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Abstract

Social media offer the opportunity for much public discourse, but also come with the potential to spread misinformation far and wide. This study investigates how older adults respond to misinformation on social media and how their responses vary by sociodemographic factors and digital skills. Based on survey data collected in 2023 from 2000 adults ages 60+, we find that many users take a multifaceted approach to assessing false or misleading information on social media. The most common strategies are reading the comments for validation and checking the source. The prevalence of such responses to misinformation highlights older adults’ active participation in information verification on social media. Findings also suggest that those who use the internet less frequently and those with lower social media skills are less likely to use such strategies suggesting that digital inequality is at play when it comes to responding to misinformation.

Keywords

misinformation online older adults online behavior digital inequality internet skills social media skills

Introduction

Older adults, defined by the United Nations as 60 and over (United Nations, 2017), have increasingly joined online platforms such as Facebook and YouTube (Auxier & Anderson, 2021). Social media platforms offer numerous advantages for connecting people, sharing ideas, and accessing information (Lin et al., 2019; Whiting & Williams, 2013). While there are many benefits of social media use for older adults, they are also fertile grounds for falsehoods (Aïmeur et al., 2023; Bode & Vraga, 2015; Chen, 2016; Enders et al., 2021; Vosoughi et al., 2018; Wang et al., 2019). As such users navigate social media, they are often confronted with the challenge of discerning credible content, a task that became particularly critical during the COVID-19 pandemic (Pehlivanoglu et al., 2022; Pennycook et al., 2020). Misinformation on social media is characterized by novel, extreme, emotional, or exaggerated claims and tends to garner attention and spread more rapidly than mundane or neutral content on these platforms (Tully et al., 2020; Vosoughi et al., 2018).

While some studies suggest that older adults may contribute to the spread of misinformation online more than younger adults (Grinberg et al., 2019; Guess et al., 2019), the extent to which this population responds to false information and what role sociodemographic factors and digital skills play in this process has been underresearched. Understanding these dynamics is crucial as the spread of misinformation can have serious consequences, including eroding public trust, encouraging misinformed health behaviors, and undermining compliance with public health measures during crises such as the COVID-19 pandemic (Barua et al., 2020; Bridgman et al., 2020; Hargittai, 2022). Existing research on the sharing of misinformation among older adults presents a nuanced picture, with findings that are both mixed and complex (Pehlivanoglu et al., 2022). For instance, a study by Guess and colleagues (2019), challenges the notion that misinformation-sharing is as prevalent among older adults as popular accounts might suggest (Guess et al., 2019) although they do find that older adults are the most likely to share such content on Facebook. Another study also found such patterns (Moretto et al., 2022) and noted that it may be a function of older adults being more likely to share political content, a variable the other piece had not considered. Since older adults are more likely to share political and partisan Web sites on platforms like Facebook compared to their younger counterparts, it is especially relevant to investigate what steps they may be taking to address misinformation.

In this study, we analyze survey data collected in Fall 2023 to examine what strategies older adults employ when they encounter misinformation and how these strategies may vary by user characteristics. Drawing on the theory of digital inequality (DiMaggio et al., 2004; Hargittai et al., 2018), we posit that both sociodemographic characteristics, and internet experiences and skills will link to whether and how many strategies older people use when encountering misinformation on social media. This research is one of the few studies examining misinformation responses by older adults, shedding light on the role of digital skills in shaping reactions to false information. Overall, the study contributes to a more nuanced understanding of how older adults navigate the complex landscape of misinformation on social media.

Users’ Interactions With Misinformation on Social Media

Social media platforms serve as significant conduits for information dissemination (Oriz-Ospina, 2019; Simon et al., 2015). While most social media users do not create misinformation, they often engage with it by reposting, retweeting, and replying, which can spread and amplify its reach (Vosoughi et al., 2018). Consequently, social media users play a critical role in both the spread and mitigation of misinformation. They can be an effective force in combating misinformation by responding with accurate information, evidence, and links to expert sources (Bode & Vraga, 2018). People report encountering significant amounts of misinformation on social media platforms (Fedeli et al., 2019). For instance, 57% of those who primarily obtained their news from social media had seen at least some misinformation about topics such as the COVID-19 pandemic (Mitchell, 2020). Research indicates that when exposed to misinformation, only 20% of users engage in some form of correction at least some of the time (Chadwick & Vaccari, 2019; Tandoc et al., 2020).

Fact-checking and credibility assessment have always been essential in combating misinformation (Walter et al., 2020). Our definition of credibility aligns with previous studies that define it as people’s subjective assessments of the trustworthiness of informational content (W. Choi & Stvilia, 2015; Metzger, 2007). It can derive from the perceived trustworthiness of the presenter, the channel, the message content, or a combination thereof (Metzger et al., 2003; Seo et al., 2021). Social media present new challenges for assessing credibility as the content is seldom subjected to editorial control, necessitating a greater role in gatekeeping by users (Bode & Vraga, 2018). Digital content can be easily created, altered, plagiarized, and misrepresented, which makes it challenging to trace the origins of content that shows up on social media (Wang et al., 2019). These challenges make users’ competencies in evaluating content a critical part of digital participation. In particular, credibility assessment is a key dimension of what scholars refer to as internet skills, that is, the ability to search for, access, and evaluate information online effectively and efficiently (W. Choi & Stvilia, 2015; Hargittai & Micheli, 2019; Kim & Yang, 2016). While search engines and platforms have become more sophisticated, users still need to interpret, compare, and verify content to avoid misleading and harmful information. Research shows, however, that many users, regardless of age, struggle with this process, often placing too much trust in top search results or relying on familiarity cues instead of verifying content quality (Hargittai et al., 2010; Rieh & Hilligoss, 2007; Seo et al., 2021).

To account for these necessary skills, scholars have introduced broad frameworks under the umbrella of media literacy, which typically includes the ability to access, analyze, evaluate, and even create media content (Livingstone & Helsper, 2010). Within this broader category, digital skills are a foundational element, referring to the ability to use internet technologies and perform tasks like locating and assessing information (Hargittai & Micheli, 2019; Litt, 2013). Social media skills form a specialized subset of digital skills that focus specifically on participatory platforms and include understanding how platforms function (e.g. privacy settings, hashtags, algorithms), how content spreads, and how to interact responsibly with others online (Fuchs et al., 2023; Nguyen et al., 2021). In this study, we do not distinguish between media literacy and social media literacy as entirely separate constructs. Rather, we treat digital skills and social media skills as interrelated components of the broader concept of media literacy, especially as it applies to misinformation on social media. These skills, both conceptual and procedural, help people navigate complex information environments, including assessing the credibility of content, identifying misinformation, and determining whether and how to respond.

Technology companies are often blamed for the rise of fake content on social media (Tandoc et al., 2020) and platforms have taken steps to send signals to help users decipher the credibility of the content they encounter on such sites (Jahanbakhsh et al., 2021). For instance, Facebook has partnered with third-party fact-checkers to debunk viral fake news posts, aiming to enhance public understanding and evaluate the accuracy of information (Jahanbakhsh et al., 2021). The platform also allows users to report false content and offers features like commenting and private messaging, which can be used to challenge or correct misleading content (Bode & Vraga, 2018). However, research shows that many users do not engage with these tools or respond to such cues (Tandoc et al., 2020), highlighting the crucial role of user skills in addressing misinformation.

Research has shown that older adults are more vigilant than younger users about the credibility of online information (Seo et al., 2021; Zulman et al., 2011) and perceive it to be less credible than printed information (W. Choi & Stvilia, 2015). For instance, studies focusing on COVID-19 during the initial lockdowns suggested that older adults are less likely to believe misinformation, particularly in the realm of public health (Hargittai, 2022; Roozenbeek et al., 2020).

When older adults engage with misleading content, they use specific fact-check techniques, relying on their perception of source credibility and triangulation, skills that reflect aspects of digital skills, such as consulting multiple sources to verify information (Flintham et al., 2018; Munyaka et al., 2022). Compared to younger users, participants aged 45 and over are more likely to challenge misinformation (Gurgun et al., 2024). One study found that older adults conduct thorough research on certain content (e.g. news articles, social media updates) before determining its accuracy, utilizing fact-checking sites and foreign journalistic services in combination with source and familiarity indicators (Sharevski & Loop, 2023).

Existing research indicates that age-related factors, such as cognitive ability, internet experience, and social beliefs contribute to how older adults respond to misinformation (Brashier & Schacter, 2020; De Keersmaecker & Roets, 2017). Some evidence suggests that older adults can better discern true and false information once they improve their digital skills for verifying the credibility of information. In addition, they frequently reject claims that contradict their knowledge, even when these falsehoods feel familiar (Moore & Hancock, 2022; Sharevski & Loop, 2023). While older adults can leverage their digital skills to assess the credibility of the information they encounter online, research indicates that distinguishing the credibility of online content itself from that of its sources presents an ongoing challenge (Seo et al., 2021). In addition, some older adults may face difficulties in acquiring the necessary digital skills to evaluate the credibility of online information (Schreurs et al., 2017).

The current literature informs our investigation by emphasizing the importance of examining the specific strategies older adults use when encountering misinformation. Despite the insights provided by existing scholarship, gaps remain in understanding the responses of older adults to misinformation, particularly how their internet usage experiences and sociodemographic characteristics shape these responses.

Digital Inequality and Internet Skills in Older Adults

Research has shown that users with higher levels of internet skills are better equipped to navigate the complexities of the digital realm, including a better understanding of how information is disseminated online as well as identifying and navigating through false or misleading content (Vissenberg et al., 2023). However, there is a notable gap in understanding whether digital skills such as social media skills, link to how people respond to misinformation online. Recognizing these disparities, studies underscore the significance of providing digital skills and information literacy education to digitally disadvantaged groups, especially for users who are less educated, minorities, and of lower socioeconomic status (Czaja et al., 2013; Seo et al., 2021). For instance, a digital literacy intervention enabled older adults to identify misinformation online and enhance their ability to distinguish fact from fiction (Moore & Hancock, 2022). A lack of online skills can be a significant barrier, contributing to perceptions of the internet as an untrustworthy and overwhelming space (Hakkarainen, 2012). Issues such as information overload and difficulty in navigation further emphasize the need to improve internet skills (Czaja et al., 2013; van Deursen & van Dijk, 2009).

While older adults can leverage their digital skills to assess the credibility of the information they encounter online, their ability to do so is influenced by a combination of cognitive functioning and physical ability. Research highlights the complex, bidirectional relationship between cognitive performance and technology use in later life (E. Y. Choi et al., 2021; Gell et al., 2015). Scholars (E. Y. Choi et al. 2021) found that not only does better episodic memory predict subsequent information and communication technology (ICT) use, but using ICTs also contributes to improved memory and executive functioning over time. These findings suggest that technology engagement can be both a product and a driver of cognitive health.

Some work has found that background user characteristics such as age, gender, and education have been associated with the dissemination and assessment of misinformation (Brashier & Schacter, 2020; Morosoli et al., 2022). The idea that older adults are more susceptible to online misinformation is based on the notion that they are “lagging behind” the rest of the population in their use of digital technology (Jacobson et al., 2017; Pavićević, 2023; van Deursen & van Dijk, 2011). However, studies have shown that age in relation to digital inequality is often more complex than it seems as it involves multiple factors such as physical access, digital skills, information and communication technologies utilization as well as socioeconomic factors (Hargittai & Dobransky, 2017; Hargittai & Palfrey, 2025; Vivion et al., 2024).

Digital inequality emphasizes disparities in access to, skills with, and uses of digital technologies, highlighting how sociodemographic factors influence online experiences. In the context of older adults, digital inequality has been applied to understanding the breadth of internet use (Hargittai & Dobransky, 2017), including the effects of misinformation (Vivion et al., 2024). Specifically, a study by Vivion et al. (2024) questions the concept of vulnerability to misinformation, suggesting that age alone does not explain the ability to spot misinformation, highlighting significant heterogeneity among older adults. Results show that older adults are critical toward false information and understand the necessity for caution while using the internet. Nonetheless, research on age and misinformation remains inconclusive, with studies showing that older social media users are more likely to engage with potentially misleading content (Grinberg et al., 2019; Guess et al., 2019). In addition, gender plays a significant role in dealing with misinformation, with men being more likely to engage with misinformation compared to women (Chadwick & Vaccari, 2019; Grinberg et al., 2019). Other findings suggest that next to age and gender, education is another crucial factor in the dealing with misinformation. Those with higher education are better able to identify and assess false information (Seo et al., 2021).

In light of existing research highlighting the need for a deeper understanding of the nuances of older adults’ internet use and skills (Hunsaker & Hargittai, 2018), this study investigates the actions older adults take when they encounter what they perceive as misinformation on social media. Specifically, we aim to explore the interplay between sociodemographic factors, internet experiences and skills, and responses to misinformation among older adults.

Research Questions

This study builds on existing literature by exploring the specific responses of adults 60+ to misinformation on social media through the following research questions:

RQ1: How popular are various response strategies to misinformation on social media among adults aged 60+?

RQ2: What is the relationship between older adults’ internet experiences and skills, and their response strategies to misinformation on social media?

RQ3: What sociodemographic factors relate to which and how many types of response strategies older adults employ when encountering misinformation on social media?

Methods

In Fall 2023, we surveyed 2000 adults aged 60+ in the United States about their internet uses including their responses to misinformation on social media. We contracted with YouGov, an established online market research and data analytics company. YouGov employs a diverse recruiting strategy, utilizing both online and offline channels to assemble a varied and representative panel. Respondents are selected based on demographic attributes (e.g. age, gender, race, education) and other pertinent criteria, such as geographic region and voting behavior, to ensure diversity within the sample. To maintain representativeness, YouGov actively employs sophisticated sampling and weighting methods, addressing potential biases in the data (YouGov, 2024). Weights used here consider age, gender, race/ethnicity, and education to represent the US older adult population. All respondents successfully passed an attention-check question (Berinsky et al., 2014).

Because the research questions concern experiences on social media, we restrict the analyses to social media users. The survey asked about people’s experiences with eight of the most popular platforms (Facebook, Pinterest, Instagram, Twitter, LinkedIn, TikTok, WhatsApp, Reddit) by inquiring how often, if ever, respondents use them. The answer options were never, occasionally, and regularly. Three percent of participants responded with never to all of them, these people are excluded from the analyses resulting in a sample of 1941 social media users.

Dependent Variable

We developed a list of misinformation response strategies based on existing literature (Munyaka et al., 2022; Wasike, 2023; Zhou et al., 2023). The survey asked the following question: “Have you ever done any of the following in response to content you saw on social media that you thought was false? Check all that apply.” with these response options:

Explicitly challenged, corrected, or questioned the content;

Checked the source of the content to verify its credibility;

Used a fact checker website to verify the content;

Searched for other sources to verify the content;

Read the comments to see if other users shared your perspective;

Used your intuition or “gut feeling” to know if the content was true or false;

Defriended or unfollowed someone because they shared the content;

Reported or flagged the content;

None of the above.

We created two recodes of the measure: (a) a continuous variable ranging from 0 to 8 indicating how many of the misinformation strategies respondents reported using; and (b) a variable for whether the respondent used any of the misinformation strategies. One respondent selected both a response strategy and “none of the above,” this case is coded as missing.

Independent Variables

Sociodemographic Variables

Information about age, gender, race and ethnicity, education level, and employment status come from YouGov’s panel data. We calculated respondents’ age by subtracting their birthyear from 2023. YouGov measures gender by offering two options: male and female, which we recoded into a binary female variable (1 = female, 0 = not female). For race, we have dummy variables indicating White, Black, Asian, and Native American. For ethnicity, we have a dummy variable for Hispanic background. For education level, YouGov offers six answer options (i.e. no high school, high school graduate, some college, 2-year, 4-year, and post-grad), which we recoded into three dummy variables reflecting: high school or less, some college, and college degree or more.

As income may not be applicable to many in this age group given high rates of retirement, we assess socioeconomic status by asking respondents whether they go on at least one vacation per year away from home, whether they dine out at least once a monthy, and whether they consume fresh fruits or vegetables once a day with the following answer options: yes; no, cannot afford it; no, another reason. Those who indicated not being able to afford the resource were coded as lacking that resource (binary variable). We then recoded these into a continuous variable representing the number of inaccessible resources (i.e. those they cannot afford) resulting in a continuous variable with values 0-3. We measured respondents’ employment status in nine categories, namely, employed full-time, employed part-time, temporarily laid off, unemployed, retired, disabled, homemaker, student, and other. We recoded these into one dummy variable versus: employed (full-time or part-time) not employed.

The survey measured metropolitan status by asking respondents whether they live in a big city, the suburbs or outskirts of a big city, a town or a small city, or a rural area. We recoded this variable to a set of dummy variables for rural, suburban, and urban (first and third options) residence. Finally, we asked participants if they have any long-lasting conditions (e.g., blindness or severe vision impairment even with glasses or contact lenses, deafness or a severe hearing impairment even with a hearing aid, serious difficulty having your speech understood) and created a dummy variable for having any disability.

Internet Experiences and Skills

We included four measures related to respondents’ internet experiences and skills. To assess autonomy of use and frequency of use, we rely on one survey question recoded in different ways. The question asked: “How often do you access the Internet using the following devices?” applied to three device types: mobile phone, tablet, and desktop or laptop computer. Answer options were: no such access, almost constantly, several times a day, about once a day, few times a week, less often, never. To measure autonomy of use, we first recoded whether respondents had access to the device at all and then created a binary variable that represents whether respondents have access to all three versus fewer devices (0 = anything below three, 1 = access to all three, which indicates high autonomy). To measure frequency of internet use, we created a dummy variable reflecting frequent internet use representing those who chose either of the first two answer options for any of the devices.

Our measure of social media skills comes from an established instrument (e.g. Nguyen et al., 2021) that asks participants to rate their understanding of six terms (“privacy settings,” “meme,” “tagging,” “followers,” “viral,” “hashtag”) on a scale of 1 (no understanding) to 5 (full understanding). We calculate the average of these scores.

Sample Characteristics

Table 1 displays the unweighted and weighted sample characteristics (N = 2000), as well as the weighted social media user sample characteristics (N = 1941). The mean age of our weighted social media user sample is 70 years (minimum: 60, maximum 94, standard deviation: 7.3). Over half (54.5%) of the sample identified as female. Most of the respondents are White (73.4%), followed by Hispanic (10.7%), and Black (10.0%). In terms of educational attainment, a little under half (42.2%) of our sample had no more than a high school degree, about a quarter completed some college (25.2%), and about a third (32.6%) held at least a college degree. For socioeconomic status, the mean value of inaccessible resources (those resources respondents cannot afford) was 0.5 (SD = 0.8). About a fifth of the sample indicated that they were employed (21.3%). Close to half (45.9%) of the sample lived in an urban area, a third (34.0%) in the suburbs, and a fifth (20.2%) in rural areas. Regarding disability, about a quarter of the sample indicated some type (25.7%).

Table 1.

Sample Characteristics (Unweighted and Weighted).

	Full Sample(N = 2000)Unweighted			Full Sample(N = 2000)Weighted			Social Media User Sample (N = 1941)Weighted
	%	M	SD	%	M	SD	%	M	SD
Age		70.2	7.1		70.7	7.3		70.6	7.3
Female	54.2			54.6			54.5
Race and ethnicity
White	75.0			73.4			73.4
Hispanic	9.4			10.9			10.7
Black	9.8			10.0			10.0
Asian	2.3			2.2			2.3
Native American	0.7			0.6			0.6
Education
High school or less	39.5			42.9			42.2
Some college	26.2			25.1			25.2
Bachelor’s degree or more	34.4			32.0			32.6
Lacking in socioeconomic resources		0.5	0.8		0.5	0.8		0.5	0.8
Employed	21.8			20.8			21.3
Metropolitan status
Rural	20.4			20.3			20.2
Suburban	33.8			34.2			34.0
Urban	45.9			45.5			45.9
Disability	26.0			25.9			25.7
Internet experiences
High user autonomy	86.5			85.8			86.0
Frequent user	89.8			89.5			89.8
Social media skills (1–5)		3.1	1.2		3.0	1.2		3.1	1.2

Regarding autonomy of use, a large majority (86.0%) of our respondents indicated having access to all three types of devices (mobile, tablet, and desktop computer). An even larger portion (89.8%) identified as frequent internet users, accessing at least one of the specified devices at least several times a day. The sample had an average score of 3.1 for social media skills, on a scale of 1 to 5 (standard deviation: 1.1). Overall, the sample consists of people with diverse sociodemographic backgrounds and varying levels of internet experiences and skills.

Analytical Procedure

To address the first research question, we report on the prevalence of using various misinformation response strategies. Next, we examine the relationship of internet experiences and skills with the use of misinformation strategies (RQ2), using bivariate and then regression analyses. The bivariate relationships reflect the results of crosstabulating two categories (e.g. autonomy and using any response strategy). In the case of the continuous variable of skills, we crosstabulate being in the lowest quartile of skill with response strategy use and then the highest quartile of skill with response strategy use. To address the third research question, we look at how use of misinformation response strategies relates to sociodemographic factors using both logit (any such use) and OLS (number of response strategies used) regressions, first only including the sociodemographic variables and then adding internet experiences and skills to see whether the findings about sociodemographic differences are robust to holding these constant.

Results

Prevalence of Misinformation Response Strategies

Two-thirds of participants (65.6%) report employing at least one strategy when encountering false information on social media (see Table 2). The most popular approach is to read comments to see if other users shared the respondent’s perspective (35.0%), followed by checking the source of the content to verify its credibility (34.7%). Defriending or unfollowing someone because they shared the content was the least used misinformation response (15.3%) of the ones listed on the survey, perhaps not surprisingly as it is a much more drastic and permanent action. On average, respondents used two strategies to deal with misinformation.

Table 2.

Prevalence of Misinformation Approaches (N = 1940).

	Percent	M	SD
Number of response strategies		2.1	2.1
Any response strategy	65.6
Read comments	35.0
Checked source to verify its credibility	34.7
Searched for other sources	32.6
Used intuition or “gut feeling”	24.0
Challenged, corrected, questioned content	23.4
Used fact checker website	23.3
Reported or flagged content	17.7
Defriended or unfollowed someone	15.3

How Internet Experiences and Skills Relate to Using Misinformation Strategies

Table 3 shows the bivariate relationships of digital experiences and using misinformation response strategies. There is no statistically significant difference by level of user autonomy. In terms of frequency of internet use, those who use digital media several times a day or almost constantly are much more likely (68.5%) to employ misinformation responses than less frequent users (40.1%). Frequent users also utilize a higher mean number of response strategies (2.2) compared to less frequent users (0.9). Regarding social media skills, respondents in the highest skill quartile are much more likely to turn to response strategies (85.0%) than the least skilled (45.4%). There is an especially large difference between the highest skilled when it comes to their average number of response strategies (3.2) compared to the least skilled (1.1). Overall, these findings suggest that two markers of digital inequality—frequent internet use and higher social media skills—are positively associated with higher likelihood of engaging in misinformation response strategies. Table 4 presents the results of regression analyses examining the same relationships to see if the above findings are robust to holding the other factors constant. This is indeed the case, user autonomy remains unrelated to employing response strategies while use frequency and social media skills remain positively related to it.

Table 3.

Bivariate Relationship of Digital Experiences and Misinformation Response Strategies.

	Use of any response strategy—percent	Number of strategies used
Less autonomy	61.4	1.9
Robust autonomy	66.3	2.1
Less frequent use	40.1***	0.9***
Frequent use	68.5***	2.2***
Lowest skill quartile	45.4***	1.1***
Highest skill quartile	85.0***	3.2***

***

p < .001.

Table 4.

Regressions on the Relationship of Internet Experiences and the Use of Any (logit) and Number of (OLS) Misinformation Response Strategies.

	Any response strategies		Number of response strategies
	Coef.	SE	Coef.	SE
Robust autonomy	-0.09	0.17	-0.03	0.13
Frequent use	0.90***	0.19	0.84***	0.15
Social media skills	0.61***	0.05	0.69***	0.04
Constant	-2.02***	0.26	-0.81***	0.19

Note. N = 1941. b = unstandardized coefficient; SE = standard error.

***

p < .001.

How Sociodemographic Factors Relate to Using Misinformation Response Strategies

Table 5 presents the regression analyses examining how sociodemographic factors relate to using misinformation response strategies among older adults on social media. The left side of the table looks at the use of any such strategies while the right side looks at the number of strategies. In both cases, the first model (on the left) only includes sociodemographic variables while the second model adds digital experiences to see whether the findings about user background are robust to holding these factors constant.

Table 5.

Regression on the Use of Any (logit) and Number of (OLS) Misinformation Response Strategies.

	Any response strategies				Number of response strategies
	Model 1		Model 2		Model 1		Model 2
	Coef.	SE	Coef.	SE	Coef.	SE	Coef.	SE
Age	-0.04***	0.01	-0.01	0.01	-0.05***	0.01	-0.01	0.01
Female	-0.03	0.12	-0.08	0.12	-0.22*	0.09	-0.28***	0.09
Race and ethnicity (base = White)
Hispanic	-0.20	0.24	-0.25	0.28	-0.34*	0.15	-0.38**	0.14
Black	-0.65**	0.21	-0.57*	0.23	-0.82***	0.16	-0.67***	0.15
Asian	-1.06***	0.33	-0.77*	0.38	-1.06***	0.31	-0.64*	0.29
Native American	-1.15	0.63	-1.11	0.64	-1.54*	0.61	-1.36*	0.56
Education (base = High school or less)
Some college	0.48***	0.14	0.35*	0.15	0.74***	0.12	0.55***	0.11
Bachelor’s degree or more	0.65***	0.13	0.47***	0.14	0.98***	0.11	0.73***	0.11
Lacking in socioeconomic resources	-0.16*	0.07	-0.12	0.08	-0.11	0.06	-0.06	0.06
Employed	-0.06	0.14	-0.15	0.16	0.01	0.12	-0.07	0.11
Metropolitan status (base = Rural)
Suburban	0.13	0.16	0.05	0.17	0.07	0.13	-0.01	0.12
Urban	0.23	0.15	0.14	0.16	0.14	0.12	0.03**	0.12
Disability	0.34*	0.13	0.38**	0.15	0.27*	0.11	0.29**	0.10
Internet experiences
Autonomy of use			0.18	0.16			0.11	0.12
Frequency of use			0.90***	0.21			0.77***	0.14
Social media skills			0.59***	0.06			0.62***	0.04
Constant	2.84	0.65	-1.80	0.82	4.97	0.51	0.12	0.56

Note. N = 1941. b = unstandardized coefficient; SE = standard error.

p ⩽ .05. **p ⩽ .01. ***p ⩽ .001.

There are two findings that do not hold once controlling for digital experiences: in the first set of models, the younger old are less likely to use response strategies when encountering misinformation on social media as are those with fewer economic resources. However, both of these findings disappear once we control for digital experiences. In terms of race and ethnicity, Black and Asian respondents are less likely to use response strategies than Whites. Education is positively linked with turning to response strategies, the higher education, the higher likelihood that a user does this. Disabled people are more likely to employ response strategies than their non-disabled counterparts.

Regarding number of response strategies used, age is again negatively linked, but again only when not taking digital experiences into consideration. Concerning race and ethnicity, all groups use fewer strategies than Whites. Higher education associates with more response strategies; disabled respondents use more than non-disabled respondents. These findings are all robust to the inclusion of the digital experience variables.

Overall, these results indicate that several markers of inequality relate to the use and number of misinformation response strategies among older adults. Only in the case of disability is this in the direction where the less privileged are more likely to use response strategies and use more of them. Furthermore, findings reported earlier about the internet use frequency and social media skills are robust to controlling for sociodemographic background.

Discussion

Drawing on a national survey of older adults in the United States, this study explored the prevalence of misinformation approaches on social media and examined factors influencing users’ responses to misinformation. Many older adults (just under two-thirds) take a proactive approach to verifying information online by employing various strategies such as reading comments to assess whether others shared their perspective on the content and checking the source for credibility. These results indicate a multifaceted approach to information verification, highlighting older adults’ proactive engagement in maintaining the accuracy of their online environment. This adaptability is promising as it reflects a population that actively participates in information verification and quality control on social media platforms.

Bivariate analyses revealed associations between digital experiences and misinformation responses. Frequent internet users were more likely to employ multiple strategies to combat misinformation, suggesting that increased online activity may necessitate or result in a more vigilant approach. Similarly, higher social media skills were strongly associated with a greater likelihood of employing response strategies. These findings emphasize the role of digital literacy in enabling users to navigate the complexities of the online information landscape effectively. As previous studies have shown (Seo et al., 2021; Vissenberg et al., 2023), recognizing the clear significance of internet skill—in this case social media skills—becomes crucial for understanding people’s online behaviors. The findings emphasize the importance of incorporating measures of online abilities in data sets, as too few studies of older adults currently include such metrics (Hargittai et al., 2018 is an exception). This scarcity hampers our ability to explore how digital skills may influence people’s engagement with misinformation. Since understanding internet operations is a teachable skill that can be improved through intervention (Moore & Hancock, 2022), it can be an important process for addressing digital inequality. This consideration gains relevance when studying older adults, a group that often remains the least connected among all age cohorts. Examining digital skills in the context of misinformation response strategies is essential for not only understanding this cohort’s online activities but also for identifying opportunities for intervention and education. For instance, workshops covering social media platform basics, discerning credible sources, and identifying common misinformation tactics can enhance social media skills. This insight can contribute to highlighting the importance of accessible design and interventions to enhance technology accessibility for all users.

The regression analyses further illuminated the factors influencing the use and number of misinformation strategies among older adults. We observe no differences between women and men. Other factors such as age, race and ethnicity, education, and disability status significantly correlate with the likelihood of using misinformation strategies. Specifically, younger old users were more likely to employ strategies confirming the importance of disaggregating the experiences of older adults rather than thinking of them as one homogeneous group (Stone et al., 2017). But once controlling for digital experiences and skills, this finding was no longer significant, highlighting why it may be that the younger old are more engaged in this domain. Higher education levels are related to increased likelihood of using response strategies, which aligns with existing literature suggesting the role of education in shaping people’s digital behaviors and information processing (Hargittai & Dobransky, 2017; Morosoli et al., 2022; Seo et al., 2021).

Disability is also associated with increased engagement in misinformation responses, prompting an examination of the intricate relationship between disability and the digital realm. The capabilities, features, and affordances of digital technologies offer the potential to enhance information, communication, and media accessibility for disabled people (Goggin, 2021). Online communities such as disability-focused chat rooms and discussion groups help alleviate the social isolation that many disabled people experience (Hamill & Stein, 2011; Obst & Stafurik, 2010). In addition, research indicates that disabled people are more likely to seek out health and government sources compared to those without disabilities (Dobransky & Hargittai, 2006). This dependence on online platforms for both social interaction and information increases the need for the viewed content to be accurate and reliable. Consequently, this heightened reliance may drive disabled people to be more proactive in identifying and responding to misinformation.

Although our study did not directly measure cognitive functioning, prior work suggests that cognitive abilities, especially episodic memory, are strongly linked to older adults’ digital participation. Choi et al. (2021) found a bidirectional relationship between information and communication technology (ICT) use and memory performance, indicating that digital engagement can support cognitive function, while better cognitive functioning also predicts higher future ICT use.

As with all studies, this one has limitations. Self-reported data on response strategies may be influenced by social desirability bias as people may feel that they should be doing something about false information they encounter. Another potential shortcoming is that the survey included a limited number of possible response strategies. While we selected these strategies based on findings from existing literature (Munyaka et al., 2022; Wasike, 2023; Zhou et al., 2023), there may be other responses to misinformation that we did not capture. While cognitive function was not directly measured in this study, its known association with digital engagement among older adults (E. Y. Choi et al., 2021) suggests it may be an important factor shaping misinformation response behaviors. This data set comes from a nationally representative sample and thus is quite diverse, nonetheless, it lacks comparisons to users in other nations. Future research should test whether the findings from this study generalize to other national contexts. Furthermore, the cross-sectional design of this study captures data at a single point in time. This design limits the ability to establish causation or track changes over time. Longitudinal and experimental studies on generalizable populations could address these limitations. In addition, while this study provides insights into the number of misinformation response strategies employed by older adults, we did not conduct an in-depth analysis of individual strategies, including how they might differ in terms of cognitive or social demands, or whether certain strategies are commonly used together. Future research should examine which factors, such as education, race, or digital experience, are associated with specific types of responses, and whether certain strategies are commonly used in combination. Finally, this study does not examine variations in misinformation responses across different social media platforms. Each platform may require distinct skills and strategies to discern false information, warranting further investigation into platform-specific dynamics.

Conclusion

Our investigation into misinformation responses among older adults on social media yields findings heretofore unexplored in the literature about how strategies vary by digital experiences and sociodemographic factors. The findings underscore the importance of considering both social media skills and sociodemographic factors in comprehending users’ engagement with misinformation. Data on nationally representative samples about coping with misinformation are rare, which is an important unique contribution of this article. The study adds to the evolving discourse on misinformation in the digital age, offering insights that can inform educational initiatives and intervention strategies tailored to the particular needs of older adults. By revealing that at least some older adults are aware of and open to using diverse strategies to respond to misinformation, the findings underscore the potential for targeted interventions to enhance digital literacy and equip a broader set of users with tools and methods to question, scrutinize, and counter misinformation. Furthermore, the results emphasize the necessity of comprehensive educational programs that not only raise awareness but also actively engage older adults in practicing these strategies. Such efforts are crucial for fostering a more informed and resilient online community.

Footnotes

Acknowledgements

The authors are grateful to Becca Smith for her contributions to the survey construction.

ORCID iDs

Annalise Baines

Eszter Hargittai

Ethical approval

This study was approved by the University of Zurich’s Research Ethics Committee.

Participant consent

All participants consented to participate in the study.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The survey data collection was supported by a grant from the John D. and Catherine T. MacArthur Foundation.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Not applicable.

Author biographies

Annalise Baines (PhD University of Kansas) is a Senior Research and Teaching Associate at the Department of Communication and Media Research of the University of Zurich. Her research interests include the role of digital communication in effecting positive change within the area of health and science communication. Specifically, she aims to enhance groups’ digital access and information literacy among marginalized and underserved populations.

Eszter Hargittai (PhD Princeton University) is professor and chair of Internet Use and Society at the Department of Communication and Media Research, University of Zurich. She is author of, with John Palfrey, “Wired Wisdom: How to Age Better Online” (2025, University of Chicago Press).

John Palfrey is President of the John D. and Catherine T. MacArthur Foundation. He has taught as a full-time and visiting professor of Law at Harvard Law School. He is author, most recently, of “Wired Wisdom: How to Age Better Online” with Eszter Hargittai (2025, University of Chicago Press) and of “Born Digital: How Children Grow Up in a Digital Age” with Urs Gasser (2016, Basic Books).

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Older Adults’ Response Strategies to Misinformation on Social Media

Abstract

Keywords

Introduction

Users’ Interactions With Misinformation on Social Media

Digital Inequality and Internet Skills in Older Adults

Research Questions

Methods

Dependent Variable

Independent Variables

Sociodemographic Variables

Internet Experiences and Skills

Sample Characteristics

Analytical Procedure

Results

Prevalence of Misinformation Response Strategies

How Internet Experiences and Skills Relate to Using Misinformation Strategies

How Sociodemographic Factors Relate to Using Misinformation Response Strategies

Discussion

Conclusion

Footnotes

Acknowledgements

ORCID iDs

Ethical approval

Participant consent

Funding

Declaration of conflicting interests

Data availability statement

Author biographies

References