Health misinformation negatively impacts individuals’ and society’s adaptive response to a health crisis [1,2]. As a communication tool, social media has worsened the problem by speeding up the spread of misinformation and strengthening people’s existing beliefs [3-5]. Conversely, social media platforms can be effectively used for health communication to disseminate accurate information by correcting misinformation and engaging users by providing verified health advice [6-9]. Many organizations devote efforts to correcting misinformation and releasing them on social media. However, the efficacy of correction is sometimes limited for various reasons, such as people feeling uncomfortable with the knowledge gap when retracting misinformation [10]. Moreover, in an increasingly complex health information environment, people are flooded with knowledge they may not be able to use correctly [11]. In this context, the conditions and message designs that make misinformation correction an effective countermeasure have been examined in experimental studies [6,12,13]. As an interactive digital environment, social media also allows researchers to access data on user responses to health communication, obtaining results from different perspectives [7-9,14].

In this study, we investigate how to enhance the effectiveness of misinformation correction based on real-world data. We selected 2 representative Facebook fan pages in Taiwan (Facebook, Meta Platforms, Inc) that provide rich health misinformation corrections in post forms. Users engage with the content provided by these 2 organizations and other users’ comments. In general, correction is a remedy that offers accurate information to counteract misinformation presented in media [10]. According to the observed data, the definition of health misinformation correction in this study is the content with the intent to resist health-related misinformation. This includes clarifications of health misinformation, fact-checking that covers the verification process, and content that addresses potential misinformation while not directly correcting a specific claim (examples are provided in Table S1 in Multimedia Appendix 1). The topics covered include diseases; health care; food safety; and issues extending from the COVID-19 pandemic, such as public welfare and information security. Then, we need a theoretical framework to identify the posts’ attributes (called features or characters in some studies [7,9]). The fuzzy-trace theory (FTT) provides a framework that differentiates between 2 cognitive representations of a message, “gist” (top-down meaning) and “verbatim” (detailed information) [15], helping us focus on analyzing textual meaning in posts rather than merely considering surface attributes such as length and tone. The theory was also used to explore physician-patient, health, and scientific communication [7,16,17].

The responses social media users have when they see posts, particularly those explicitly displayed on social media, are referred to as user engagement in this study. These are important metrics for examining the impacts of post attributes in many studies [8,9,14]. This study explored user engagement in 2 dimensions. The first is based on the metrics Facebook provides (sharing, reacting, and commenting), which are the main focus of the predictions in this research. The second is to examine the content users create when they comment. We assume that users engage with different intentions in their comments, and this is reflected in the comment content, which is presented as additional information accessible to other users. Then, this study proposes a framework to classify the engagement categories in comments and examine if this content affects the other 2 metrics: sharing and reacting.

We then introduced prior work about health communication on social media, explained the research gap, and explored the perspective of FTT to identify post attributes and the relationship between post attributes and user engagement in this study.

In previous studies, the acceptance of misinformation correction across different contexts and content types has been examined through experiments. The variables often examined include simple versus detailed or factual elaboration [12,13,18]; correction sources [6,12,13,19]; and information content such as quality, intensity, or emphasis [18,20,21].

Another approach in the context of social media involves collecting data from these platforms, labeling them manually or automatically, and analyzing them. A study using FTT as a framework explored news reports about measles and vaccines and expressed the opinions of vaccination proponents or opponents as the gist. In this study, data were coded to predict the likelihood of news sharing on Facebook. The regression model included other variables, such as statistics and stories, but only the gist could significantly predict news sharing [14]. Health communication efforts increased significantly during the COVID-19 pandemic, allowing for analysis through publicly available data. A study of 1816 posts from hospitals in Taiwan during the first 4 months of the COVID-19 pandemic found that specific social media strategies significantly increased user engagement, indicating the potential to improve health literacy among health care organizations [7]. Such studies have also observed that users inevitably share misinformation through comments when discussing health issues on social media. A study analyzed 1534 comments from 2 Facebook groups discussing mental health symptoms between January 2019 and December 2021. It found that 26.1% of the comments contained medically inaccurate information, specifically regarding complementary and alternative medicine (60.3%) [22]. By observing long-term trends, it is also possible to track changes in the quality of health communication and user attitudes. An analysis of 12,553 COVID-19 vaccine fact-checking posts and 122,362 comments from January 2020 to March 2022 showed increased analytical thinking and confidence in the posts, alongside decreased tentativeness among comments, showing an evolution in public sentiment over time [8]. Finally, a study analyzed 914 fact-checking news articles to understand how source transparency, contextual information, and vividness influence user engagement. The study found that these factors significantly affected user responses, especially likes and angry reactions, emphasizing the importance of content style and depth in capturing public attention [9]. These studies underline the critical role of social media in shaping public health narratives and emphasize the necessity of designing engaging yet accurate content during health crises.

Regarding the research gap, there is a lack of health communication studies that incorporate both data and theory, and finding a theory that fits the data is not easy. To explore the effect of misinformation correction, the primary theories considered include the inoculation theory [23], elaboration likelihood model [24], heuristic-systematic model [25], and FTT [15], all of which offer different interpretations from a cognitive perspective on how misinformation correction can be effectively achieved. The FTT emphasizes the importance of conveying the essence of a message, focusing on its core meaning rather than merely summarizing or simplifying it. It highlights the need to avoid overwhelming people with excessive quantitative details. [26]. Moreover, FTT has robust cognitive neuroscience foundations, making it suitable for scientific communication and compliant with nonpersuasive fact-checking methods that prioritize reference-based corrections. Therefore, this study adopted FTT for analysis. The method of conveying essential meaning is called gist communication [17].

In addition, we proposed a framework for classifying user-generated comment content, as previous studies have rarely examined this aspect. We have included them as independent variables in the prediction model to explore whether the FTT’s gist communication remains effective even when different comments are present as additional post information.

Social media offers benefits for health communication, such as improving the accessibility of health information and allowing users to generate and exchange information, but the quality and reliability of the shared information must be monitored [42]. The FTT suggests that information with gist cues supports reasoning and decision-making, and they are more likely to share when the cues connect their motivations or values. Facebook shares are commonly used as a metric, indicating users’ affirmation and commitment to a post [43]. In addition to sharing, users can also engage through reactions (including likes and 6 emoticons) and comments on social media platforms. A study used the common-sense model of illness self-regulation to describe humans’ cognitive and emotional systems to increase user engagement toward some attributes of illness representations on social media [44]; that is, shares, reactions, and comments were metrics driven by cognitive and emotional factors.

This study defined the basic metrics of user engagement as follows:

Metrics such as shares, reactions, and comments were commonly used in health communication studies to identify factors that increase user engagement [44-46]. However, user engagement is not always positive, especially in comments, which often contain errors, biases, and uncivil behavior [22,47,48]. The phenomenon of echo chambers on social media refers to selective exposure and confirmation bias, which limit diverse perspectives and foster polarized groups [3]. A study distinguishes between 2 belief systems among users: scientific and conspiracy [49]. Some studies have suggested that user-generated comments influence other users’ perceptions of posts. For example, it was found that the perceived quality of journalistic content being commented on decreased due to a lack of reasoning and incivility in user comments [47]. When exposed to critical comments about a fake news article, people are less likely to share or comment positively on the article [50]. A study reported that positive comments influenced people’s perceptions of a company’s reputation in crisis [51]. However, well-informed individuals often remain quiet on certain issues, whereas misinformed individuals tend to be more vocal online [52].

This study categorized comments into cognitive and emotional engagement [44]. For cognitive engagement, we were concerned with the conspiracy belief group and its counterpart, the scientific group [49]. We observed the data potentially driven by conspiracy belief as bias-based engagement and identified 2 types of engagement driven by scientific belief: one focused on knowledge itself, called knowledge-based engagement, and the other on misinformation issues, called critical engagement. Furthermore, we observed user engagement driven by emotions, which can be divided into positive, negative, and neutral emotional engagement according to the positive-negative valence framework [53]. We classified comments into one of six categories, which are further defined as follows:

User attitudes toward correction may change over time [8], and the misinformation contained in comments is also noteworthy [22]. Therefore, this study first investigated whether user engagement toward health misinformation correction has changed over the past 3 years. Second, according to the FTT, providing the gist as cues can effectively and directly convey messages to users [17]. Accordingly, we hypothesized that providing gist representations would help users grasp the main aspects of health misinformation issues. We also identified some important verbatim representations as post attributes to examine their relationship with user engagement. Finally, we considered whether the content of comments affects users’ choice to share and react to a post. On the basis of the earlier discussion, the research questions (RQs) posed by this study are as follows:

This study focused on health misinformation correction, drawing on 2 representative platforms, the Taiwan Fact-Checking Center (TFC) and the Ministry of Health and Welfare (MOHW), with rich data, including fact-checking and clarifying health-related event or policy posts and user feedback. Data were collected from the Facebook pages of the TFC, which was established in 2018 and has >160,000 followers on Facebook, and the MOHW, which has a significant following of approximately 1.52 million Facebook users, accounting for 6.45% of the total population of Taiwan. We collected 3769 posts from the TFC and 5103 posts from the MOHW from January 2020 to December 2022. The TFC targets various misinformation issues such as health, politics, economics, and celebrities. By contrast, the post content of MOHW focuses on public health, such as health policy announcements, and the correction of health misinformation accounts for a small portion. To ensure our focus was on health misinformation correction, we manually reviewed each post to identify those that involved both health and misinformation correction. For TFC posts, the reference keywords included health, pandemic, disease, food safety, folk remedies, vaccine, and health care. For MOHW posts, the reference keywords included misinformation, false information, clarification, and fake news. Because relying solely on keywords may either miss or capture non-target content, we primarily relied on manual review. After the filtering process, we found 34.89% (1315/3769) posts from the TFC source and 2.14% (109/5103) posts from the MOHW source. Corresponding to the posts from TFC and MOHW, 26,458 (including 913 self-replies by the TFC manager) and 40,920 comments (including 97 self-replies by the MOHW manager) were downloaded, respectively. Finally, the dataset consisted of 1424 posts and 67,378 comments from both platforms.

We initially used descriptive statistics to provide an overall overview of the data to answer RQ1. The mean share count was 79.12 (SD 293.14; range 0-4259), with 57 posts without sharing. The mean reaction count was 914.35 (SD 2,582.93; range 13-34,403). The mean comment count, excluding self-replies by the platform manager was 46.61 (SD 212.82; range 0-5092), with 198 posts without commenting. The 1.5% (1010/67,378) comments made by the platform manager supplied full information links or expressed positive feedback to some comments. These comments were excluded because we aimed to understand engagement from regular users. After examining the data distribution with Q-Q plots, we found that share, reaction, and comment count were nonnormally distributed. In this situation, it may be appropriate to transform the dependent variable using a natural logarithm in a linear regression model [9,59] or to use Poisson or negative binomial regression models [44,56]. We chose to use natural logarithms of the dependent variables as ln(shares+1), ln(reactions), and ln(comments+1). To improve the interpretability of the model, this study also transformed some independent variables using the natural logarithm, including ln(post length) and ln(engagement categories in comments+1). This study considered post length, readability, formal language use, and images as control variables based on prior literature [56,60]. For the regression models of 3 dependent variables, observations with studentized residuals that fall outside the mean of +3 or –3 were considered outliers and excluded. Then, regression analysis was performed to confirm the theory’s applicability and answer the RQs.

The study was approved by the National Tsing Hua University Research Ethics Committee (11308HT139). This study used publicly available data and processed and stored the data in compliance with the Personal Data Protection Act. As the data were publicly accessible, informed consent was not required.

To answer RQ1 regarding user engagement trends with misinformation correction during the COVID-19 pandemic, we aggregated 3 years of data and analyzed various types of user engagement. For RQ2, we performed hierarchical multiple regression to examine the relationship between the identified attributes and user engagement in sharing and reactions. For RQ3, we considered the content in comments as additional information on the post, incorporating 6 categories of user engagement in commenting as independent variables in the models for RQ2.

First, this study organized the data to understand the data profile. We merged the MOHW and TFC datasets to obtain an overview of the situation. We categorized the data by themes based on a quarterly distribution, as shown in Figure 2, in which the distribution of misinformation correction themes aligned with the development trajectory of the COVID-19 pandemic. The peak of misinformation correction occurred in the second quarter of 2021, coinciding with Taiwan’s level 3 alert and a vaccine shortage. The most common theme was health care, food safety, and home remedies, followed by understanding and controlling diseases and vaccine safety and policies.

To answer RQ1, we conducted a 1-way ANOVA to analyze user engagement over 3 years, and the results are shown in Table 1. Through homogeneity tests, these counts were found to be heterogeneous, and Welch ANOVA and Games-Howell post hoc comparisons were performed. We found that sharing (Welch F_{2, 805.887}=31.082; P<.001) and reactions (Welch F_{2, 802.838}=40.620; P<.001) declined significantly over time, while comments (Welch F_{2, 909.388}=11.253; P<.001) remained high in the first 2 years, before dropping in the third year. Under the general trends, we further examined the 3-year change patterns of comments based on user engagement categories. The patterns of the knowledge-based engagement (Welch F_{2, 931.301}=19.470; P<.001) and 3 emotional engagements (positive emotional: Welch F_{2, 741.713}=22.636; P<.001; negative emotional: Welch F_{2, 845.244}=11.857; P<.001; neutral emotional: Welch F_{2, 849.284}=21.055; P<.001) in commenting were similar, remaining consistent in the first and second years but significantly declining in the third year. Critical engagement declined significantly over time (Welch F_{2, 785.780}=32.996; P<.001), while bias-based engagement peaked in the second year and then significantly declined in the third year (Welch F_{2, 922.113}=3.120; P=.045).

We further explored the relative proportions among different categories of user engagement in commenting, as shown in Figure 3. In the first year of the COVID-19 pandemic, the engagement across categories was fairly balanced, with cognitive and emotional engagement each accounting for approximately half. The sum of knowledge-based and critical engagement comments exceeded those of bias-based engagement in 2020 in overall data, suggesting that the 2 beliefs could counterbalance each other. However, bias-based engagement became predominant by the second and third years on both platforms. In overall data, in 2020, bias-based engagement accounted for 23.55% (4920/20,893) of the comments; in 2021, it accounted for 47.51% (14,876/31,309); and in 2022, it accounted for 47.95% (3726/7770). Specifically, based on the MOHW data, in 2020, bias-based engagement was 32.97% (2776/8419), which rose to 52.39% (11,976/22,858) in 2021 and remained at 52.57% (2662/5064) in 2022. On the TFC data, bias-based engagement was 17.19% (2144/12,474) in 2020, 34.32% (2900/8451) in 2021, and 39.32% (1064/2706) in 2022. The discussions were dominated by bias-based engagement in the later stages of the COVID-19 pandemic. When comparing platforms, there was relatively more knowledge-based and critical engagement on the TFC platform, while the MOHW platform had relatively more positive emotional engagement in the first year.

Objective metrics such as shares, reactions, and comments for user engagement showed that interest in health-related misinformation significantly decreased in the third year. However, regarding the number of deaths due to COVID-19, the pandemic was most severe in Taiwan in its third year, with 7 deaths in 2020, 843 in 2021, and 14,636 in 2022 [61].

In the annotated dataset, of the 1424 posts, 261 (18.33%) contained risk, 868 (60.96%) contained awareness, 527 (37.01%) contained value, 163 (11.45%) contained numeric information, and 768 (53.93%) contained authority. In addition, of the 1424 posts, 202 (14.19%) did not target specific misinformation, 505 (35.46%) provided a simple correction, and 717 (50.35%) offered a detailed correction. Subsequently, concerning RQ2, we focused on the relationship between post attributes and 3 user engagement metrics: shares, reactions, and comments, as shown in Table 2. Model 1 controlled for background variables including post length (transformed using the natural logarithm), official status (converted to a dummy variable, where 1 represents the official platform [MOHW] and 0 represents the nonofficial platform [TFC]), readability, formal tone (where 1 indicates formal and 0 indicates informal), and image (where 1 indicates the attachment of at least 1 image and 0 indicates none). The dependent variables were also transformed using the natural logarithm. These background variables contributed significantly to the regression models for shares (R²=0.334; F_5,1412=141.380; P<.001), reactions (R²=0.407; F_5,1404=192.994; P<.001), and comments (R²=0.328; F_5,1414=137.794; P<.001). All background variables were predictive of these engagement metrics, except for post length, which did not significantly predict comment count.

Model 2 incorporated gist and verbatim representations. The R² changes for the 3 models were significant. In the model focused on predicting shares (R²=0.353; F_11,1406=69.808; P<.001; ∆ R²=0.020), risk (β=.07; P=.001), awareness (β=.09; P<.001), and facts (β=.14; P<.001) were significant predictors. When predicting reactions (R²=0.435; F_11,1398=97.292; P<.001; ∆ R²=0.028), awareness (β=.07; P=.001) and facts (β=.24; P<.001) were found to be significant factors. Similarly, when predicting comments (R²=0.351; F_11,1408=69.158; P<.001; ∆ R²=0.023), awareness (β=.06; P=.005), numeric (β=.06; P=.02), and facts (β=.19; P<.001) played significant roles. This finding suggests that 2 of the gist representations, risk and awareness, can enhance post endorsement and sharing, but only awareness was predictive of reactions and comments. Moreover, facts positively influenced the 3 metrics in the verbatim dimension, indicating that detailed clarifications enhanced shares, reactions, and comments.

For RQ3, we incorporated comments generated by 6 categories of user engagement into model 3, as presented in Table 3. To ensure the validity of our findings, we used the variance inflation factor to diagnose multicollinearity, which was within acceptable limits (<10). In the model predicting shares (R²=0.668; F_17,1400=165.904; P<.001; Δ R²=0.315), the 3 gist representations (risk: β=.08; P<.001, awareness: β=.08; P<.001, and value: β=.06; P<.001) were significant predictors, while facts (β=.02; P=.36) no longer predicted it. Then, when considering user engagement in commenting, in the cognitive aspect, only bias-based engagement (β=–.11; P=001) negatively predicted shares, and knowledge-based and critical engagement positively predicted shares (knowledge-based: β=.28; P<.001 and critical: β=.15; P<.001). In the emotional aspect, positive and negative emotional engagement both predicted shares, while neutral did not predict it (positive: β=.29; P<.001, negative: β=.09; P=.02, and neutral: β=.05; P=.15). Similarly, in the model predicting reactions (R²=0.772; F_17,1392=277.743; P<.001; Δ R²=0.337), the 3 gist representations (risk: β=.04; P=.007, awareness: β=.06; P<.001, and value: β=.05; P<.001) significantly predicted the reaction count, and facts (β=.12; P<.001) also predicted it. All comment categories, except for bias-based engagement (β=–.04; P=.15), significantly predicted reactions (knowledge-based: β=.19; P<.001, critical: β=.14; P<.001, positive: β=.26; P<.001, negative: β=.15; P=<.001, and neutral: β=.08; P=.002).

First, in response to RQ1, all metrics unsurprisingly decreased, and it is noteworthy that the proportion of bias-based engagement in comments increased on both platforms. At the outbreak of the COVID-19 pandemic, despite the potential chaos in information, the comment categories remained relatively balanced and were not dominated by any category. The increase in bias-based engagement in the second year was likely due to the first COVID-19 community transmission in Taiwan and the rise of COVID-19 vaccine–related issues. The result aligns with findings from previous studies, which show that comments often contain inaccuracies and are predominantly made by uninformed users [22,52].

Then, we compared our findings with similar studies that explored the relationship between post attributes and user engagement. Regarding background variables, according to model 1, readability aligned with previous research findings [56], which explained why readability influences social media engagement through fluency. Posts using a formal tone also showed higher user engagement, consistent with the literature [60], in which formality was a heuristic for credibility and importance. However, we did not control for the topics or timing associated with using a formal tone; more severe events may be more likely to use a formal tone. Posts containing images also positively impacted user engagement, a finding supported and explained in the literature by the concept of media type richness [59] or an approach-motivated perspective [44].

Drawing on the FTT and literature on misinformation, we identified 3 key gist representations and tested their effectiveness. The theory suggests that a message with a clear core and causal explanation is more likely to resonate with people, leading to behaviors such as sharing [30]. Moreover, this study suggests that gist representations provide cues that enhance the probability of users finding the respective information relatable or practical. Risk may involve terms such as danger or harm. Awareness often describes tips and tricks and is relatively neutral. Value sometimes refers to reassurance and tends to have more positive terms. However, only awareness is a significant predictor in each model. When considering the content of comments, all gist representations significantly predict reactions, indicating that risk and value retain their value within social media posts that include comments. In addition, value was the only gist of predicting comments negatively, although not significantly. Whether value can further reduce bias-based engagement remains to be examined in future studies. Overall, providing gist information increased shares, reactions, and comments, consistent with previous research showing that gist information enhances article sharing [14]. Awareness of misinformation is the most crucial factor in this study, as a previous study has emphasized awareness of misinformation as the foundational concept of citizen resilience to misinformation [35].

Regarding the verbatim dimension, we found that numeric information cannot significantly predict shares and reactions but can predict comments. Statistics also failed to predict sharing in another study based on the FTT [14]. According to discussions related to the FTT, individuals’ numerical abilities influence the numeric factor [38], and users might engage in discussions based on different perceptions of numeric information. However, statistics as a factor of source transparency was one of the predictors of shares and comments, suggesting that numerical information in news articles may increase the audience’s perceived accuracy [9]. A previous study indicated that participants engaged more with corrections even without statistical information due to the simplicity of the material, which made it easier to assess credibility. Then, they put less cognitive effort when the information included statistical evidence [20]. We offered a different perspective through FTT, suggesting that numeric information did not significantly affect user engagement in sharing and reacting, as these forms of engagement relied more on top-down meaning. However, numeric information increased commenting, which might rely on detailed information and higher cognitive engagement. Furthermore, this study identified authority as verbatim because the names of experts or institutes as references were detailed. Nevertheless, the attribute still conveyed a sense of credibility. The concept was similar to source transparency but with slight differences, and a previous study showed that using official reports increased shares and comments, and references to laws and news articles also increased comments [9]. In this study, this variable did not significantly predict any metrics. This could also be due to both platforms being trustworthy sources. Finally, facts significantly predicted all 3 metrics in model 2, indicating that posts with detailed and complete content generated higher user engagement, which is consistent with past experimental results [12,13,18]. Detailed corrections might contain alternative interpretations and context information for understanding and discussion, generating user engagement.

Finally, we considered that readers might refer to comment content to decide whether to share or react in model 3. An increase in knowledge-based and critical engagement comments, which reflect scientific beliefs, led to higher shares and reactions. Conversely, comments reflecting bias-based engagement negatively predicted shares, suggesting that these comments, much like uncivil comments [47], may diminish the quality of discussion threads and reduce share counts. Comments with positive and negative emotional engagement both predicted shares and reactions. Although comment categories could predict shares and reactions, all 3 gist representations still play an important role, indicating that gist communication can still be effective.

In summary, this study defined post attributes based on the FTT and considered a more diversified array of user engagement types to explore their relationship with post attributes. The main theoretical contribution of our study is that it presents a framework applied to misinformation issues validated using real social media data. It examined user engagement in commenting to better reflect the content users are most likely to encounter. The main practical contribution is the introduction of gist representations—risk, awareness, and value—that serve as simple but important cues when publishing correction messages, helping users grasp and address misinformation issues and encouraging content sharing.

FTT emphasizes the mutually supportive relationship between our cognition and social values and recommends using reasoned approaches to guide decision-making and emotional responses, rather than emotional appeals, to counter misinformation [17]. Building on this foundation, we identified 3 rational gist representations that connect with social values. Among these, awareness of misinformation proved to be a highly effective gist when comment content was not considered. In our data, this concept included elements such as increasing awareness of misinformation and fraud, encouraging verification of sources, promoting cautious sharing, and identifying characteristics and sources of misinformation. Mentioning the risks associated with misinformation can increase share counts. The data for this study referenced health risks, social panic, legal risks, and information security risks. Mentioning the value of health promotion is effective when considering comment content. The data for this study referenced health promotion and health literacy, a positive attitude toward the COVID-19 pandemic prevention, media or eHealth literacy, public trust, psychological well-being, and social confidence. These attributes serve as guidelines for designing content in misinformation correction and science communication.

This study had some limitations. First, this study examined data from 3 years during the COVID-19 pandemic, during which attention to health misinformation was heightened. There were also some health crises before and after the COVID-19 pandemic. Whether inferences based on the results of this study remain valid requires further examination. Second, we used GPT-3.5 to code comments efficiently, followed by a manual review. Cultural characteristics may have caused GPT-3.5 to misinterpret specific comments. For example, the colors blue and green sometimes represent political parties, and GPT-3.5 may not always accurately interpret this from the context, leading to some classification inaccuracies. Nevertheless, GPT-3.5 offers several advantages over previous machine learning methods, such as cross-language capabilities and emoji interpretation. Third, authority, as one of the independent variables in this study, has implications for credibility in some types of research. In the heuristic-systematic model, it is viewed as having an intuitive role, and in the elaboration likelihood model, it is considered a peripheral cue, suggesting the credibility of a message. We cannot confirm whether the dual roles cause construct confusion or whether the platform symbolizes knowledge authority. Authority did not significantly predict the outcomes in models 2 and 3; however, this does not imply that it is unimportant. Finally, we considered whether there was an attached image but did not examine its content or whether the images contained gist messages. Images attract user attention more quickly and should be considered in future research.

In addition to addressing the abovementioned limitations, future research could consider simplifying the 6 comment categories we used. For instance, knowledge-based and critical engagement could be combined, as well as positive and neutral emotional engagement. In addition, some comments directly responded to the content design of certain corrections. We observed that some posts were criticized for lacking focus, while others were praised for being practical. If these comments can be accurately extracted and systematically analyzed, it would also help assess whether this type of gist communication is well received. We found that posts containing numerical information, such as vaccine adverse reaction rates, received more comments, as users may interpret and engage more with rates and numbers. This area requires more systematic analysis and effective framing of numeric information in correction design to prevent misinterpretation on social media, enhancing the application of FTT. Finally, this study used regression analysis to explore the impact of 6 attributes on metrics; however, a single post often contains multiple attributes, and it may be possible to identify an optimal combination. Future studies could further explore this area.

This study enriches the theoretical understanding of the relationship between user engagement and the effectiveness of web-based communication strategies in conveying corrections for health misinformation. It provides evidence-based web content for delivering health misinformation corrections, and FTT explains user engagement with specific post attributes. The theoretical contribution strengthens the link between FTT and multiple engagement metrics beyond sharing and discusses post attributes in terms of gist and verbatim dimensions. In addition, by incorporating comment content, gist communication increases shares and reactions effectively. Finally, these findings provide a foundation for designing more effective content strategies to combat health misinformation, ultimately contributing to more resilient public health communication.