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Abstract

Social media foster a News-Finds-Me perception (NFM), whereby individuals believe important news will “find them” without actively seeking it. Although the negative consequences of NFM are well-documented, strategies to mitigate NFM’s effects remain unexplored. In a pre-registered 3 (educational warning: general vs. personalized vs. control) ✕ 2 (NFM level: low vs. high) between-subjects experiment (N = 405), we test whether general educational warnings (forewarning individuals about NFM and its detrimental outcomes) and personalized warnings (targeting specific NFM groups) affect political news learning and NFM reliance. Low-NFM participants exhibited greater political learning from news stories and greater intention to reduce NFM than high-NFM participants. Neither warning reduced these gaps. However, a personalized warning (vs. general) elicited stronger defensive reactions among high-NFM participants, which in turn negatively predicted political learning and intention to reduce NFM. These findings underscore the need to thoughtfully develop intervention strategies that avoid triggering unintended reactions among high-NFM individuals.

Keywords

News-Finds-Me perception warning message political learning news literacy cognitive elaboration defensive reactions news consumption political news

Social media have become an integral source for news consumption, with over half of U.S. adults (54%) now consuming news through these platforms (Pew Research Center, 2024). With advanced algorithms recommending news content to social media users, many individuals have developed a News-Finds-Me perception (NFM). NFM refers to the belief that individuals can stay informed about public affairs without actively seeking news, under the impression that important news will “find them” through general Internet use and their social networks (Gil de Zúñiga et al., 2017). This perception is largely fueled by the ubiquitous nature of social media, and an ambient information environment, driving reliance on it for news and public affairs (Gil de Zúñiga and Cheng, 2021).

Previous research has shown the detrimental effects of holding a NFM, including increased political cynicism (Song et al., 2020) and the preference for soft news (Skurka et al., 2023), alongside decreased political knowledge and interest (Gil de Zúñiga and Diehl, 2018). However, studies assessing NFM’s effect on political knowledge have primarily been based on longitudinal survey data in which participants are asked to answer questions about their general political knowledge of ongoing public affairs (e.g., Gil de Zúñiga et al., 2017; Song et al., 2020). Although these findings provide insights into how NFM can undermine an informed citizenry, they do not capture how individuals actually engage with specific news content (i.e., a singular news story) and the extent to which they acquire knowledge from the news story in a tightly controlled environment. It is unknown whether high-NFM individuals’ lack of political knowledge is simply due to limited exposure to news (Skurka et al., 2023), or if they also fail to critically think about arguments presented in the news (i.e., cognitive elaboration) because they lack the motivation and proper abilities to process the news (Skurka et al., 2025). Thus, this study aims to conceptually replicate the finding that the NFM reduces political learning by testing how much high- and low-NFM individuals learn from political news stories they are explicitly asked to read.

In addition, though the negative consequences of the NFM have been well-documented, there remains a critical gap in understanding how to mitigate its impacts. We examine the effects of educational warning messages (either generalized or personalized based on NFM level) as possible interventions to counteract the adverse effects of NFM. In doing so, we consider both the intended and unintended consequences of such warning messages. First, drawing upon relevant literature on news media literacy, we propose that educational warning messages can alert individuals to potential cognitive biases, such as NFM (in which individuals mistakenly perceive themselves as sufficiently informed), thereby promoting more mindful and engaged news consumption. In this study, our warning messages forewarn the possibility of developing the NFM and its consequences. After warning message exposure, we presented participants with a news story and assessed their news elaboration and information gains (i.e., political learning) based on the content of the news story they read. This type of news literacy intervention has proven effective in encouraging thoughtful news consumption (Tully and Vraga, 2018; Vraga and Tully, 2016); however, its potential to prompt elaboration with the news and enhance political learning among those who hold the false beliefs (i.e., NFM) remains to be seen.

Second, at the same time, we consider the very real possibility that a forewarning message may not only be ineffective, but may also in fact yield unintended effects opposite of the message’s goal. Namely, a personalized message (i.e., one that identifies the individual as being high on the NFM) runs the risk of being seen as a threat to one’s freedom or as a personal insult (Kim et al., 2017). This could prompt defensive reactions among high-NFM individuals that undermine the success of the educational message, as would be expected based on psychological reactance theory (Brehm and Brehm, 1981). Similarly, the literature on feedback interventions demonstrates that interventions can be poorly received if the recipient appraises the message as a threat to their ego (Kluger and DeNisi, 1996), which could lead them to reject the warning message. In sum, we aim to explore interventions that can address NFM’s negative consequences, while also offering theoretical insights into the psychological mechanisms explaining the effectiveness (and potential unintended consequences) of such interventions.

The NFM and political learning

The NFM is defined as “the extent to which individuals believe they can indirectly stay informed about public affairs–despite not actively following the news–through general Internet use, information received from peers, and connections within online social networks” (Gil de Zúñiga et al., 2017: 107). Song et al. (2020) elaborated on the three key dimensions of NFM. First, high-NFM individuals often hold a (false) epistemic belief that they are more informed about current affairs and political issues than they truly are (Song et al., 2020). Second, they rely on peers for news, believing that their social connections will share important news with them (Gil de Zúñiga et al., 2017). Third, as a consequence of having epistemic and instrumental beliefs, high-NFM individuals are less motivated to actively seek out the news, especially in social media environments where incidental news exposure is common and political content is plentiful (Skurka et al., 2023; Song et al., 2020). As social media news use positively predicts the development of NFM (e.g., Gil de Zúñiga et al., 2017), scholars have recently identified a fourth dimension of NFM, algorithmic reliance, whereby individuals expect social media algorithms to present important current affairs as they unfold (Gil de Zúñiga and Cheng, 2021).

Despite high-NFM individuals’ beliefs that they are well-informed, previous studies have consistently provided evidence that NFM is negatively associated with political knowledge over time. Specifically, NFM predicts lower traditional news use (e.g., TV, newspapers) and higher social media exposure, yet this increased exposure does not translate into more general political knowledge (Gil de Zúñiga et al., 2017). One key mechanism believed to explain this negative effect on knowledge is that NFM reduces the likelihood of individual opportunities for political learning from news. There are at least two ways this can occur.

First, because NFM produces a false sense of being informed, high-NFM individuals feel little need to seek out news in the first place, making them unlikely to intentionally expose themselves to political news. This selective exposure away from news limits their political learning from individual news encounters (Shehata and Strömbäck, 2021), which undermines the development of robust political knowledge structures. Second, even when high-NFM individuals are exposed to individual news stories, they may be less motivated to critically engage with or cognitively elaborate on the news content because they believe they are already well informed. As such, they are less likely to absorb meaningful details from that news story than their low-NFM counterparts. Such diminished levels of political learning from these in-the-moment news encounters will, over time, result in lower accumulated levels of political knowledge.

The first mechanism has recently received empirical support. Skurka et al. (2023) found that high-NFM individuals are more inclined to selectively expose themselves to soft news (e.g., entertainment, sports) over hard news (e.g., politics, science). However, the second mechanism (i.e., lack of cognitive elaboration on the news) has so far gone untested. Previous studies, which have primarily been longitudinal in nature, have focused only on the cumulative effect of NFM on political knowledge over long stretches of time. This means researchers have yet to explicitly test the political learning mechanism—an oversight we seek to address in the current investigation. This mechanism is plausible because high-NFM individuals’ false sense of being sufficiently informed (i.e., perceived information sufficiency) may undermine their motivations to cognitively engage with political information compared to low-NFM individuals (Chaiken and Ledgerwood, 2012; Skurka et al., 2025). Consistent with this notion, previous research suggests that cognitive elaboration is negatively associated with the NFM (e.g., Strauß et al., 2021). That is, high-NFM individuals are less likely to think about, process, and connect new information with their existing knowledge while reading news, and thus, they may learn less. Specifically, when people are presented with legitimate news stories about a range of political issues and then surveyed immediately afterward, we would expect high- and low-NFM individuals to differ in how much they learn from that news story. We hypothesize:

H1. Low-NFM individuals will show higher levels of political learning from a news story than high-NFM individuals (in the absence of an educational warning message).

Educational warning messages as interventions to mitigate NFM

Although the negative outcomes associated with high-NFM have been well-documented, it is largely unknown how and what specific interventions aiming to reduce NFM could foster more mindful news consumption. In other words, how might scholars and practitioners mitigate the adverse effects of NFM among high-NFM individuals by promoting their awareness of the NFM? To explore this, we draw on research from news media literacy (NML).

NML involves a general understanding of how news is produced and encourages more critical news consumption (Tully and Vraga, 2018). Vraga et al. (2020) identified five domains of NML: context, creation, content, circulation, and consumption. The last domain, news consumption, is relevant here, referring to individual-level variables that matter for news exposure, attention, and evaluation (Vraga et al., 2020). It includes an understanding of how one’s own biases and news choices affect news consumption processes. From this perspective, NFM (a low-effort cognitive bias toward news consumption) can be understood as a direct challenge to NML, which NML-focused interventions could be designed to address. In this spirit, we investigate whether an NML-based intervention—delivered as an educational warning about NFM and its consequences—could mitigate the negative effects of NFM, particularly on political learning and one’s intent to rely on NFM, by enhancing high-NFM individuals’ attentiveness to and critical engagement with news.

Educational warnings can take various forms (e.g., Tully and Vraga, 2018; Tully et al., 2020). They can be more general—that is, not targeted at a specific NFM group—broadly informing individuals of the possibility of developing the NFM as well as its detrimental effect on political learning. Alternatively, educational warnings can be more personalized, targeted specifically to high- or low-NFM individuals, with tailored information about how their NFM level could affect their political knowledge over time. This type of personalized warning is akin to feedback interventions documented in psychological research, wherein information about one’s task performance is communicated to improve outcomes (Kluger and DeNisi, 1996). The rationale behind feedback interventions is that formative assessment helps individuals reflect on their current performance and serve as guidelines for behavioral change, bridging the gap between their actual performance and a desired standard (Hattie and Timperley, 2007). We explore both general and personalized warnings in this investigation.

Broadly speaking, warning messages have been effective in the context of NML (Van der Meer and Hameleers, 2021), although they have not yet been applied specifically to the NFM context. Prior studies have found that general educational messages, such as NML interventions or forewarnings about the potential exposure to misinformation, encourage news consumers to become more vigilant when browsing online content (e.g., Cook et al., 2017; Lu et al., 2024). For example, public service announcements reminding individuals that it is the citizens’ role to be critical when evaluating the news content significantly improved their perceived media literacy, which then predicted political efficacy (i.e., confidence in one’s ability to participate in the political process and make sense of political information) (Tully and Vraga, 2018). These messages also reduced partisan selective exposure to news during the 2016 US presidential election (Vraga and Tully, 2019) and led to higher engagement intention in participatory democracy (Vraga and Tully, 2016). In addition, providing media literacy tips on how to discern false content improved discernment of false and truthful news, although this was only effective educated people in India (Guess et al., 2020). There is also some evidence that personalized messages can make people feel that the message is directly relevant to themselves, leading to greater compliance with personalized message than generalized messages (Jensen et al., 2012; Van der Meer and Hameleers, 2021; Tully et al., 2020). For example, van der Meer and Hameleers (2021) showed that tailored NML interventions that aligned with participants’ immigration attitudes reduced boomerang effects seen in their untailored NML interventions.

In summary, then, educational warnings (either personalized or general) could serve as a reminder to read the news more critically and encourage them to reduce their reliance on the NFM. We hypothesize:

H2. Exposure to educational warning messages (either general or personalized) will result in higher levels of (a) political learning and (b) intention to reduce NFM compared to no exposure to an educational warning message.

As for the mechanism of these positive effects, we expect cognitive elaboration about the news to play a central role. According to the cognitive mediation model (Eveland Jr, 2001), news exposure, attention, and motivations to consume news media are critical predictors for learning from news. Nonetheless, they alone are insufficient to predict the extent to which individuals learn from the news. The model maintains that effective learning from news requires cognitive activities, such as cognitive elaboration, whereby individuals critically think about arguments and ideas presented in a message (Eveland Jr, 2001; Petty and Cacioppo, 1986).

If high-NFM individuals indeed learn less compared to low-NFM individuals during their processing of news stories, fostering cognitive elaboration might be an important goal, which can be achieved with educational warning messages. For example, McGuire’s (1964) inoculation theory posits that preemptively exposing news consumers to a weakened dose of persuasive argument, such as a forewarning of a possible attack or a pre-bunking of a specific piece of misinformation, helps build cognitive immunity against the stronger attacks and encourages more refutation (e.g., misinformation) (Banas and Rains, 2010). Buczel et al. (2024) showed that forewarning reduced reliance on misinformation by making individuals more vigilant, and more likely to actively filter out the misinformation during the encoding state of their cognitive processing. Similarly, Iles et al. (2021) revealed that forewarning enhanced cognitive resistance, such as counterarguments against false information, enabling individuals to critically assess misinformation. We predict:

H3. The effects of exposure to educational warning messages (either general or personalized), per H2, will indirectly increase (a) political learning and (b) intention to reduce NFM by promoting cognitive elaboration.

Although both general and personalized warning messages should be effective, the effect of a general warning may differ based on individuals’ NFM. Research on confirmation bias suggests people tend to seek and process information that aligns with their pre-existing beliefs to achieve cognitive resonance (Nickerson, 1998). As such, individuals might be more receptive to information that is consistent with their existing perceptions or attitudes. Consequently, the effects of a general message may be more pronounced among low-NFM individuals, as the message aligns with their belief in the need to actively seek out news rather than rely on peers or algorithms for getting news. Conversely, high-NFM individuals may be less receptive to this warning message, as it contradicts their established beliefs and orientation to news. We propose a contributory divergent positive moderation pattern, per Holbert and Park (2020):

H4. Compared to no educational warning message, a general educational warning message will increase (a) political learning and (b) the intention to reduce NFM for high and low-NFM individuals (per H2), but this effect will be stronger for low-NFM individuals than for high-NFM individuals.

Considering the (in)effectiveness of a personalized NFM warning

To this point, we have assumed educational warning will lead to positive outcomes. Yet there is reason to suspect such warnings—especially personalized warnings—will yield unintended effects. From the literature on feedback interventions, Kluger and DeNisi (1996) noted that individuals receiving feedback may respond in one of two ways to bridge the gap between current and desired performance: (1) they may improve their performance to meet the standard and (2) they may reject the standard and dismiss the feedback. In the context of NFM, a personalized warning could encourage low-NFM individuals to further decrease their reliance on passive news consumption, as the feedback aligns with their beliefs. On the other hand, high-NFM individuals may respond in either direction: they might reject the personalized message that contradicts their views, or they might be motivated to reduce the gap to meet the desired standard. Considering such opposing possibilities, we ask:

RQ1. Will the effect of a personalized warning on (a) political learning and (b) intention to reduce NFM depend on the NFM level?

One reason feedback interventions can fail (or backfire) is that threaten self-esteem, shifting cognitive focus away from the task at hand to meta-task concerns, such as managing one’s identity and ego (Kluger and DeNisi, 1996). Similarly, feedback that comes across as overly controlling can undermine intrinsic motivation to engage in subsequent tasks (Ryan, 1982). It is therefore possible that personalized warnings about the negative consequences of NFM—particularly those directed to high-NFM individuals—could be interpreted as overly controlling and threatening to one’s self-esteem. After all, individuals generally do not appreciate being told they are doing something problematic or foolish, so feedback interventions aiming to reduce risky behaviors often face resistance from those individuals most likely to benefit from the intervention (Schüz et al., 2013). For example, Kroon et al. (2022) found that providing feedback on racial and religious biases reduced the influence of bias on selective news exposure among individuals with low explicit but not high implicit bias. For those with high explicit bias, the feedback had a backfire effect. Recently, Bär et al. (2024) also showed that a personalized AI-generated warning of the negative outcomes of hate speech produced backfire effects, such as greater willingness to share it, compared to the predefined generic warning, because participants perceived it as more intrusive, even triggering hostility.

According to psychological reactance theory, individuals value their autonomy to think and act as they wish (Brehm and Brehm, 1981). When individuals perceive that such a freedom is threatened or lost, they experience an aversive, motivational state called reactance—an amalgam of negative cognitions and feelings of anger (Dillard and Shen, 2005). When the reactance state is triggered, individuals then become defensive and are motivated to reject the message and even increases motivation to perform the proscribed behavior. This reactance process can even be instigated by an insult that attacks the message recipient’s character (Kim et al., 2017).

Applying this logic to the current study, personalized warnings may run the risk of inviting such defensive responses. That is, when a high-NFM individual receives a personalized warning that not only explains what the NFM is, but also identifies them as being high in NFM, the person may interpret the message as a threat to their freedom or as a personal affront to their abilities or intellect. Even if the warning avoids explicitly insulting language, the personalized nature of the feedback in and of itself may put high-NFM individuals on the defensive by associating them with NFM—an undesirable characteristic. This appraisal of a freedom threat or insult would then be accompanied by reactance (i.e., negative thoughts about and anger toward the message) and decreased willingness to comply with the warning’s recommendations (i.e., to learn from the news and to reduce one’s NFM reliance in the future). Given the novelty of these propositions, we explore this matter with a research question:

RQ2. To what extent will a personalized warning trigger defensive reactions relative to a general warning among high-NFM individuals?

Nonetheless, reactance theory leads us to expect that the more any individual experiences defensive reactions toward the warning, the less likely they will be to comply. Therefore:

H5. Greater defensive reactions will be negatively associated with (a) political learning and (b) intention to reduce NFM.

Method

Participants

Participants were recruited via the CloudResearch platform which includes high-quality participants from the Amazon Mechanical Turk pool from April to May 2024. The final sample (N = 405) consisted of 186 men (45.9%), 218 women (53.8%), and 1 who preferred not to say (0.2%). The average age was 46.54 years (SD = 14.19, range = 20–86). Most of the participants had a college degree (60.7%) or higher (29.7%). The annual household income ranged from “$0 to $14,999” (1) to “$200,000 or more” (7), with a Mdn = 4.00; $50,000 to $99,999, SD = 1.40. The average political orientation was 3.71 (SD = 1.87), ranging from (1) extremely liberal to (7) extremely conservative, and party affiliation was as follows: Republicans (27.4%), Independents (26.2%), and Democrats (46.2%). Participants received a total of $2.63 for their participation, based on an hourly rate of $7.25. Compensation was distributed as follows: $0.10 for screening questions (median completion time: 2.1 min), $0.53 for Wave 1 (3.4 min), and $2.00 for Wave 2 (13.65 min). The sample included n = 233 low-NFM individuals (57.5%) and n = 172 high-NFM individuals (42.5%).

Study design and procedure

We conducted an experiment using a 3 (educational warning: general vs. personalized vs control) ✕ 2 (NFM level: low vs. high) between-subjects design. Before data collection, this study received institutional review board (IRB) approval and was pre-registered with all materials including a full questionnaire¹ on OSF (https://osf.io/b4jgc)

This study consisted of three phases: screening phase, Wave 1, and Wave 2. In the screening phase, we recruited 1914 participants and assessed their NFM levels (see Measures section). Based on the median value of a 10-point scale (1 = strongly disagree, 10 = strongly agree; Mdn = 4.38, M = 4.42, SD = 1.97, Cronbach’s α= .97), participants were categorized into high- and low-NFM groups. This categorization² allowed us to deliver personalized messages tailored to each group in Wave 2 of the study. In addition, we measured social media news use to disguise the study purpose. After excluding participants who did not complete the survey (n = 4) and duplicate cases (n = 3), we classified participants scoring one standard deviation below the median (2.40) as low-NFM individuals (n = 359), and we classified those scoring one standard deviation above the median (6.35) as high-NFM individuals (n = 365), resulting in a total of 724 eligible participants.

A week later, we invited those 724 participants to participate in Wave 1, which 524 completed (72% returning rate). In Wave 1, participants only responded to a series of questions that measured covariates and demographics. Eight weeks later,³ we invited the 524 participants who completed Wave 1 to participate in Wave 2, 407 of which completed it (56% returning rate from Wave 1). After excluding cases with straight lining for all outcome measures (n = 1) and a duplicate case (n = 1), we were left with our final sample of N = 405 participants.

In Wave 2 (the main portion of the experiment), participants within each NFM group were randomly assigned into one of three conditions: control, general educational warning, or personalized educational warning. After reading the assigned warning (or no warning in the control condition), participants were randomly assigned to read one of four news stories. Participants were required to spend at least 100 seconds on the news page before continuing. Afterward, participants answered questions for manipulation and attention checks and intention to reduce NFM. They then answered a randomly ordered set of outcome measures assessing political learning (recall and recognition of details from the story), cognitive elaboration about the news story, and defensive reactions.

Experimental materials

Educational warning messages

We created two types of educational messages for this study: general and personalized. Both types included a brief explanation of the “News-finds-me” (NFM) perception (baseline message in Figure 1). In the general educational message condition, participants received an explanation of NFM perception and the detrimental effects of the NFM (i.e., being less politically informed over time). For the personalized message, participants received the same NFM explanation, along with their own NFM level (high or low), as determined during the screening phase. Those with low-NFM were informed that people with low NFM are more likely to actively consume news and remain informed, whereas those with high-NFM were warned that people with high NFM may become less politically informed over time. Full messages and details for language use are presented in Figure 1 and Supplementary File—A, respectively. In the control condition, participants were directed to read the news story without any message.

Figure 1.

Educational warning messages.

News stories

The four news stories mimicked the layout of a Wall Street Journal page in order to control the format and length of news. The articles were originally published in The New York Times and The Wall Street Journal⁴ (see Figure 2 for the sample screen-captured mock news page; Supplementary File—B and C for the news selection). Subsequent analyses (not presented here) indicated that there were no interaction effects between news story and warning message condition (or with NFM level). Thus, we did not consider news stories further in our analyses.

Figure 2.

Sample of screen-captured mock news page.

Measures

The following measures were employed to evaluate the variables of interest. Unless otherwise noted, 7-point Likert-type scales (1 = strongly disagree, 7 = strongly agree) were used for all measures.

NFM level

The NFM measure (Gil de Zúñiga and Cheng, 2021; Gil de Zúñiga et al., 2017) captured four dimensions (perception of being well-informed, not actively seeking news, reliance on peers, and reliance on algorithms), totaling eight items measured on a 10-point scale (1 = strongly disagree, 10 = strongly agree). Sample items included: “I rely on my friends to tell me what’s important when news happens” and “I rely on social media algorithms to provide me with news through social media.” (M = 4.09, SD = 2.85, α = .97). As noted previously, NFM level was dichotomized (low = 0, high = 1).

Political learning

The learning questions shown to each participant were based on the news story that participant read (see Supplementary File—D for details on the development of the question set). There were five recognition questions (two multiple choice and three true/false statements) and five cued-recall questions, balanced between verbatim and gist details. For each question, an “I don’t know” option was provided to minimize guessing. For the cued-recall items, one of the verbatim answers for each news story allowed slight variations when the answer was a number; a range of a few units off was considered correct. Correct answers to each item were coded as 1, while incorrect and “I don’t know” responses were coded as 0. We then summed all correct answers for recognition (M = 3.04, SD = 1.29) and cued-recall (M = 2.89, SD = 1.46), and added the two mean values to create an index of political learning (M = 5.93, SD = 2.22, range [0, 10]).

Intention to reduce NFM

We used four items adapted from the NFM measure to assess participants’ plans to reduce their reliance on NFM in the future, such as “From now on, I will make an effort to stay informed about public affairs to see what’s important when news happens” and “From now on, I will actively seek news to be informed about important public affairs” (M = 5.63, SD = 1.17, α = .88).

Cognitive elaboration

Four items were adapted from Perse (1990) and Kahlor et al. (2003) to measure cognitive elaboration about the news story participants read. Sample items included: “While reading the news article, . . . I thought about what should be done” and “. . . I found myself making connections between the story and what I’ve read or heard about elsewhere” (M = 4.33, SD = 1.47, α = .85).

Defensive reactions

Four constructs related to defensive reactions were assessed: freedom threat, perceived insult, anger, and negative cognition. Freedom threat was measured with four items taken from prior work (Dillard and Shen, 2005; e.g., “The message that I’ve received tried to manipulate me” and “The message threatened my freedom to choose”) (M = 2.35, SD = 1.49, α= .93). Perceived insult was measured with four items (e.g., “The message tried to make me feel stupid” and “The message tried to insult me”) (M = 2.10, SD = 1.41, α = .95). For anger and negative cognition, a 7-point scale (1 = not at all, 7 = extremely) was used. Anger was measured with three items following Dillard and Shen (2005): “angry,” “irritated,” and “annoyed” (M = 1.63, SD = 1.32, α= .96). Negative cognition was measured with three items: “unfavorable,” “negative,” and “bad” (M = 1.98, SD = 1.59, α= .98). Based on the results from a confirmatory factor analysis for these four measures (see Supplementary File—E), we opted to average the four constructs into one measure of defensive reactions (M = 2.02, SD = 1.27, α= .96).

Covariates

We controlled for social media news use and general political interest, as these variables were highly correlated with the variables of interest (which result is presented in Supplementary File—F). The zero-order correlations among key variables, the randomization check, and the manipulation check are reported in Supplementary File—G, H, and I, respectively.

Results

To answer our hypotheses and research questions, a series of Hayes’s (2017) PROCESS macro-analyses⁵ were conducted with 10,000 bootstrapping to generate 95% confidence intervals. High-NFM was coded as 1, while low-NFM was coded as 0.

Main effect of NFM in the absence of educational warnings (H1)

H1 predicted a main effect of NFM, such that low-NFM individuals would show higher levels of learning than high-NFM individuals in the absence of educational messages (i.e., the control condition). A simple regression (N = 127) excluding data from the warning conditions showed that controlling for social media news use and general political interests, NFM level was negatively associated with political learning, such that low-NFM participants reported greater political learning compared to high-NFM participants, b = −1.61, SE = 0.58, p = .006. Thus, H1 was supported.

Although not hypothesized, we found a similar pattern for the effect of NFM on intention to reduce NFM, such that low-NFM participants reported greater intention to reduce NFM than high-NFM participants, b = −0.91, SE = 0.13, p < .001.

Effects of any educational warning on political learning and intention (H2)

H2 predicted that exposure to either educational message (general or personalized) would result in higher levels of (a) political learning and (b) intention to reduce NFM, compared to no exposure. We dummy-coded the educational warning conditions as 1 and the control condition as 0. A simple regression with the full sample (N = 405) showed that controlling for covariates and the NFM level, exposure to warnings (either general or personalized) did not significantly increase political learning, b = −0.28, SE ‎ = 0.23, p = .223, or intention to reduce NFM, b = 0.14, SE = 0.09, p = .107, compared to no exposure to a warning. Thus, H2 was not supported.

Indirect effects of any educational warning via cognitive elaboration (H3)

H3 predicted that the effects of exposure to either educational message would indirectly increase (a) political learning and (b) intention to reduce NFM by increased cognitive elaboration. Using the same coding as in H2, we used PROCESS macro (model 4). As seen in Figure 3 that presents indirect effects via cognitive elaboration, neither type of educational warning (vs. control) led to higher levels of cognitive elaboration, b = −0.11, SE = 0.15, p = .472. However, cognitive elaboration was positively associated with both political learning, b = 0.20, SE = 0.08, p = .009, and intention to reduce NFM, b = 0.21, SE = 0.03, p < .001. A post hoc analysis revealed that the association between elaboration and political learning was only significant for low-NFM participants (b = 0.31, p = .002, 95% CI [0.12, 0.50]), not for high-NFM participants (b = 0.06, p = .620, 95% CI [–0.17, 0.28]). Because there was no effect of the warnings relative to control on elaboration, the indirect effects on political learning, b = −0.02, BootSE = 0.03, 95% CI (–0.09, 0.03), and intention to reduce NFM, b = −0.02, BootSE = 0.03, 95% CI (–0.07, 0.03), were not statistically significant. H3 was not supported.

Figure 3.

Indirect effects via cognitive elaboration.

NFM as moderator of warning effects on political learning and intention (H4, RQ1)

H4 predicted that a general educational warning (vs. control) would increase (a) political learning and (b) intention to reduce NFM but would be stronger for low-NFM individuals than for high-NFM individuals. RQ1 inquired whether the effects of a personalized educational warning (vs. control) on (a) political learning and (b) intention to reduce NFM would depend on the NFM level. We performed PROCESS macro (model 1) using indicator coding (comparing each treatment condition to the control) to answer both H4 and RQ1. The results showed that the effect of general warning (vs. no warning) did not differ depending on the NFM on both political learning, b = 0.35, SE = 0.54, p = .513, and intention to reduce NFM, b = 0.13, SE = 0.21, p = .540. Thus, H4 was not supported. Similarly, the effects of personalized warning (vs. no warning) did not differ depending on the NFM level for political learning, b = −0.05, SE = 0.53, p = .926, or intention to reduce NFM, b = −0.07, SE = 0.20, p = .723. These patterns address RQ1 (i.e., no conditional effects of the personalized warnings on the dependent variables as a function of NFM level).

NFM as moderator of the indirect effects of warnings via defensive reactions (H5, RQ2)

RQ2 asked to what extent a personalized message (vs. a general message) might trigger defensive reactions among high-NFM individuals. H5 predicted that greater defensive reactions, in turn, would be negatively associated with (a) political learning and (b) intention to reduce NFM. Excluding data from the control condition, we ran PROCESS macro (model 7) to test both RQ2 and H5, which together imply a moderated mediation effect. First, we found a significant interaction effect between warning type and NFM level on defensive reactions, b = 0.78, SE = 0.29, p = .007. We found that a personalized warning, compared to general warning, led to greater defensive reactions among high-NFM participants (b = 0.57, SE = 0.22, p = .010, 95% CI [0.14, 0.99]) but not low-NFM participants (b = −0.22, SE = 0.19, p = .247, 95% CI [–0.58, 0.15]). Figure 4 presents the interaction plot between warning and NFM on defensive reactions.

Figure 4.

Interaction effect between educational warning and NFM on defensive reactions.

Defensive reactions in turn were negatively associated with political learning, b = −0.62, SE = 0.10, p < .001. These results, taken together, provide evidence for moderated mediation, as indicated by the moderated mediation index = −0.49, BootSE = 0.23, 95% CI (–0.98, –0.10). That is, a personalized message (vs. a general message) indirectly led to lower political learning via greater defensive reactions among high-NFM participants, indirect effect = −0.35, BootSE = 0.19, 95% CI (–0.76, –0.03). However, for low-NFM participants, this indirect effect was not significant, indirect effect = 0.13, BootSE = 0.10, 95% CI (–0.04, 0.34).

Defensive reactions were also negatively associated with intention to reduce NFM, b = −0.10, SE = 0.04, p = .025, providing evidence for moderated mediation, as demonstrated by the moderated mediation index = −0.08, BootSE = 0.05, 95% CI (–0.19, –0.001). Although we found a similar trend, with a positive coefficient for low-NFM participants (i.e., greater intention to reduce NFM; indirect effect = 0.02, BootSE = 0.02, 95% CI [–0.01, 0.06]) and a negative coefficient for high-NFM participants (lower intention to reduce NFM; indirect effect = −0.05, BootSE = 0.04, 95% CI [–0.14, 0.004]), this effect was weaker compared to the effects on political learning. Figure 5 presents the moderated mediation effects via defensive reactions. These findings⁶ support H5. Table 1 presents a summary of the moderated mediation effects.

Figure 5.

Moderated mediation effects via defensive reactions.

Table 1.

Summary of moderated mediation effects.

Educational warning (Personalized vs General) → Defensive Reactions → DVs
	Coefficients	BootSE	95% LLCI	95% ULCI
Political learning
Low-NFM	0.13	0.10	−0.04	0.34
High-NFM	−0.35	0.19	−0.76	−0.03
Index of Moderated Mediation	−0.49	0.23	−0.98	−0.10
Intention to reduce NFM
Low-NFM	0.02	0.02	−0.01	0.06
High-NFM	−0.05	0.04	−0.14	0.004
Index of Moderated Mediation	−0.08	0.05	−0.19	−0.001

Note. DVs = dependent variables; Unstandardized coefficients are reported; 10,000 bootstrapping were performed to generate the 95% confidence interval.

Discussion

Our findings corroborate those from prior longitudinal survey research, showing that high-NFM participants learn significantly less from political news compared to low-NFM participants. This pattern remained consistent regardless of the presence of educational warning messages and across various political news topics, including the economy, national security, history, and policy. This finding adds another layer of empirical evidence for a vicious reinforcing loop (Skurka et al., 2023), whereby high-NFM individuals tend to become politically cynical (Song et al., 2020), favor soft news over hard news (Skurka et al., 2023), and acquire less general political knowledge over time (Gil de Zúñiga et al., 2017). This in turn may solidify or even boost one’s NFM.

Importantly, our study design required participants to read full-length articles. Although this approach was intended to create an optimal condition for understanding news exposure and learning, it limits the study’s ecological validity; high-NFM individuals tend to rely on news snippets from social media feeds and are less likely to visit the news websites directly due to their beliefs in NFM. Despite this controlled exposure—which likely elicits greater attention than voluntary news consumption—it appears that high-NFM individuals were still less likely to process the information critically than their low-NFM counterparts, learning less from the news they consume. This is particularly concerning because previous research has shown that partial news exposure (what high-NFM individuals often experience) may lead them to overestimate their learning. For example, Anspach et al. (2019) found that individuals who view Facebook news snippets learned more than those with no exposure but far less than those who read full articles, and importantly they overestimated their understanding, exhibiting overconfidence in their knowledge. These findings suggest that if high-NFM individuals learn less even when exposed to a full-length news in an experimental setting, their learning is likely more limited in real-world, high-choice media environments where individuals can easily opt out of news exposure entirely. Even more concerning is that high-NFM individuals showed little interest in changing this mind-set compared to low-NFM counterparts, even after learning about the potential negative consequences of NFM. These findings imply that simply increasing exposure to political news alone is unlikely to fully mitigate the effects of NFM, requiring interventions that address the underlying cognitive and motivational barriers to enhance learning beyond a personalized warning.

A personalized warning message also put high-NFM participants on the defensive. Compared to a general warning that broadly described problems associated with NFM, a personalized warning message increased resistance among high-NFM participants because they perceived the message as personally insulting and as a threat to freedom. Notably, high-NFM individuals exhibited this defensive response to the personalized warning even though we made a concerted effort to avoid explicitly insulting or demeaning language in the warning. Moreover, high-NFM participants also reacted more defensively than low-NFM participants, even to a general warning message that did not specifically single them out. These findings suggest that even gently phrased interventions may inadvertently trigger defensive responses, undermining the intended effectiveness of the intervention.

Addtionally, although we found no significant indirect effects of educational warnings through cognitive elaboration on political learning or reliance on NFM, we observed a positive association between cognitive elaboration and political learning among low-NFM participants but not high-NFM participants. One possibility for this finding could be that though high-NFM participants did elaborate on the news content, their elaboration process may have been biased, which might not have been helpful in enhancing their knowledge learning. Motivated reasoning posits that when individuals encounter information that challenges their beliefs, or is otherwise undesirable, they are motivated to discredit the evidence, even if it is scientific, that contradicts their beliefs (Kunda, 1990). This may reflect an entrenchment of the NFM, such that a strong NFM is not simply persistent but also makes people deeply resistant to changing their beliefs, engaging in elaboration not to genuinely learn or change their views, but rather to reinforce pre-existing beliefs. These findings highlight the need for robust strategies to counteract motivated reasoning—strategies that actively disrupt biased reasoning.

One promising strategy could be to identify other interventions that stimulate effortful processing, either by enhancing motivation to engage with news content, or improving the ability to do so. However, caution is advised, as a post hoc result indicates that high-NFM individuals may not benefit from such strategies, possibly due to defensive reactions to educational warnings. Nonetheless, since high-NFM individuals often lack the motivation and cognitive skills necessary for political learning, it may be effective to develop persuasion techniques that avoid provoking defensive responses. Most importantly, offering concrete and actionable guidelines for how they can deliberately engage with news may be essential (see Lutzke et al., 2019 for guidelines for critical thinking; Sunstein, 2014), such as checking the source, considering key questions while reading, and reflecting on how the information relates to their prior knowledge. Moreover, given that high-NFM individuals tend to rely on their peers for news, they may be more likely to be persuaded by interventions leveraging social norms—particularly the behaviors commonly accepted and/or practiced within their peer groups, to promote desirable behaviors (Gimpel et al., 2021; Van der Meer and Hameleers, 2021; Wojcieszak et al., 2020). For example, a social norms message like “Most people in your group read this article, carefully reviewing it to understand both sides of an issue,” or a visual cue such as a time bar indicating typical reading duration among their peer group members, could encourage careful engagement with news by signaling ingroup behavior.

Limitations and future study directions

This study has several limitations. First, we acknowledge that the effects of NFM levels on political learning might be short-lived. Future studies should investigate whether the NFM gap in learning from the news story develops into lasting knowledge over time and whether the intervention can encourage low-NFM individuals to acquire broader political knowledge beyond the specific news to which they have been exposed. Second, although forewarning messages are known to have pre-emptive effects (e.g., increasing vigilance toward forthcoming information), their effectiveness appears limited under certain conditions, such as among individuals with strong conspiracy beliefs (Bertolotti and Catellani, 2023) or when compared to corrective strategies delivered after misinformation exposure (Walter and Murphy, 2018). Future studies should examine the boundary conditions of forewarning effects to inform more targeted interventions, particularly for high-NFM individuals. Third, our experimental design, which required participants to read a full-length political news story, lacks ecological validity. In real-world settings, high-NFM individuals are less likely to expose themselves to hard news or visit news websites. Instead, they tend to rely on news snippets encountered from social media feeds. Therefore, our study does not capture the effects of educational warnings on individuals’ actual exposure to political news. Although our reliance on the NFM measure partially captures individuals’ intentions to depend on passive news consumption, behavioral intention does not always translate directly into action. This link is particularly weak when individuals lack intrinsic motivation to change (Sheeran and Webb, 2016). Moreover, because we did not observe an effect on intentions, future research should test stronger interventions, such as those that target motivational barriers and use a longitudinal design to examine whether these strategies influence individuals’ actual news-seeking behaviors.

Conclusion

This study replicates the detrimental effects of the News-Find-Me Perception on political learning in a controlled experimental context. In addition, to our knowledge, this investigation is the first attempt to mitigate NFM’s negative impact by introducing educational warnings as interventions. Although these warnings did not improve political learning or reliance on NFM, our findings show that NFM’s effects on learning persist, likely due to biased elaboration among high-NFM individuals. The defensive reactions triggered by personalized messages underscore the need for more nuanced strategies when addressing high-NFM individuals in an effort to neutralize and counteract the problems associated with believing the NFM. These results provide a valuable first step in designing effective interventions aimed at lowering NFM. We invite future studies to focus on refining these strategies to promote more informed news consumption for high-NFM individuals.

Supplemental Material

sj-docx-1-nms-10.1177_14614448261438455 – Supplemental material for Mitigating the News-Finds-Me Perception: Evaluating the intended (and unintended) effects of educational warnings on political learning

Supplemental material, sj-docx-1-nms-10.1177_14614448261438455 for Mitigating the News-Finds-Me Perception: Evaluating the intended (and unintended) effects of educational warnings on political learning by Eunchae Jang, Mengqi Liao, Chris Skurka and Homero Gil de Zúñiga in New Media & Society

Footnotes

Authors’ Note

Homero Gil de Zúñiga is now affiliated to Universidad Diego Portales, Chile.

Declaration of conflicting interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethical approval and informed consent statements

The institutional review board (IRB) at the Pennsylvania State University approved this exempt study (approval #: STUDY00022772) on 28 September 2023. Participants gave informed consent before participating in the study.

Consent to participate

Informed consent was written.

Consent for publication

Not applicable.

ORCID iDs

Eunchae Jang

Mengqi Liao

Chris Skurka

Homero Gil de Zúñiga

Data availability statement

The data underlying this study are available in Open Science Framework (OSF) at .

Supplemental material

Supplemental material for this article is available online.

Notes

Author biographies

Eunchae Jang (M.S., Yonsei University) is a PhD candidate in the Donald P. Bellisario College of Communications at Penn State University. She studies how AI affordances shape information processing and trust. Specifically, she examines the boundary conditions of those affordances, including how they can result in unwarranted trust in AI and how they can be leveraged to promote more informed behaviors, such as the systematic evaluation of information and warranted trust in AI.

Mengqi Liao (Ph.D. in Mass Communication, Penn State University) is an Assistant Professor in the Department of Advertising and Public Relations at Grady College of Journalism & Mass Communication, University of Georgia. Her research explores the process and psychological effects of human interactions with emerging artificial intelligence (AI) applications. Her research aims to gain a deep understanding of the effects of different technological affordances on persuasion, users’ cognitive information processing, psychological well-being, and trust.

Chris Skurka (Ph.D., Cornell University) is an Associate Professor in the Donald P. Bellisario College of Communications at Penn State University. He studies the effects of media messages that aim to promote engagement with health, environmental, and science issues.

Homero Gil de Zúñiga (Ph.D. in Politics at Universidad Europea de Madrid and Ph.D. in Mass Communication at University of Wisconsin – Madison) is Distinguished Research Professor – Chair in Political Behavior at University of Salamanca, Distinguished Professor of Media Effects & AI at Pennsylvania State University, and Senior Research Fellow at Universidad Diego Portales. In general, his work draws from theoretically driven research, aiming to shed an empirical social scientific light over how social media, algorithms, AI, and other technologies affect society and democracy.

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