Abstract
Fact-checking information (FCI) serves in the fight against the infodemic and as an information service that helps people use their discretion in judging information in a post-truth era. Therefore, we investigated personal factors influencing users’ decisions in using and sharing COVID-19-related FCI in South Korea. The study took three steps to build hypotheses and collect data: a theoretical approach; an empirical approach through in-depth interviews; and an online survey amongst 304 information users who reside in Korea. More strictly, the interview data were analyzed through content analysis, and the online survey data were statistically analyzed using a SPSS 25.0 program. In conclusion, the study revealed that previous political FCI user research ignored health belief variables (health consciousness, perceived severity, and perceived susceptibility), which also influenced FCI usage. Moreover, critical prosuming literacy, a key predictor of sharing and disseminating misinformation, has a strong causal relationship with FCI seeking and sharing. The findings expand the notion of fact-checking from a type of journalism to “information” and “information services” and suggest that fact-checking has the potential to become an expanded information service in which experts in broader areas can participate.
1 Introduction
We live in a post-truth world where public opinion is increasingly being shaped and created by producing content that appeals to people’s personal beliefs and emotions. The boundaries between facts and claims are blurred, and misinformation, rumors, and superstitions are widely and rapidly distributed through Social Networking Sites (SNS). In emergencies, such as the COVID-19 pandemic, these problems are magnified. Health-related misinformation can directly affect our lives beyond merely obstructing decision-making, posing a substantial threat to people’s health (Islam et al. 2020) and encouraging conspiracy thinking that induces violent behavior (Jolley and Paterson 2020). Against this backdrop, fact-checking information (FCI) is being carried out, which involves fact-checking information distributed in a platform-centered media environment to fight the infodemic of misinformation.
The utility of FCI in responding to misinformation remains controversial. However, it can effectively prevent damage caused by misinformation when it is used well and correctly (Bode and Vraga 2015; Clayton et al. 2020; Cook, Lewandowsky, and Ecker 2017; Walter et al. 2020). Additionally, there is a possibility that the effect of information modification will be maximized when people seek and share FCI and act as mediators between non-users and providers (Celik, Muukkonen, and Dogan 2021; Hannak et al. 2014; Jiang and Wilson 2018). Personal beliefs and intimacy are often perceived to be more important than truth. Therefore, exploring who pays attention to the “facts” is essential.
2 Problem Statement
FCI has become mainstream over the past four years (Ryu and Chung 2020; Stencel and Luther 2019) and now covers a broader range of topics, especially after the spread of COVID-19 (Chong 2020; Kim and Cheong 2021). These studies indicate that there is a high demand for FCI. However, the information on people who use fact-checking services to seek and share correct information is ambiguous.
Most prior research has focused on political FCI (Nieminen and Rapeli 2019). However, the scope of FCI is diversifying; among the various types, FCI related to health issues accounts for a large proportion (Chong 2020). In addition, numerous people use online health information (Fox and Duggan 2013), and concerns regarding its quality are constantly being raised (Li et al. 2020; Swire-Thompson and Lazer 2020). Specifically, in the case of health-related FCI, users may react differently than in political FCI (Walter et al. 2020). Consequently, a separate study on health-related FCI is needed.
Current research on FCI is overwhelmingly focused on the US context (Nieminen and Rapeli 2019). However, different factors affecting FCI can be derived from different cultures. Therefore, we explored the case of South Korea to expand the perspective. Before the pandemic, there was considerable interest in health here, as Koreans recognized health information as vital. As a result, health-related indicators have continuously topped product purchase trends (Korea Standards Association 2018), and Koreans showed high interest in the corresponding news field (Korea Press Foundation 2015). However, Korean adults still seem to have difficulty accessing, evaluating, and utilizing accurate health-related information (Choi et al. 2020). Furthermore, to effectively respond to false information through FCI, it is necessary to identify the target who uses FCI and how this can help fight misinformation (Yum and Jeong 2019). Hence, we examined the Korean context to bridge this research gap and addressed the following questions.
Research Question 1: What is the status of Korean users’ FCI use?
Research Question 2: What personal factors influence the intention to seek FCI on COVID-19?
Research Question 3: What personal factors influence the intention to share FCI on COVID-19?
We tried to answer the research questions to develop countermeasures against the massive infodemic phenomenon. We also wanted to suggest directions for health information dissemination in an online environment. Finally, we desired to help experts to provide information online to offer user-centered information services.
3 Literature Review
3.1 Big Five (Big 5) Factors in Personality Trait
Trait theorists have argued that individuals have consistent “characteristics” that differ from others and that each individual determines their actions according to these characteristics. They referred to this ensemble of characteristics as personalities and have attempted to categorize these types (Goldberg 1990; Guilford et al. 1976). The model proposed by McCrae and Costa (1987) is the most widely used model of the Big 5 approach (O’Connor 2002; Saucier and Goldberg 1998). These five personality factors are neuroticism, extroversion, openness, conscientiousness, and agreeableness. Researchers have actively used this model to examine new media, such as SNS and online news, and discussed how users accept new media and what influences online behaviors (Azucar, Marengo, and Settanni 2018). In addition, some studies have suggested a link between personality traits and the identification of misinformation (Giachanou et al. 2020; Lee, Hwang, and Jeong 2021; Wolverton and Stevens 2020). However, there was no consensus on which of the five dimensions of the Big 5 affect the behavior of using FCI. Accordingly, we used all factors in the investigation.
3.2 Health Belief
The Health Belief Model (HBM) is a theoretical framework developed to predict behavioral responses to treatment in individuals with acute or chronic diseases (Champion and Skinner 2008). It is actively used in studies exploring health information-seeking behavior (Ahadzadeh et al. 2015; Mou, Shin, and Cohen 2016; Shang, Zhou, and Zuo 2021).
Perceived threats consist of perceived severity and susceptibility proposed through HBM. It influences health-oriented behaviors, including seeking information and adopting preventive health behaviors, such as using message channels (Ahadzadeh et al. 2015; Yun and Park 2010). Additionally, the higher the awareness of perceived susceptibility and severity of diseases, such as COVID-19, the stronger the intention to take preventive action (Bish and Michie 2010; Lee and You 2020; Prasetyo et al. 2020). Seeking health information through the Internet is a typical response to perceived health threats and helps find stability amid anxiety and uncertainty (Dutta and Feng 2007; Salkovskis et al. 2002). Therefore, such action is a preventive health behavior. In other words, the two variables of HBM affect the seeking and sharing of health information in terms of preventive behavior. Additionally, as collecting health information primarily prevents diseases, the information sought by people with high perceived severity and probability may be relatively reliable.
Meanwhile, “health consciousness” has also been studied as a critical factor in predicting health behavior (Dutta-Bergman 2004a; Iversen and Kraft 2006; Michaelidou and Hassan 2008). According to the concept of health consciousness, as defined by Hong (2009, 2011, people who are highly interested in health are motivated to take responsibility for their health and improve or maintain its level while being aware of and concerned about it. Furthermore, studies have explored health consciousness as a predictor of using various message channels to seek health information (Ahadzadeh et al. 2015; Dutta-Bergman 2006; Gould 1990; Moorman and Matulich 1993; Yun and Park 2010). These results identified the possibility that health consciousness may be related to the efficiency, criticism, and accuracy of information-seeking and processing.
3.3 Health Anxiety
Health anxiety, caused by perceived health threats, leads many to seek health information online. Therefore, people with health anxiety need to constantly seek relief to reduce their anxiety and uncertainty (Abramowitz and Moore 2007; Abramowitz, Olatunji, and Deacon 2007) by using online health information (Salkovskis et al. 2002). As a result, such individuals spend more time searching for health information online and are more likely to exhibit repetitive and excessive search behavior (Brown, Skelly, and Chew-Graham 2020; Muse et al. 2012; Starcevic and Berle 2013). Therefore, the assumption is that they will be exposed to more health information than the general public, and there is a high possibility of accessing FCI. Additionally, people with health anxiety are biased toward negative stimuli and respond more sensitively to negative information (Bar-Haim et al. 2007; Cisler and Koster 2010). Therefore, they will likely seek information from reliable sources to achieve emotional stability (Baumgartner and Hartmann 2011; Garfin, Silver, and Holman 2020).
Health anxiety and seeking online health information have mutually negative consequences (Abramowitz and Moore 2007; Asmundson et al. 2010; Garfin, Silver, and Holman 2020; Rachman 2012). Studies show that people with health anxiety may not conduct additional searches if, through the information they obtain, they recognize that the threat is minimal (Singh, Fox, and Brown 2016). In other words, FCI can positively influence individuals with health anxiety facing exaggerated or uncertain information about the side effects of the COVID-19 vaccine and those repeating excessive searches: hence, whether health anxiety affects the seeking and sharing of FCI was explored.
3.4 Literacy
New Media literacy (NML) is a concept that includes essential process technologies, including access, analysis, criticism, production, and participation in media content, reflecting the characteristics of new media in contrast to the traditional (Lee et al. 2015). Chen, Wu, and Wang (2011) proposed four conceptual frameworks for NML: 1) functional consuming; 2) functional prosuming; 3) critical consuming; and 4) critical prosuming. Subsequently, Lin et al. (2013) presented ten sub-indicators under these four classifications and further refined them.
Critical consuming literacy (CCL) is a barrier to protecting an individual from the influence of false information. However, exploring complex new media environments has proved to be a necessary competency (Xiao, Su, and Lee 2021; Yum and Jeong 2019). People with high CCL will find it easier to reach true, appropriate, and unbiased information by repeating the acts of “analyzing,” “synthesizing,” and “evaluating” various information. Additionally, we can assume that the information they finally reach is more likely to be fact-checked, with relatively strong truth attributes based on significant evidence rather than indiscriminate information.
Critical Prosuming Literacy (CPL) may be related to FCI in its conceptual definition; however, opinions on whether this capability enhances or suppresses the negative effects of media remain divided. For example, Yum (2018) found that CPL positively affects the spread of misinformation. However, it remains unclear what information is disseminated by those with high CPL when encountering misinformation and FCI simultaneously. Therefore, before considering the above possibilities, it will be necessary to explore how CCL and CPL exchange with FCI.
Meanwhile, e-health literacy (eHL) is a concept that refers to the ability to find, understand, evaluate, and apply knowledge to solve health problems or make health-related decisions (Norman and Skinner 2006). eHL capabilities do not fully guarantee the accuracy of information retrieval. However, studies have confirmed the association between eHL and personal motivation when participating in online searches for health information. Researchers have found the same relationship holds for eHL and information quality assessment (Hu et al. 2012; Li et al. 2014; Wong and Cheung 2019). Furthermore, those with higher eHL are more careful about the reliability and accuracy of the information they search (Neter and Brainin 2012), and those with lower eHL are more likely to use uncertain criteria when evaluating the quality of information (Diviani et al. 2016). Therefore, it is also necessary to explore the relationship between eHL and FCI related to COVID-19.
4 Methodology
4.1 Preliminary Study
There have been various studies on FCI, but health research has been scarce. Additionally, the influencing factors of who would seek and share such information remain unclear. Therefore, we conducted a preliminary study in the form of in-depth interviews before developing questionnaires and surveys. The interview results formed the base data for questionnaires and confirmed the research validity. The interview participants were people who had experience using FCI. Consequently, we explored the overall perception of personal characteristics, FCI, and online health information based on the analyzed transcripts. Data collection through telephone interviews took place from April 19, 2021 to April 30, 2021, for an average of 45 min (Table 1).
Information on the in-depth interviewees.
| Interviewee 1 | Interviewee 2 | Interviewee 3 | Interviewee 4 | |
|---|---|---|---|---|
| Sex | Female | Male | Female | Male |
| Age | 20 s | 20 s | 40 s | 50 s |
The interviews revealed that the interviewees requested and sought information because of their perceived probability of contracting COVID-19. Consequently, Interviewees 1 and 2 searched for fact-checked online health information to share relevant information and persuade people around them about the gravity of the disease. Furthermore, it showed how FCI users believe that FCI is more transparent and sufficiently valid to convince others regarding accurate health information. Thus, seeking FCI may be a prerequisite for sharing FCI.
All interviewees actively searched for information when they judged that their knowledge related to their interests was uncertain. We observed that they did not unconditionally trust their knowledge and online information but always doubted them, aiming to find new evidence to support their ideas. When facing uncertain knowledge, their attitudes seem to be related to personal factors, such as critical literacy—skills in judging the reliability and usefulness of messages. Therefore, we posit that critical literacy is related to seeking FCI.
Most interviewees showed significant interest in health and had a strong perception of the seriousness of COVID-19. In addition, their interest in health resulted in online health information search behavior. Therefore, we could discern that they had accessed FCI to obtain accurate health information. Furthermore, people with a high perceived seriousness of COVID-19 are likely to practice preventive behaviors toward COVID-19. Thus, these results help predict the link between online health information-seeking behavior (a preventive behavior) and the perceived gravity of COVID-19.
4.2 Research Model and Hypothesis Development
The following nine hypotheses were formulated based on the abovementioned theoretical foundation and preliminary research results to develop a model for evaluating the causal relationship between personal characteristics and seeking and sharing FCI (see Figure 1). We assumed that seeking and sharing FCI will be influenced by: 1) the Big Five factors in personality traits; 2) health beliefs; 3) health anxiety; and 4) literacy. The following shows each dimension of the research model and hypothesis:
H1: Big 5 affect the intention to seek FCI on COVID-19.
H2: Health beliefs affect the intention to seek FCI on COVID-19.
H3: Health anxiety affects the intention to seek FCI on COVID-19.
H4: Literacy affects the intention to seek FCI on COVID-19.
H5: Big 5 affects the intention to share FCI on COVID-19.
H6: Health beliefs affect the intention to share FCI on COVID-19.
H7: Health anxiety affects the intention to share FCI on COVID-19.
H8: Literacy affects the intention to share FCI on COVID-19.
H9: Intention to seek FCI affects the intention to share FCI on COVID-19.
The proposed research model.
4.3 Participants
We performed simple random sampling to select subjects without age, gender, or education restrictions. Data sampling was conducted via online surveys by recruiting participants through online communities with high utilization rates for each, such as second-hand trading applications, SNS, and health information sites. A total of 304 survey result data were collected. Table 2 shows the participants’ characteristics.
The participants’ characteristics.
| Category | Frequency | Ratio (%) | |
|---|---|---|---|
| Sex | Male | 100 | 32.9 |
| Female | 204 | 67.1 | |
| Age | 20 s (20–29) | 163 | 53.6 |
| 30 s (30–39) | 65 | 21.4 | |
| 40 s (40–49) | 24 | 7.9 | |
| 50 s (50–59) | 39 | 12.8 | |
| 60 s or higher | 12 | 4.3 | |
| Final academic background | Middle school or lower | 0 | 0 |
| High school | 15 | 4.9 | |
| College/university (attending) | 85 | 28.0 | |
| College/university (graduated) | 147 | 48.4 | |
| Master’s or Doctorate | 57 | 18.8 | |
| Occupational | Students and part-time workers | 108 | 35.5 |
| General office workers | 75 | 24.7 | |
| Education workers | 24 | 7.9 | |
| Public officials, police, and firefighters | 15 | 4.9 | |
| Service workers | 14 | 4.6 | |
| Personal business | 13 | 4.3 | |
| Architecture and civil engineering workers | 5 | 1.6 | |
| Other | 50 | 16.5 | |
4.4 Data Collection Tools
The questionnaire included four questions on demographic characteristics, nine open-ended questions, and 67 structured questions on FCI recognition. In the following, we explain each variable’s conceptual definition, the questionnaire’s composition, and the original source.
4.4.1 Big Five Factors in Personality Trait
We used the Big Five factors scale by Lang et al. (2011) to measure the personality trait factors. Each factor was measured with three questions. All 15 questions were scored on a 5-point Likert scale, ranging from 1 (not at all) to 5 (very much). We modified some expressions according to the pre-test results.
4.4.2 Health Belief
A scale developed by Deng et al. (2020), focusing on the perceived severity of COVID-19, was used. The scale is based on the authors’ selection of six items from the COVID-19 Pandemic Awareness Questionnaire published by a research team at Jungsan University and is scored on a 5-point Likert scale. Higher scores indicate higher perceived severity of COVID-19. In the study by Deng et al. (2020), six items on that scale had high structural validation (confirmatory factor analysis goodness of fit: χ2 = 50.58, df = 9, RMSEA = 0.06), and Cronbach’s alpha value was 0.81.
Second, we modified a measurement tool revised and supplemented by Lee et al. (2008) for influenza prevention behavior for COVID-19 to measure perceived susceptibility. Finally, the health interest variable was evaluated on a 5-point Likert scale using the measurement items of Dutta-Bergman (2004a, 2004b).
4.4.3 Health Anxiety
The Short Health Anxiety Inventory (SHAI) with 18 items, a shortened version of the Health Anxiety Inventory developed by Salkovskis et al. (2002), was used to measure health anxiety variables. SHAI is a suitable measure for both healthy and physically ill individuals (Alberts et al. 2013; Salkovskis et al. 2002). Additionally, it is a valid psychometric tool for evaluating health anxiety symptoms across non-clinical, clinical, and medical samples (Alberts et al. 2013). Several studies using SHAI showed high reliability and validity (Abramowitz, Deacon, and Valentiner 2007; Alberts et al. 2013; Ferguson 2009; Jungmann and Witthöft 2020). Therefore, we conducted the measurements using the 18 items (0–52 points) of SHAI.
4.4.4 Literacy
We utilized the scale developed by Koc and Barut (2016) and translated and used by Yum (2018) to measure CCL and CPL among NML. In each of those studies, Cronbach’s alpha values for CCL were 0.87 and 0.90, respectively, and CPL had values of 0.93 and 0.91, respectively. Consequently, we found the results to be highly reliable. Each scale consisted of five questions scored on a 5-point Likert scale.
eHL was measured via eHEALS (e-health literacy scale) developed by Norman and Skinner (2006) by partially changing the expression of tools modified and supplemented by Lee, Byoun, and Lim (2010). The eHEALS has been criticized for not fully explaining the essential capabilities in the digital environment (Britt and Hatten 2016; Griebel et al. 2018; Van der Vaart et al. 2011). Nevertheless, it remains the most widely used measure by researchers (Choi and DiNitto 2013; Ghaddar et al. 2012). The measurement was scored on a 5-point Likert scale.
4.4.5 FCI
The dependent variable measurement was organized by specifying and providing an article posted on the Seoul National University (SNU) Fact Check Center as an example and asking about the overall perception, seeking, and willingness to share it. The fact-checking article was based on a report by Kim (2021) regarding the widespread claim that “vaccinated people have a six times higher mortality rate when they catch the COVID-19 mutant virus.” “General article-type information” was presented, reflecting on the contents, and “bullet form information like report” was used by trimming only the structural aspects of the contents, reconstructed in a remodeling format by the SNU Fact Check Center (2021).
4.4.6 Intention to Seek and Share FCI
The information-seeking intention was measured using an instrument translated by Lee and Oh (2017), a sub-dimension of communication behavior developed by Kim and Grunig (2011). First, the variable was measured on a 5-point Likert scale in three items and modified to suit the present purpose. Then, among the items measuring the secondary communication intention of Schultz, Utz, and Göritz (2011), three were modified and translated by Lee and Oh (2017) and then remodified to suit the present purpose. Finally, we used a 5-point Likert scale to score the items.
4.5 Data Analysis
We obtained 304 samples from the survey and conducted a factor analysis and reliability verification. We also used SPSS 25.0 program for the data analysis. Initially, descriptive statistical analysis was performed to identify the respondents’ demographic and FCI-related characteristics. Then, validity and reliability analyses were conducted via exploratory factor analysis and confirmation with the Cronbach alpha coefficient. Then, correlation analysis was used to confirm the relationships between variables. Then, simple regression analysis between independent and dependent variables was performed based on the research model to confirm the directions of the relationships and the magnitudes of influences. Finally, multiple regression analysis and mediating analysis were conducted to confirm the direct/indirect influence of the relationship between independent and dependent variables based on the research model. Accordingly, we verified our hypothesis via previously mentioned analyses.
5 Finding
5.1 Characteristics Related to the Survey Respondents’ Experience in Using FCI
Among the respondents, 202 (66.4 %) said they had previously used FCI (“users” from now on), and 102 (33.6 %) reported having no experience using it (“non-users” from now on). As many as 138 people said they had used fact-check information for politics, followed by health (125), society (110), economy (91), science (68), general (47), and others (12).
Moreover, to determine what information structure users find more useful, the FCI related to COVID-19 was presented in three formats: (1) summarized information so that only the core could be grasped; (2) general article-type information; and (3) bullet form information, like a report. Finally, we asked the respondents which information was most helpful. Most people recognized (3), the type used by the SNU Fact Check Center (144, 47.4 %), followed by (1) and (2).
Additionally, we asked the respondents to write a short reason to determine why the respondents perceived the bullet form information format more helpful. In response, 94 of the respondents who chose (3) and 76 who chose (1) answered the question. Based on these responses, frequency analysis for each word was performed to organize the top ten words in Table 3. Respondents who considered (3) helpful felt “the verification process was organized in the form of a report, so it was easy to see, read, and understand at a glance.” The frequency of the word “core” was overwhelmingly high for respondents who chose (1), who seemed to recognize it as applicable “because only the core and points can be easily grasped through short, simple, concise, and brief information.”
Reasons for the usefulness of FCI perceived by the respondents.
| Category | Frequency | Category | Frequency | ||
|---|---|---|---|---|---|
| Information (3) | Arrangement | 17 | Information (1) | Core | 30 |
| At a look | 15 | Figure out | 13 | ||
| Verification | 13 | Conciseness | 13 | ||
| Easy | 12 | Easy | 10 | ||
| Comfortable | 10 | Arrangement | 6 | ||
| Understanding | 9 | The main point | 6 | ||
| Report | 9 | Short | 6 | ||
| Simple | 8 | Simple | 6 | ||
5.2 Characteristics Related to the Experience of Using Health-Related FCI of Survey Respondents
As a part of the response to RQ 1, examination of the characteristics related to the experience of using health-related FCI in this sample revealed that 125 respondents used FCI regarding health topics. Furthermore, as a result of comparing the ratio of FCI users and health-related FCI users by age and education through frequency analysis, no clear difference for any item was observed. Therefore, FCI users may be more likely to access health-related FCI universally, regardless of age or education (Table 4).
Characteristics related to the use of health-related FCI by respondents.
| Category | Frequency (people) | Ratio (%) | Category | Frequency (people) | Ratio (%) | ||
|---|---|---|---|---|---|---|---|
| Health-related FCI users by age (N = 125) | 20 s | 66 | 52.8 | FCI users by age (N = 202) | 20 s | 113 | 55.9 |
| 30 s | 22 | 17.6 | 30 s | 39 | 19.3 | ||
| 40 s | 15 | 12 | 40 s | 20 | 9.9 | ||
| 50 s | 17 | 13.6 | 50 s | 24 | 11.9 | ||
| 60 s∼ | 5 | 4 | 60 s∼ | 6 | 3.0 | ||
| Total | 125 | 100 | Total | 202 | 100 | ||
| Health-related FCI users by final education (N = 125) | High school | 7 | 5.6 | FCI users by final education (N = 202) | High school | 8 | 4.0 |
| College/university (attending) | 33 | 26.4 | College/university (attending) | 57 | 28.2 | ||
| College/university (graduated) | 58 | 46.4 | College/university (graduated) | 95 | 47.0 | ||
| Master or Doctorate | 27 | 21.6 | Master or Doctorate | 42 | 20.8 | ||
| Total | 125 | 100 | Total | 202 | 100 | ||
Additionally, for the FCI related to the “COVID-19 vaccine,” the average value of usefulness perception in relieving anxiety about the disease and vaccines was moderate or higher in each case (3.64 and 3.63, respectively). On average, respondents recognized COVID-19 fact-checking information as helpful in relieving anxiety. These results suggest that information confirmed via experts and evidence can positively suppress anxiety in an emergency, such as the COVID-19 pandemic. However, these results will need to be explored closely in the future.
5.3 Influence of Personal Factors on Seeking and Sharing FCI
To answer RQs 2 and 3, and research hypotheses 1 to 9, we conducted multiple regression analysis on two dependent variables (seeking and sharing FCI), representing the use of FCI to confirm the direct causal relationship between the individual factors affecting each dependent variable. Before performing regression analysis, independent variables with no direct relationship with the two dependent variables were excluded through correlation analysis. Then, we used stepwise regression analyses to derive the optimal regression output by avoiding overfitting caused by too many independent variables.
5.3.1 Direct Effects of Personal Factors for Intention to Seek FCI
Table 5 shows the results of the final calculated stepwise multiple regression analysis. Five independent variables, including eHL, extroversion, openness, agreeableness, and health anxiety, were not significant. Thus, we excluded them from the analysis.
Multiple regression analysis result for intention to seek FCI.
| Dependent variable | Independent variable | B | β | t |
|---|---|---|---|---|
| Seeking FCI | (Constant) | 3.185E−17 | 0.000 | |
| Critical consuming literacy | 0.188 | 0.188 | 3.155** | |
| Critical prosuming literacy | 0.169 | 0.169 | 2.853** | |
| Health consciousness | 0.151 | 0.151 | 2.734** | |
| Perceived severity | 0.115 | 0.115 | 1.851 | |
| Perceived susceptibility | 0.124 | 0.124 | 2.079* | |
| F = 16.330 (p < 0.001***, 0.01**, 0.05*), R 2 = 0.215, adjusted R 2 = 0.202, D-W = 1.898 | ||||
The multiple regression analysis (see Table 5) found a regression model R 2 of 0.215 (adjusted R 2 = 0.202), with an explanatory power of 21.5 %. We identified VIF (variance inflation factor) values to check for multicollinearity, indicated by a strong correlation among independent variables. By applying the most conservative reference value of 3, we determined no multicollinearity with less than 2. The Durbin-Watson statistics, which test autocorrelation in a model, were computed as 1.898. Additionally, the relative influence of independent variables was compared through the relative comparison of β, which is the standardization coefficient value, showing CCL (0.188) to have the most significant influence, followed by CPL (0.169), health consciousness (0.115), and perceived susceptibility (0.124). All independent variables had a positive (+) influence. Consequently, H2 and H4 were accepted.
5.3.2 Direct Effects of Personal Factors for Intention to Share FCI
Table 6 shows the results of the final calculated stepwise multiple regression analysis. We confirmed that the five independent variables were insignificant. The variables include eHL, openness, conscientiousness, agreeableness, and health anxiety. Thus, these variables were excluded from the analysis.
Multiple regression analysis result for intention to share FCI.
| Dependent variable | Independent variable | B | β | t |
|---|---|---|---|---|
| Sharing FCI | (Constant) | 9.157E−17 | 0.000 | |
| Critical consuming literacy | −0.100 | −0.100 | −2.092* | |
| Critical prosuming literacy | 0.137 | 0.137 | 2.868** | |
| Health consciousness | 0.108 | 0.108 | 2.398* | |
| Extroversion | 0.071 | 0.071 | 1.665 | |
| Perceived severity | 0.099 | 0.099 | 2.238* | |
| Seeking FCI | 0.609 | 0.609 | 13.432*** | |
| F = 52.314 (p < 0.001***, 0.01**, 0.05*), R 2 = 0.514, adjusted R 2 = 0.504, D-W = 1.888 | ||||
The multiple regression analysis (see Table 6) resulted in a regression model R 2 of 0.514 (adjusted R 2 = 0.504) and explanatory power of 51.4 %. By applying the most conservative reference value of 3 here, VIF indicated no multicollinearity with less than 2. Therefore, the Durbin-Watson statistics were computed as 1.888. Additionally, the relative influence of independent variables was compared through the relative comparison of β, which is the standardization coefficient value, showing that FCI-seeking intention (0.609) had the most significant influence, followed by CPL (0.137), health consciousness (0.108), CCL (−0.100), and perceived severity (0.099). Accordingly, H6, H8, and H9 were supported.
5.3.3 Indirect Effects Between Measurement Variables
Based on the correlation analysis results, exploring the direct influence relationship between each measurement variable through multiple regression analysis showed no significant influence on personality traits (H1, H5), health anxiety (H3, H7), and eHL (H4, H8). The personal traits included extroversion, openness, agreeableness, and conscientiousness. However, we conducted a simple regression analysis between independent and dependent variables to predict the relationship. All 24 relationships were within the range of significance (p < 0.05). Therefore, the analysis results required additional verification.
We conducted a mediating analysis to determine whether these variables indirectly affected the relationship between seeking and sharing FCI. Specifically, we used the Hayes Process (Hayes 2018) to test the mediation effects. The analysis used model 4 of the SPSS 25-PROCESS MACRO v3.4. The number of bootstraps was 5000 with a 95 % confidence interval. In verifying the mediating effect, the analysis was performed by controlling the remaining independent variables, except for the variables to be analyzed. Table 7 shows the summarization of relationships demonstrating a significant level of indirect effect. For example, we found 13 indirect effects to be significant at the 95 % confidence level in the relationship among the present measurement variables. H3 and H7 were adopted, and H1 and H5 were partially adopted through this statistical analysis.
Mediation effect analysis result (N = 304).
| Independent variable → | Mediating variable → | Dependent variable | Category | β | 95 % confidence interval | |
|---|---|---|---|---|---|---|
| LLCI | ULCI | |||||
| Extroversion | Critical prosuming literacy | Seeking | H1, H4 | 0.0197* | 0.0008 | 0.0463 |
| Critical prosuming literacy | Sharing | H1, H8 | 0.0207* | 0.0018 | 0.0458 | |
| Openness | Critical prosuming literacy | Seeking | H1, H4 | 0.0182* | 0.0002 | 0.0435 |
| Critical prosuming literacy | Sharing | H1, H8 | 0.0244* | 0.0048 | 0.0510 | |
| Agreeableness | Critical consuming literacy | Seeking | H1, H4 | 0.0303* | 0.0015 | 0.0646 |
| Perceived severity | Health consciousness | Seeking | H2 | 0.0299* | 0.0000 | 0.0800 |
| Perceived susceptibility | Critical prosuming literacy | Sharing | H6, H8 | −0.0244* | −0.0509 | −0.0051 |
| Health anxiety | Critical consuming literacy | Seeking | H3, H4 | 0.0031* | 0.0001 | 0.0071 |
| Critical prosuming literacy | Seeking | H3, H4 | −0.0041* | −0.0096 | −0.0001 | |
| Critical prosuming literacy | Sharing | H7, H8 | 0.0034* | 0.0008 | 0.0071 | |
| e-Health literacy | Critical consuming literacy | Seeking | H4 | 0.0585* | 0.0048 | 0.1270 |
| Critical prosuming literacy | Seeking | H4 | 0.0234* | 0.0008 | 0.0581 | |
| Critical prosuming literacy | Sharing | H8 | 0.0257* | 0.0046 | 0.0543 | |
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p < 0.05∗, 0.01∗∗, 0.001∗∗∗.
6 Discussion
6.1 Implications
According to the results of this study, today’s information consumers need to have information in a form that is easy to grasp immediately based on responsibly and diligently collected evidence by experts and also more likely to be true. Accordingly, one of the alternatives to consumers’ desired artifacts is FCI, which needs to be structured to satisfy users’ expectations. However, anyone can claim to have their writings based on fact-checked information without restrictions, especially in online publications. Consequently, there are evidence-lacking or biased “fact-checked” claimed but personal opinions that did not meet users’ expectations. This problem led to users’ general mistrust of “fact-checked” information and dissatisfaction. To overcome these issues and to further vitalize the fact-checking information service, there should be efforts by both information experts (providers) and users.
First, we believe those fact-checking providing subject experts need to collect and prepare information at a level that matches the type of information that users want for fact-checking. In addition, it is necessary to broaden the scope of information providers to include various subject matter experts in the fact-checking process to secure more supporting evidence and suppress bias during the fact-check compilation stage. Lastly, we should remember that not all fact-checked labeled information is really fact-checked, and the claimed facts might not be accurate. Thus, we should strive to strengthen users’ media literacy through education to help them determine the true nature of the fact-checked information. Accordingly, users should evaluate all online information critically. Additionally, we should assess whether the fact-checked information had credible sources before accepting information at its face value.
In addition, for the fact-check information related to the “COVID-19 vaccine” presented as a case in this study, the average values of perceived usefulness in anxiety about COVID-19 and vaccines were all above average (3.64 and 3.63, respectively). Thus, we can conclude that those survey respondents recognized COVID-19-related FCI as useful. Consequently, we can claim that expert-verified credible sources will positively affect people in suppressing unnecessary anxiety during the pandemic. However, there should be a future study to explore the claim further.
We found that interest in health and COVID-19-inspired health-related convictions directly influenced the FCI-seeking intention. Similarly, interest in health and COVID-19-inspired health-threatening seriousness directly influenced the FCI-sharing intention. Accordingly, we discovered that the affecting factors could vary based on the fact-checked subject areas. Thus, future researchers in fact-checking should consider the subject area-oriented factors in their studies. Furthermore, online health information providers should consider the FCI consumers’ information use intentions in their services.
The CPL had a positive effect on both dependent variables. Thus, we can infer that the movement to seek “truth” and one’s NML ability may be in contact in the era of post-trust. Consequently, people’s critical ability to consume and produce new media expands one’s acquisition of correct information into the new producer and transmitter of nearly accurate information in the chaotic pandemic period. Accordingly, we can claim that an individual’s NML capabilities are essential and that there should be active education and social support for such capacity.
6.2 Limitations
Our study was about the health-related FCI regarding COVID-19. Thus, it might be premature to generalize our findings to all types of health-related FCI. However, we wish to point out that our study was one of the first attempts to explore health-related FCI, which had not been widely researched. Thus, we hope to see additional studies on FCI regarding post-COVID-19 health issues.
Our study focused on adult internet information consumers. Most samples came from respondents in their 20 s. However, previous studies showed that Internet usage rates in Korea were constant for people from their twenties to their fifties, although people in their fifties continue to have the highest active media service use (Kim 2020). To fully understand “information seeking” and “information sharing,” which are actions that actively search for and share information among different online information resources, it is essential to consider both Internet use and one’s capacity to use media services.
7 Conclusions
Approximately five years have passed since fact-checking, a new type of information service, became available in South Korea. As a result, there have been attempts to improve the effectiveness of FCI. However, there was a lack of observation from the user’s point of view regarding who uses these services and how they perceive them. Accordingly, we investigated the online information users’ recognition of FCI and personal factors influencing the seeking and sharing of FCI related to COVID-19 in South Korea.
We statistically analyzed health beliefs and health anxiety variables, which were not discussed in existing FCI user studies as factors influencing the use of fact-checking. The FCI concept has been mainly studied in the journalism field. However, we expanded the FCI concept as a new type of information service from the library and information science perspective.
We still face the one-dimensional problem of confirming fact-verified information as true and useful. However, FCI plays an essential role in our society because it is a move to pursue the truth in an era of post-truth, where the distinction between truth and claim has become ambiguous, making the truth unimportant. Therefore, our concept expansion should allow FCI to be about a broader range of topics in the future. Also, we hoped to encourage experts from various fields to participate in the FCI development and further related research.
CPL was a predictor of sharing and spreading misinformation in related studies (Lee 2020; Yum and Jeong 2019). However, we found that CPL has a strong causal relationship with the seeking and sharing of FCI. A follow-up study will be needed to determine a person’s choice to share either fact-checked information or misinformation when both are presented simultaneously. Furthermore, we need to find out the personal factors affecting the person’s decision.
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Research funding: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022S1A5C2A0309359721).
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