is a risk to media literacy, democracy, civil discourse, and our sense of agency.
Academic Librarians and Media Literacy Instruction
One strategy for helping students and others improve their media consumption is to teach them media literacy skills. Media literacy is generally thought to be the ability to access, evaluate, analyze, and create media messages (Aufderheide, 1993), although definitions vary considerably between researchers and practitioners (Fleming, 2014; Hobbs, 1998). Media literate individuals have the skills to identify media sources and messages that are unreliable, and, perhaps more importantly, craft an overall media diet that is more likely to consist of reliable information.
Academic librarians are interested in and possess relevant expertise to teach students media literacy skills that are relevant in academic and non-academic settings. Many librarians have explored tactics for teaching students source evaluation skills that move beyond the CRAAP test (Currency, Relevance, Authority, Accuracy, Purpose), such as the SIFT method (Stop, Investigate, Find, Trace), created by Mike Caulfield (2019), or lateral reading, popularized by the Civic Online Reasoning organization (Digital Inquiry Group, n.d.). Caulfield’s SIFT method provides a more up-to-date approach to source evaluation by offering strategies that are more efficient, straightforward, and applicable in a wide variety of contemporary information settings (Bull, 2021). “Lateral reading,” which is a key component of SIFT, involves leaving the source that is being evaluated and opening new browser tabs to investigate what other Internet sources report about the site and its claims (Wineburg & McGrew, 2019). Research has shown that the SIFT method and lateral reading results in more accurate student source evaluation (Bobkowski & Younger, 2020; Breakstone et al., 2021; Brodsky et al., 2021). These techniques reflect a better understanding of the modern online information environment than simplistic checklist strategies. However, they still expect students to avoid misinformation through careful self-control and self-monitoring.
Misinformation is an interdisciplinary problem with significant complexities. As Sullivan has argued, librarians have historically focused on media literacy instruction strategies that neglect the psychology of how people interact with information, and the field of library and information sciences is somewhat siloed in its exploration of source evaluation instruction (2019). For example, heuristics, systems thinking, mental models, and cognitive biases all play a role in how and why people adopt misinformed beliefs. Emotions also influence the ways that individuals evaluate information (Hewitt, 2023; Hicks & Lloyd, 2021), yet they play a minor role in most library source evaluation instructional strategies. Academic librarians may have a role in combatting misinformation, but we should proceed, as much as possible, guided by research conducted across disciplines (Saunders, 2025). As an example, academic librarians have often focused their source evaluation teaching on investigation strategies and fact-checking skills. These skills are very important, and we shouldn’t abandon them. But there are many reasons, informed by research outside of Library and Information Science (LIS), why reactive strategies that rely on individual willpower are destined to be difficult to maintain.
Challenges of Fact-Checking and Other Traditional Source Evaluation Techniques
Evidence shows that, globally, trust in institutions is decreasing, including in democratic societies (Kavanagh & Rich, 2018; Gil de Zúñiga & Diehl, 2019). The consequences of this could be severe, as many scholars posit that trust in institutions is an important pillar of democracy (Haider & Sundin, 2022). There are also a number of well-studied examples of how bad actors can sow doubt in institutions, such as academia, to achieve their own ends (Haider & Sundin, 2022). This has played out in the case of the tobacco industry and fossil fuel companies; in both cases, the science is clear, but raising uncertainty can be enough to sway consumers to take actions that are not in their best interests (Oreskes & Conway, 2010). All of this said, when society’s institutions become corrupt or unreliable, or when institutions are systematically unfair to one’s group or identity, distrust in institutions is often justified (Haider & Sundin, 2022). So while dismissing institutionally-backed information in favor of persuasive individuals is risky, confidently pointing to institutions as always trustworthy is also unlikely to be effective. Easy-to-apply source evaluation checklists that are meant to be used across all contexts and blind trust in compelling individual voices both fail to reflect the complexity of information environments.
While media literacy that relies on individual fact-checking skills is very important, there are many reasons why a willpower approach is likely to have limited success. The section below explores these limitations from internal factors, to external factors, and finally, to systemic factors.
Limits of Fact-Checking: Internal Factors
The intuitive solution to the problem of misinformation is to let media consumers know that a piece of information is untrue. However, there is mounting evidence that retractions and corrections have little effect on whether someone will make decisions based on misinformation (Seifert, 2014; Thorson, 2016; Zhou & Shen, 2024). There are many potential reasons for this, but one that almost certainly plays a role is the effect of cognitive bias. For example, epistemic egocentrism is a cognitive bias that occurs when individuals fail to consider their own privileged information when imagining the perspectives of others (Royzman et al., 2003; Zhou & Shen, 2024), which can cause people to judge their own source evaluation skills highly and blame the problem of misinformation’s spread on others. Closely related is blind spot bias, which is the belief that one is immune to bias (Pronin, et al., 2002). Confirmation bias is also relevant to the adoption and spread of misinformation; this bias is the tendency to seek out and remember information in ways that favor existing beliefs (Nickerson, 1998; Oswald & Grosjean, 2004). A consequence of confirmation bias is selective exposure, or a person’s proclivity to preferentially seek and engage with information that is in alignment with their existing values, beliefs, or attitudes (Zhou & Shen, 2024). These cognitive biases, which can occur whether or not the person has a pre-existing attitude about the misinformation, may lead people to dismiss corrections, assume they are correct in situations where there is substantial conflicting evidence, or, by consciously or subconsciously designing their information environment, rarely encounter threats to their existing worldview.
Research into the mechanisms that cause misinformation adoption to persist (sometimes called the “continued influence effect of misinformation”) shows that corrections can fail in their effectiveness when they leave a gap in someone’s mental model, especially when the misinformation fills that gap in a more satisfying way (Johnson & Seifert, 1994, p. 1420). Retrieval errors can also contribute; for example, when misinformation is retrieved from memory without the “false” label, or when misinformation is retrieved more readily than its correction (Ecker et al., 2011; Gordon et al., 2017; Lewandowsky et al., 2012). Because the misinformation and correction both exist in memory, deliberate, effortful thinking is necessary to retrieve corrections from memory, and natural cognitive efficiency processes can make this retrieval difficult or unlikely (Kendeou & O’Brien, 2014; Pennycook & Rand, 2019). These neurological processes make debunking misinformation incredibly challenging once it has been adopted into someone’s mental model.
Information consumers are also often very confident about their beliefs, even if their knowledge about the topic at hand is, upon investigation, quite shallow. While perceptions of widespread misinformation increase, Americans are confident that they have the skills to identify this unreliable content. In 2016, a study found that 84% of participants were confident in their ability to spot “fake news” and 64% of those same participants believed that fabricated news stories caused significant confusion for Americans (Barthell et al.). Who is being confused by these stories? Not them, the participants in the study seemed to say; it’s everyone else. This points to an overconfidence that individuals have in their own ability to detect false information, contributing to the problem of misinformation’s spread.
One cognitive bias that helps to explain this phenomenon is the Dunning-Kruger effect, whereby individuals with limited knowledge of a subject fail to accurately assess their own level of expertise (Dunning, 2011). For example, research has shown that overconfidence in news judgments is associated with higher susceptibility to false news across a variety of topics, from autism awareness to nutrition claims (Lyons, et al., 2021; Motta, Callaghan, & Sylvester, 2018; Peng & Shen, 2025). Along the same lines, the “nobody-fools-me perception” is a cognitive bias whereby someone is overconfident in their ability to detect misinformation, especially as compared to others (Martinez-Costa et al., 2022). This leads people to make claims like “Many people haven’t learned to check facts” but fail to recognize their own media literacy deficiencies (Martinez-Costa et al., 2022).
Relatedly, the illusion of explanatory depth occurs when people believe they understand a complex topic more than they actually do upon further probing (Rozenblit & Keil, 2002; Sloman & Fernbach, 2017). Humans move through the complex, nuanced, and dangerous modern world by holding a naive intuition that they understand how the world around them works. This, combined with poor knowledge about the extent of our knowledge, causes a pervasive belief that we can explain the world around us even when we can’t (Bailey, 2021). The illusion of explanatory depth can cause people to adopt false beliefs confidently, not realizing their shallow understanding of the topic should cause them to question their self-assured stance.
It’s important to note that a 2025 study found that exposing participants to false news not only caused them to become overconfident in their judgments about whether news stories were true or false, it also fueled news mistrust (Altay et al.). This study demonstrates how news environments themselves contribute to issues that spur misinformation’s spread, such as overconfidence and cynicism. Along the same lines, some researchers worry that media literacy interventions that focus on “misinformation’s omnipresence” risk heightening the salience of misinformation as a threat to society and individuals, ultimately increasing news mistrust (van der Meer, Hameleers, & Ohme, 2023). Misinformation warnings alone can provoke a deception-bias, whereby people assume deception in news messages, rather than defaulting to a trust-bias as they often do in other contexts (van der Meer, Hameleers, & Ohme, 2023).
Limits of Fact-Checking: External Factors
While it’s clear that cognitive limitations make corrections to misinformation difficult or impossible, other researchers argue that misinformation itself is not as widespread of a problem as is commonly believed. They argue that the current perceived prevalence and “panic” about misinformation is a kind of “historical amnesia” (Stecula, 2025). The spread of misinformation is nothing new, and misleading messages have been created and spread for hundreds of years, from anti-vaccination movements of the early 1800s to disbelief about the real cause of JFK’s assassination, all of which occurred before the invention of social media (Stecula, 2025). What is different about the spread of false messages today is their overt support by important societal leaders and the new visibility their small groups of adherents have due to social media. These changes have allowed society to diverge into competing knowledge communities with unique standards for expertise, source evaluation, and, ultimately, defining truth (Stecula, 2025). These new, ideologically isolated communities with extreme views do not represent the majority of the population, but may seem to, given the way social media can amplify their messages. Fact-checking is likely to have limited reach and impact in these isolated, closely-knit communities.
Even in the rare cases when overtly false information is spread outside of isolated bubbles, fact-checking as a strategy for stopping its spread has limitations. Some argue that most fact-checking is ultimately reactive, constrained by scale and speed, and destined to always be catching up with rapidly changing misinformation messages (Wack, Duskin, & Hodel, 2024). Fact-checkers themselves worry that fact-checking risks drawing additional attention to misinformation and has limited impact for cognitive reasons; one said, “I can only convince those already convinced” (Westlund et al., 2024).
Another assumption of fact-checking is that knowledge of the truth impacts people’s behaviors in positive ways. However, research about climate change misinformation, for example, found that even when people have accurate beliefs about climate change, it has limited impact on their willingness to engage in pro-environmental behavior (Spampatti, 2025). Additional research has shown that, for some individuals, feeling and appearing independent from outside influence is more important than being correct; for these individuals, whether something is factual or not is irrelevant to whether it should be shared (Stein & Rutchick, 2025).
It’s also possible that the problem of misinformation has been mischaracterized due to how it is typically studied. Current research on misinformation often focuses on issues that are likely to invoke false beliefs, and it also rarely asks participants to indicate confidence levels; both of these oversights may inflate the perception that people are deeply divided about many issues. In reality, participants may just be uninformed about issues, not misinformed, which is not captured in most studies (Stecula, 2025). Along the same lines, many studies that rely on truth discernment tasks impose a false dichotomy between true and false statements, when misinformation in real world contexts often rides the line between true and false, or may include some true statements with an overall misleading message (Spampatti, 2025).
Limits of Fact-Checking: Systemic Factors
Research on the spread of misinformation has also frequently focused on individual-level susceptibility without addressing the role of structural inequities in shaping exposure to misinformation and capacity to resist it (Lin et al., 2022; Schirmer, et al., 2025; Walter et al., 2020). Socioeconomic disparities limit who can access high-quality information; lack of broadband access, language differences, and digital literacy deficiencies can all contribute to this problem (Schrimer, et al., 2025). Systemic mistrust, justified by decades of historical injustice, can lead some to seek information outlets alternative to the mainstream, exposing them to misinformation (Jaiswal et al., 2020; Pew Research Center, 2024). Many marginalized communities, however, are actively working to understand the impacts of misinformation and take grassroots efforts to combat it (Schirmer, et al., 2025). There are many ways to move beyond laying the responsibility of misinformation avoidance on individuals, and structural interventions have more potential to address the social disparities that shape misinformation adoption.
While fact-checking strategies in particular have limited utility, all misinformation interventions that expect individuals to exercise willpower in algorithmically-driven environments will face considerable difficulties. Algorithms have significant power to influence what information and voices individuals encounter. While evidence about the impact of “filter bubbles,” or isolated online spaces that perpetuate misinformation messages (Pariser, 2011), is mixed (Arguedas et al, 2022), there is some evidence that filter bubbles can limit users’ exposure to diverse points of view and increase users’ access to lower-quality content (Ciampaglia et al, 2018). It can be tempting, in today’s algorithm-rich environment, to assume that, instead of intentionally seeking out standards-based news, that news will “find” you (Skurka, et al., 2025). American adults who think the news will “find” them are more likely to overestimate their ability to tell false from true political news and more likely to engage confidently with false news messages (Skurka, et al., 2025).
One reason social media messages can be especially compelling has to do with influencers. Social media platforms allow for individual voices to have an outsized influence on large sections of the population. These individual voices, or “influencers,” do more than entertain people; they often drive the narrative around topics ranging from politics to economics to health (Thi & Ibrahim, 2025). While research shows that credibility, consistency, and transparency are important characteristics of an influencer that people trust, for an influencer to truly appear “authentic,” they must also build an emotional connection with their audience by seeming relatable and “being real” (Thi & Ibrahim, 2025). Accuracy of the messenger, while not completely irrelevant, is not the most important factor when people decide who to trust in social media settings.
The emotional bond that audience members form with influencers contributes to the rise of parasocial relationships, which are one-sided relationships in which someone develops a sense of closeness and intimacy with a media figure, usually a celebrity or influencer (Hoffner & Bond, 2022). The intensity of parasocial relationships is driven by the media figure’s moments of self-disclosure, glimpses into parts of the person’s life that are usually unknown, and momentary, technology-mediated interactions (e.g. reposting or liking a fan’s post) (Hoffner & Bond, 2022; Kim & Song, 2016; Kurtin, O’Brien, Roy, & Dam, 2018; Dai & Walther, 2018). Even though the influencer or celebrity does not know fans or even necessarily have their best interests at heart, it can feel to fans that they do because of the sense of closeness and trust they have for the influential person.
Influencers are an important source of misinformation in the information ecosystem because of the scale of their impact. This is especially true for messages that are already viral or widespread; these messages actually help influencers gain more trust from their followers, regardless of the veracity of the message (Mulcahy, et al., 2024). However, influencers face little to no accountability when it comes to sharing misinformation, beyond the impact that being found to have shared inaccurate information might have on their reputation (Thi & Ibrahim, 2025). Unlike journalists, who receive training and commit to a code of ethics, social media creators operate outside any kind of formal ethical framework.
Complicating the interplay between cognitive biases, algorithmically-driven online spaces, and persuasive social media personalities, is the rise of generative artificial intelligence (AI). Although access to this technology is fairly recent, the use of these systems contributes significantly to the existing problem of misinformation by allowing for the easy creation and customized dissemination of misinformation at scale (Bontridder & Poullet, 2021). Even elected officials have shared AI-generated misinformation with a wide audience (Skau, 2026).
The widespread sharing of AI-generated misinformation has two main negative impacts; first, even when the content is fact-checked, it can continue to misinform due to the previously mentioned continued influence effect. Sandra Ristovska, an expert in visual evidence from the University of Boulder, Colorado described this challenge of false AI-generated images: “It lies deep in human nature and in the way we see and interpret images that it can be difficult to ‘un-see’ an image or a video once we have seen it” (Ristovska as cited in Skau, 2026, para. 10). The other negative effect is that it can contribute to a sense that nothing online is real, or that we shouldn’t bother determining if something is true or false; in other words, it deepens the cynicism many already feel. As Renee Hobbs, Professor of Communication at the University of Rhode Island, stated, “If we become indifferent to whether something is true or false, we risk losing many of the cooperative structures that make civilization possible” (as cited in Skau, 2026, para. 13).
Willpower and Habits
Clearly many factors make fact-checking a challenging strategy to rely on for stopping the spread of misinformation and improving students’ media literacy. Importantly, whether an individual is stumbling upon someone else’s fact-check or considering whether to fact-check something themselves, they must have the willpower to take additional critical steps.
It could be argued that the most effective means of improving this situation is to make systemic changes, such as improving social media and search engine algorithms to prioritize accuracy and flag misinformation, or requiring influencers to be more transparent about their motives or qualifications. But while we continue to push for these systemic changes, individuals must continue to make information choices everyday, and this is what library instruction tends to focus on. With that in mind, how can we encourage individual actions that rely less on willpower?
What we are ultimately trying to accomplish is a habit change. Considerable research shows that changing someone’s habits through willpower is very challenging and often destined to fail (Bargh & Barndollar, 1996; Borland, 2013; Muraven, 2012; Wood et al., 2014). What is more effective is changing someone’s environment to encourage the desired behaviors (Bargh & Barndollar, 1996). In research conducted about the importance of environmental as opposed to willpower-based approaches to habit change, Duckworth et al. describe how “situational selection strategies” like putting a distracting device in another room during study time, spending time with friends who value studying, and telling someone else their study goal to hold them accountable had maximum success in improving student study habits (2016). These strategies were more successful than “self control” strategies, which students described as a mindset like, “Just deal with it and study” or “Just do it…I just focus and get my work done” (p. 334). This is just one example of many studies that show how stopping a bad habit through sheer willpower and keeping all other aspects of the environment the same has limited success. However, changing the environment to make the bad habit more difficult and good habits easy and effortless has a much better chance at success.
The same is true with our information environments. When students spend considerable time in algorithmically-driven social media spaces, they may encounter more poor-quality information that requires fact-checking, and they may feel both a sense of cynicism about the information system more broadly as well as a lack of agency. However, when students spend less time being directed by an algorithm in information spaces with lots of tempting, low-quality information, and more time consulting reliable, standards-based information sources, they improve their information behavior, and, importantly, gain a sense of agency about what information they encounter and consume.
Recommendations for Academic Librarians
Although structural changes are necessary to address many of the issues discussed here, academic librarians may be able to contribute by changing how we approach information literacy instruction. While fact-checking methods like SIFT and lateral reading are important skills (that are convenient to fit into a 50 minute class period), librarians could instead (or in addition) address the importance of adopting new information habits. Rather than asking students to start with having the presence of mind and willpower to “stop” as in SIFT, maybe we should start our process before that “stop” is even necessary by intentionally designing the information environment in the first place.
“Lift Our Gaze” : Teach about Systemic Information Structures
One initial challenge that librarians must address is that it may require considerable motivation for students to take the initial steps to improve their information environments. If students believe that influencers are just as reliable as journalists (or more so), why would they change their habits?
One strategy is to lean into the ACRL Frame “Information Creation is a Process” (2016). Librarians can help students better understand the systems that underlie the information they encounter through the concept of “infrastructural meaning-making” offered by Haider and Sundin (2023). They define infrastructural meaning-making as going “beyond examining the content’s sources, and even beyond evaluating the source’s content, to also be concerned with the institutions and systems, the platforms and algorithms that deliver it to us and onto our devices” (p. 2). To apply this concept, in addition to traditional source evaluation methods like CRAAP and SIFT, instructors would also encourage students to consider why that particular source appeared to them at that time – in other words, how do the conditions of access, along with the information and its source, help us understand the piece of information? (Haider & Sundin, 2019). Algorithmic literacy, situational awareness, and platform knowledge can all contribute to better decisions about whether to pay attention to a particular piece of information (Haider & Sundin, 2023). Fortunately, many simple and creative activities exist to help students understand how algorithms work to impact their information environments (Camarillo, 2025). While digital information infrastructures are often invisible to us (intentionally on the part of platform providers), we benefit from “lifting our gaze” to understand how networked environments impact what information we encounter (Haider & Sundin, 2023, p.3).
With this strategy, it’s important to consider how affective or attitudinal factors might impact students’ source evaluation approaches, and to add instructional interventions that address these factors to typical source evaluation instruction. For example, one researcher found that just teaching algorithmic awareness to students was helpful, but it was limited in its impact because students felt such a sense of powerlessness to shape their online experiences. However, by pairing algorithmic knowledge with activities that promote digital agency, we can help to combat the significant cynicism students feel about their digital environments (Chung, 2025).
Along the same lines, helping students understand how standards-based news is created, especially in comparison to influencer-generated content, can help them view the information landscape with a wider scope, rather than focusing on fact-checking individual claims. In the field of communication, researchers have found that knowledge of how news is produced, disseminated, and consumed can improve misinformation detection (Ashley et al., 2023; Chan, 2024; Chan et al., 2024).
Deliberately Design a News and Information Landscape
Next, students should be encouraged to intentionally seek out reliable information, rather than allow algorithms to determine their information landscape. Research shows that young adults who are exposed to news-rich environments, especially in the classroom, are more likely to develop news consumption habits (Edgerly, 2025; York & Scholl, 2015). In general, people need more help accepting true news than rejecting false news (Pfänder & Altay, 2025), so deliberately undertaking this task could be helpful. Researchers have also found that this approach – focusing on what sources to trust, rather than focusing on the small prevalence of misinformation – can increase trust in standards-based news, rather than fueling cynicism about news (Altay, De Angelis, & Hoes, 2024). However, it’s important to incorporate instruction about negativity bias and click-bait into this process, because research shows that a pessimistic outlook is correlated with self-selecting more negative and episodic news when given the chance to intentionally select news outlets (van der Meer & Hameleers, 2022). Encouraging students to deliberately select reliable information while also helping them break out of their cynical outlooks may improve the effectiveness of this strategy. Recommending platforms like the Good News Network and others that focus on positive news stories can help address the very real mental health concerns of increasing time spent focused on news.
Abstain from Unreliable Information Spaces
Finally, while it may not always be popular, taking time to teach students why social media platforms are an unreliable source of information is essential. These platforms are “firmly grounded in beliefs about individualism, capitalism and consumerism,” not the pursuit of accuracy (Fister, 2021). Librarians might even encourage students to step away from these platforms when possible and to the extent they feel comfortable. This might mean deliberately limiting or eliminating social media accounts, or engaging in phone-free time, which some college students are choosing to do for a variety of other reasons (Beres, 2025). In the habit example above, this is the step when the triggers for the bad habit are removed from the environment, and it is essential to success in new habit formation. Helping students recognize what platforms they engage in that deliver mostly low-quality information is an information literacy issue.
Conclusion
Media literacy skills are essential to today’s college students, and academic librarians are among the few on campus with the expertise and skills to promote these skills for students. However, teaching students quick fact-checking strategies that they must remember and be motivated to use in the moment may not be effective in real-world environments for a variety of reasons, including the power of cognitive biases, the sway of parasocial relationships, the influence of algorithms and generative AI, and the systemic nature many of these problems. To teach students new habits, we should rely less on willpower and more on proactively/preemptively shaping information environments that help students feel empowered, informed, and positive (or at least realistic) about the information landscape.
It’s not as quick and easy as a fact-checking strategy, but helping students understand the information landscape and set up a more reliable information environment may have longer-lasting positive impacts than hoping to instill new habits for them that face considerable challenges to implement. It’s clear that we are facing more cynicism and disengagement from standards-based news and other authoritative information sources than we ever have before. Even with our limited resources, academic librarians can leverage our expertise to help with this major problem and move students towards a healthier relationship with online information. This foundational shift—from fact-checking individual claims to fostering a healthier, more intentional relationship with information—is arguably among the most critical skills college students can learn.
Acknowledgements
I would like to extend my sincere gratitude to editors Ian G Beilin, Jess Schomberg, and, especially, Brittany Paloma Fiedler, for their invaluable feedback throughout the editing process. I would also like to thank Amber Willenborg for her thoughtful peer review of the manuscript. The input of these reflective, considerate people greatly improved the story-telling and flow of the paper, and it ensured that it was as inclusive as possible. Finally, I would like to thank Andrea Baer for significantly contributing to the ideas behind this manuscript through our engaging, helpful, and inspiring discussions.
Works Cited
Ahmed, S., Masood, M., Deng, R., & Malviya, S. (2025). Why cynics disengage: the nexus of political cynicism, misinformation, and online political participation. Asian Journal of Communication, 35(5), 381-402. https://www.tandfonline.com/doi/pdf/10.1080/01292986.2025.2538142
Altay, S., De Angelis, A., & Hoes, E. (2024). Media literacy tips promoting reliable news improve discernment and enhance trust in traditional media. Communications Psychology, 2(1), 74. https://www.nature.com/articles/s44271-024-00121-5
Altay, S., Lyons, B. A., & Modirrousta-Galian, A. (2025). Exposure to higher rates of false news erodes media trust and fuels overconfidence. Mass Communication and Society, 28(2), 301-325. https://doi.org/10.1080/15205436.2024.2382776
Ashley, S., Craft, S., Maksl, A., Tully, M., & Vraga, E. K. (2023). Can news literacy help reduce belief in COVID misinformation? Mass Communication and Society, 26(4), 695-719. https://doi.org/10.1080/15205436.2022.2137040
Association of College & Research Libraries. (2016). Framework for information literacy for higher education. ACRL. https://www.ala.org/acrl/standards/ilframework
Aufderheide, P. (1993). Media literacy. A report of the national leadership conference on media literacy. Aspen Institute, Communications and Society Program. https://eric.ed.gov/?id=ED365294
Bailey, J. J. (2021). False beliefs and the illusion of explanatory depth. Journal of Business and Behavioral Sciences, 33(2), 54-64. https://asbbs.org/files/2021-22/JBBS_33.2_Fall_2021.pdf#page=55
Bargh, J. A., & Barndollar, K. (1996). Automaticity in action. The psychology of action, 457-481.
Barthell, M.; Mitchell, A.; and Holcomb, J. (2016, December 15). Many Americans believe fake news is sowing confusion. Pew Research Center. https://www.pewresearch.org/journalism/2016/12/15/many-americans-believe-fake-news-is-sowing-confusion/
Beres, D. (2025, November 5). The age of anti-social media is here. The Atlantic. https://www.theatlantic.com/magazine/2025/12/ai-companionship-anti-social-media/684596/
Bobkowski, P. S., & Younger, K. (2020). News credibility: Adapting and testing a source evaluation assessment in journalism. College & Research Libraries, 81(5), 822. https://doi.org/10.5860/crl.81.5.822
Bontridder, N., & Poullet, Y. (2021). The role of artificial intelligence in disinformation. Data & Policy, 3, e32. https://doi.org/10.1017/dap.2021.20
Borland, R. (2013). Understanding hard to maintain behaviour change: a dual process approach. John Wiley & Sons.
Breakstone, J., McGrew, S., Smith, M., Ortega, T., & Wineburg, S. (2018, March). Why we need a new approach to teaching digital literacy. Phi Delta Kappan, 99(6), 27-32. https://doi.org/10.1177/00317217187624
Brodsky, J. E., Brooks, P. J., Scimeca, D., Todorova, R., Galati, P., Batson, M., … & Caulfield, M. (2021). Improving college students’ fact-checking strategies through lateral reading instruction in a general education civics course. Cognitive Research: Principles and Implications, 6, 1-18. https://link.springer.com/article/10.1186/s41235-021-00291-4
Bull, A.C. (2021). Dismantling the evaluation framework. In the Library with the Lead Pipe. https://www.inthelibrarywiththeleadpipe.org/2021/dismantling-evaluation/
Camarillo, L. A. (2025). Squinting through the dawn of AI: Embedding algorithmic literacy principles in library instruction. ACRL 2025 Proceedings. https://www.ala.org/sites/default/files/2025-03/SquintingThroughtheDawnofAI.pdf
Cappella, J. N., & Jamieson, K. H. (1996). News frames, political cynicism, and media cynicism. The Annals of the American Academy of Political and Social Science, 546(1), 71-84. https://www.jstor.org/stable/pdf/1048171.pdf
Caulfield, M. (2019, June 19). SIFT (The four moves). Hapgood. https://hapgood.us/2019/06/19/sift-the-four-moves/
Chan, M. (2024). News literacy, fake news recognition, and authentication behaviors after exposure to fake news on social media. New Media & Society, 26(8), 4669-4688. https://doi.org/10.1177/146144482211276
Chan, M., Vaccari, C., & Yamamoto, M. (2024). Examining the relationship between dispositional news literacy and discernment of real and misleading news: Cross-national evidence. International Journal of Public Opinion Research, 36(2), edae020. https://doi.org/10.1093/ijpor/edae020
Chung, M. (2025). When knowing more means doing less: Algorithmic knowledge and digital (dis) engagement among young adults. Harvard Kennedy School Misinformation Review. https://misinforeview.hks.harvard.edu/wp-content/uploads/2025/10/chung_algorithmic_literacy_youth_20251013.pdf
Ciampaglia, G. L., Nematzadeh, A., Menczer, F., & Flammini, A. (2018). How algorithmic popularity bias hinders or promotes quality. Scientific Reports, 8(1), 1-7. https://doi.org/10.1038/s41598-018-34203-2
Clear, J. (2018). Atomic habits: An easy & proven way to build good habits & break bad ones: tiny changes, remarkable results. Random House Business.
Dai Y, Walther JB. (2018). Vicariously experiencing parasocial intimacy with public figures through observations of interactions on social media. Human Communication Research, 44: 322–342, https://doi.org/10.1093/hcr/hqy003.
Digital Inquiry Group. (n.d.). Teaching lateral reading | Civic online reasoning. Retrieved February 11, 2026, from https://cor.inquirygroup.org/curriculum/collections/teaching-lateral-reading/
Duckworth, A., White, R., Matteucci, A., Shearer, A., & Gross, J. (2016). A stitch in time: Strategic self-control in high school and college students. Journal of Educational Psychology, 108(3): 329-41. https://psycnet.apa.org/fulltext/2016-15978-003.pdf
Dunning, D. (2011). The Dunning–Kruger effect: On being ignorant of one’s own ignorance. In Advances in Experimental Social Psychology (Vol. 44, pp. 247-296). Academic Press.
Ecker, U. K., Lewandowsky, S., Swire, B., & Chang, D. (2011). Correcting false information in memory: Manipulating the strength of misinformation encoding and its retraction. Psychonomic Bulletin & Review, 18(3), 570-578. https://doi.org/10.3758/s13423-011-0065-1
Eddy, K.; Lipka, M.; Matsa, K. E.; Forman-Katz, N.; Liedke, J.; St Aubin, C.; & Wang, L. (2025, August 20). How Americans view journalists in the digital age. Pew Research Center. https://www.pewresearch.org/journalism/2025/08/20/how-americans-view-journalists-in-the-digital-age/
Edgerly, S. (2026). Developing the habit: The socialization of US teens into distinct repertoires of news consumption. Journal of Children and Media, 20(1), 132-150. https://doi.org/10.1177/14648849211012922
Fister, B. (2021). Lizard people in the library. PIL Provocation Series, 1(1). Project Information Literacy. https://files.eric.ed.gov/fulltext/ED613472.pdf
Fleming, J. (2014). Media literacy, news literacy, or news appreciation? A case study of the news literacy program at Stony Brook University. Journalism & Mass Communication Educator, 69(2), 146–165. https://doi.org/10.1177/1077695813517885
Fletcher, R., Andı, S., Badrinathan, S., Eddy, K. A., Kalogeropoulos, A., Mont’Alverne, C., … & Nielsen, R. K. (2025). The link between changing news use and trust: longitudinal analysis of 46 countries. Journal of Communication, 75(1), 1-15. https://academic.oup.com/joc/article/75/1/1/7907139
Gil de Zúñiga, H., & Diehl, T. (2019). News finds me perception and democracy: Effects on political knowledge, political interest, and voting. New Media & Society, 21(6), 1253-1271. https://journals.sagepub.com/doi/pdf/10.1177/1461444818817548
Gordon, L. T., & Thomas, A. K. (2017). The forward effects of testing on eyewitness memory: The tension between suggestibility and learning. Journal of Memory and Language, 95, 190-199. https://doi.org/10.1016/j.jml.2017.04.004
Haider, J., & Sundin, O. (2019). The fragmentation of facts and infrastructural meaning-making: new demands on information literacy. Information Research, 24(4), 24-4. https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=4698023
Haider, J., & Sundin, O. (2022). Paradoxes of media and information literacy: The crisis of information. Routledge.
Haider, J., & Sundin, J. H., Olof. (2023, September 21). What is infrastructural meaning-making and why do we need it? Information Matters. https://informationmatters.org/2023/09/what-is-infrastructural-meaning-making-and-why-do-we-need-it/
Hasell, A., & Halversen, A. (2024). Feeling misinformed? The role of perceived difficulty in evaluating information online in news avoidance and news fatigue. Journalism Studies, 25(12), 1441-1459. https://doi.org/10.1080/1461670X.2024.2345676
Hewitt, A. (2023). What Role Can Affect and Emotion Play in Academic and Research Information Literacy Practices?. Journal of Information Literacy, 17(1), 120-137. https://files.eric.ed.gov/fulltext/EJ1393880.pdf
Hicks, A., & Lloyd, A. (2021). Deconstructing information literacy discourse: Peeling back the layers in higher education. Journal of Librarianship and Information Science, 53(4), 559-571. https://link.springer.com/chapter/10.1007/978-3-030-43687-2_28
Hobbs, R. (1998). The seven great debates in the media literacy movement. Journal of Communication, 48(1), 16-32. https://mediaeducationlab.com/sites/default/files/Seven_Great_Debates_0.pdf
Hoffner, C. A., & Bond, B. J. (2022). Parasocial relationships, social media, & well-being. Current Opinion in Psychology, 45, 101306. https://doi.org/10.1016/j.copsyc.2022.101306
Jaiswal, J., LoSchiavo, C., & Perlman, D. C. (2020). Disinformation, misinformation and inequality-driven mistrust in the time of COVID-19: lessons unlearned from AIDS denialism. AIDS and Behavior, 24(10), 2776-2780. https://doi.org/10.1007/s10461-020-02925-y
Johnson, H. M., & Seifert, C. M. (1994). Sources of the continued influence effect: When misinformation in memory affects later inferences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(6), 1420-1436. https://psycnet.apa.org/fulltext/1995-04372-001.pdf
Kavanagh, J., & Rich, M. D. (2018). Truth decay: An initial exploration of the diminishing role of facts and analysis in American public life. https://www.rand.org/pubs/research_reports/RR2314.html
Kendeou, P., & O’Brien, E. J. (2014). The Knowledge Revision Components (KReC) framework: Processes and mechanisms. In D. N. Rapp & J. L. G. Braasch (Eds.), Processing inaccurate information: Theoretical and applied perspectives from cognitive science and the educational sciences (pp. 353–377). MIT Press.
Kim J, Song H. (2016). Celebrity’s self-disclosure on Twitter and parasocial relationships: a mediating role of social presence. Computers in Human Behavior, 62:570–577. https://doi.org/10.1016/J.chb.2016.03.083
Kurtin KS, O’Brien N, Roy D, Dam L (2018). The development of parasocial relationships on YouTube. The Journal of Social Media and Society, 7:233–252.
Lewandowsky, S., Ecker, U. K., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and its correction: Continued influence and successful debiasing. Psychological Science in the Public Interest, 13(3), 106-131. http://dx.doi.org/10.1037/a0039684
Lin, F., Chen, X., & Cheng, E. W. (2022). Contextualized impacts of an infodemic on vaccine hesitancy: The moderating role of socioeconomic and cultural factors. Information Processing & Management, 59(5), 103013. https://doi.org/10.1016/j.ipm.2022.103013
Lyons, B. A., Montgomery, J. M., Guess, A. M., Nyhan, B., & Reifler, J. (2021). Overconfidence in news judgments is associated with false news susceptibility. Proceedings of the National Academy of Sciences, 118(23), e2019527118. https://doi.org/10.1073/pnas.2019527118
Martínez-Costa, M. P., López-Pan, F., Buslón, N., & Salaverría, R. (2023). Nobody-fools-me perception: Influence of age and education on overconfidence about spotting disinformation. Journalism Practice, 17(10), 2084-2102. https://www.tandfonline.com/doi/full/10.1080/17512786.2022.2135128
Mont’Alverne, C., Badrinathan, S., Ross Arguedas, A., Toff, B., Fletcher, R., & Kleis Nielsen, R. (2022). The trust gap: how and why news on digital platforms is viewed more skeptically versus news in general. Reuters Institute. https://reutersinstitute.politics.ox.ac.uk/trust-gap-how-and-why-news-digital-platforms-viewed-more-sceptically-versus-news-general
Motta, M., Callaghan, T., & Sylvester, S. (2018). Knowing less but presuming more: Dunning-Kruger effects and the endorsement of anti-vaccine policy attitudes. Social Science & Medicine, 211, 274-281. Knowing less but presuming more_ Dunning-Kruger effects and the endorsement of anti-vaccine policy attitudes
Mulcahy, R., Barnes, R., de Villiers Scheepers, R., Kay, S., & List, E. (2025). Going viral: Sharing of misinformation by social media influencers. Australasian Marketing Journal, 33(3), 296-309.
Muraven, M. (2012). Ego depletion: Theory and evidence. The Oxford handbook of human motivation, 111, 126.
News Literacy Project (2024). News literacy in America: A survey of teen information attitudes, habits and skills. NLP-Teen-Survey-Report-2024.pdf
News Literacy Project (2025). ‘Biased,” “boring” and “bad”: Unpacking perceptions of news media and journalism among U.S. teens. NLP-Teens-and-News-Media-Report-2025.pdf
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220. https://doi.org/10.1037/1089-2680.2.2.175
Oreskes, N., & Conway, E. M. (2010). Defeating the merchants of doubt. Nature, 465(7299), 686-687.
Oswald, M. E., & Grosjean, S. (2004). Confirmation bias. Cognitive illusions: A handbook on fallacies and biases in thinking, judgement and memory. Psychology Press.
Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.
Park, S., Fisher, C., Fletcher, R., Tandoc Jr, E., Dulleck, U., Fulton, J., … & Yao, S. P. (2025). Exploring responses to mainstream news among heavy and non-news users: From high-effort pragmatic skepticism to low effort cynical disengagement. New Media & Society, 27(7), 4143-4163. https://journals.sagepub.com/doi/pdf/10.1177/14614448241234916
Peng, R. X., & Shen, F. (2025). Why fall for misinformation? Role of information processing strategies, health consciousness, and overconfidence in health literacy. Journal of Health Psychology, 30(8), 2030-2045. https://doi.org/10.1177/13591053241273647
Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39-50. https://doi.org/10.1016/j.cognition.2018.06.011
Pew Research Center. (2024, June 15). Most Black Americans believe U.S. institutions were designed to hold Black people back. https://www.pewresearch.org/race-and-ethnicity/2024/06/15/most-black-americans-believe-u-s-institutions-were-designed-to-hold-black-people-back
Pfänder, J., & Altay, S. (2025). Spotting false news and doubting true news: a systematic review and meta-analysis of news judgements. Nature Human Behaviour, 9(4), 688-699. https://www.nature.com/articles/s41562-024-02086-1
Pronin, E., Lin, D. Y., & Ross, L. (2002). The bias blind spot: Perceptions of bias in self versus others. Personality and Social Psychology Bulletin, 28(3), 369-381. https://journals.sagepub.com/doi/pdf/10.1177/0146167202286008?casa_token=ZS3Q-iOSxikAAAAA:Pgfv8gXr2LjWr3lxdBD5Evj8BcjLJpGW9F6GJFfbWqnj4OGpbJcCNQbQoklWWxwO7lv4yMauNnY
Raffio, N. (2024, October 28). Trust in voting: How misinformation threatens democracy. USC Today. https://today.usc.edu/trust-in-voting-how-misinformation-threatens-democracy/
Ross Arguedas, A., Robertson, C., Fletcher, R., & Nielsen, R. (2022). Echo chambers, filter bubbles, and polarisation: A literature review. The Royal Society. https://doi.org/10.60625/risj-etxj-7k60
Royzman, E. B., Cassidy, K. W., & Baron, J. (2003). “I know, you know”: Epistemic egocentrism in children and adults. Review of General Psychology, 7(1), 38-65. https://doi.org/10.1037/1089-2680.7.1.38
Rozenblit, L., & Keil, F. (2002). The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive science, 26(5), 521-562. https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog2605_1
Saunders, L. (2025). Information literacy as part of an interdisciplinary approach to combat misinformation. Information Research an International Electronic Journal, 30(CoLIS), 424-442. https://publicera.kb.se/ir/article/download/52318/43437
Schirmer, M., Walter, N., & Horvát, E. Á. (2025). Disparities by design: Toward a research agenda that links science misinformation and socioeconomic marginalization in the age of AI. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-178
Seifert, C. M. (2014). The continued influence effect: The persistence of misinformation in memory and reasoning following correction. In Rapp, D. & Braasch, J.L.G. (Ed.s.), Processing inaccurate information: Theoretical and applied perspectives from cognitive science and the educational sciences (pp 39-71.) MIT Press.
Skurka, C., Cheng, Z., Goyanes, M., & Gil de Zúñiga, H. (2026). News Finds Me as the illusion of competence: evidence for overconfidence in discernment of political misinformation. Human Communication Research, 52(1), 11-23. https://doi.org/10.1093/hcr/hqaf015
Skau, M. 2026, Feb 24. AI challenges our relationship with truth. Media Education Lab. Retrieved March 2, 2026, from https://mediaeducationlab.com/index.php/blog/ai-challenges-our-relationship-truth
Sloman, S., & Fernbach, P. (2018). The knowledge illusion: Why we never think alone. Penguin.
Spampatti, T. (2025). Truth discernment may not help to overcome misinformation. Nature Climate Change, 15(10), 1006-1009. https://www.nature.com/articles/s41558-025-02426-7
Stecula, D. A. (2025). Getting misinformation wrong: Why context fixes can’t solve structural problems [white paper]. University of Delaware Biden School of Public Policy & Administration; Stavros Niarcho Foundation Ithaca Initiative. https://udspace.udel.edu/server/api/core/bitstreams/3cac95b7-d97c-4929-8ec9-e88cbc76470a/content
Stein, R., Rutchick, A. M., Sin, A. Y., & Jarrin Rueda, L. F. (2025). Symbolic show of strength: a predictor of risk perception and belief in misinformation. The Journal of Social Psychology, 1-27. https://doi.org/10.1080/00224545.2025.2541206
Sullivan, M. C. (2019). Why librarians can’t fight fake news. Journal of Librarianship and Information Science, 51(4), 1146-1156. https://doi.org/10.1177/0961000618764258
Thi, P. V., & Ibrahim, A. (2025). Influencer credibility and authenticity in the fight against misinformation. Feedback International Journal of Communication, 2(3), 205-215. https://doi.org/10.62569/fijc.v2i3.199
Thorson, E. (2016). Belief echoes: The persistent effects of corrected misinformation. Political Communication, 33(3), 460-480. https://repository.upenn.edu/bitstreams/fde2b15d-38dd-4d96-9205-6ca7bfb356e2/download
Valgarðsson, V., Jennings, W., Stoker, G., Bunting, H., Devine, D., McKay, L., & Klassen, A. (2025). A Crisis of Political Trust? Global Trends in Institutional Trust from 1958 to 2019. British Journal of Political Science, 55, e15. https://doi.org/10.1017/S0007123424000498
van der Meer, T. G., & Hameleers, M. (2022). I knew it, the world is falling apart! Combatting a confirmatory negativity bias in audiences’ news selection through news media literacy interventions. Digital Journalism, 10(3), 473-492. https://doi.org/10.1080/21670811.2021.2019074
Van Der Meer, T. G., Hameleers, M., & Ohme, J. (2023). Can fighting misinformation have a negative spillover effect? How warnings for the threat of misinformation can decrease general news credibility. Journalism Studies, 24(6), 803-823. https://doi.org/10.1080/1461670X.2023.2187652
Wack, M., Duskin, K., & Hodel, D. (2024). Political fact-checking efforts are constrained by deficiencies in coverage, speed, and reach. arXiv preprint arXiv:2412.13280.
Walter, N., Cohen, J., Holbert, R. L., & Morag, Y. (2020). Fact-checking: A meta-analysis of what works and for whom. Political Communication, 37(3), 350-375. https://doi.org/10.1080/10584609.2019.1668894
Westlund, O., Belair-Gagnon, V., Graves, L., Larsen, R., & Steensen, S. (2024). What is the problem with misinformation? Fact-checking as a sociotechnical and problem-solving practice. Journalism Studies, 25(8), 898-918. https://www.tandfonline.com/doi/pdf/10.1080/1461670X.2024.2357316
Willenborg, A., & Detmering, R. (2025). ” I don’t think librarians can save us”: The material conditions of information literacy instruction in the misinformation age. College & Research Libraries, 86(4), 534. doi:https://doi.org/10.5860/crl.86.4.535
Wineburg, S. & McGrew, S. (2019). Lateral reading and the nature of expertise: Reading less and learning more when evaluating digital information. Teachers College Record: The Voice of Scholarship in Education, 121(11): 1-40. https://doi.org/10.1177/016146811912101102
Wood, W., Labrecque, J. S., Lin, P. Y., & Rünger, D. (2014). Habits in dual process models. Dual process theories of the social mind, 1, 371-85.
York, C., & Scholl, R. M. (2015). Youth antecedents to news media consumption: Parent and youth newspaper use, news discussion, and long-term news behavior. Journalism & Mass Communication Quarterly, 92(3), 681-699. https://doi.org/10.1177/1077699015588191
Zhou, Y., & Shen, L. (2024). Processing of misinformation as motivational and cognitive biases. Frontiers in Psychology, 15, 1430953. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1430953/pdf
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