Buffering Against Exposure to Mental Health Misinformation in Online Communities: The Interplay of Depression Literacy and Expert Moderation

doi:10.21203/rs.3.rs-2787217/v1

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Research Article

Buffering Against Exposure to Mental Health Misinformation in Online Communities: The Interplay of Depression Literacy and Expert Moderation

https://doi.org/10.21203/rs.3.rs-2787217/v1

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Journal Publication

published 18 Aug, 2023

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Background

The proliferation of health misinformation on social media is a growing public health concern. Online communities for mental health (OCMHs) are also considered an outlet for exposure to misinformation. This study explored the impact of the self-reported volume of exposure to mental health misinformation in misinformation agreement and the moderating effects of Depression literacy and Type of OCMHs participation (expert vs. peer-led).

Methods

Participants (n = 403) were recruited in Italian-speaking OCMHs on Facebook. We conducted regression analyses using PROCESS macro (moderated moderation, Model 3). Measures included: the Depression Literacy Questionnaire (Griffiths et al., 2004), the Self-reported Misinformation Exposure in the OCMHs (3 items), and Misinformation Agreement with the exposure items (3 items). Whether participants were members of expert or peer-led OCMHs was also investigated.

Results

The final model explained the 12% variance in the Agreement. There was a positive and significant relationship between Misinformation Exposure and Misinformation Agreement (β = .3221, p < 0.001), a significant two-way interaction between Misinformation Exposure and Depression Literacy (β = -.2179, p < 0.01), and between Self-reported Misinformation Exposure and type of OCMH (β = -.2322, p < 0.05), such that at higher levels of Depression Literacy and in case of participation to in expert-led OCMHs, the relationship Misinformation Exposure-Misinformation Agreement was weaker. Finally, a three-way interaction was found (β =.2497, p < 0.05) that showed that Depression Literacy moderated the positive relationship between Misinformation Exposure and Misinformation Agreement such that the more misinformation participants were exposed to, the more they agreed with it unless they had higher levels of Depression Literacy; this, however, occurred only if they participated in peer-led groups.

Conclusions

Results provide evidence that the more members reported being exposed to mental health misinformation, the more they tended to agree with it, however this was only visible when participants had lower depression literacy and were participating in peer-led OMCH.

Results of this study suggest that both internal factors (i.e., high depression literacy) and external factors (the type of online community individuals were participating to) can buffer about the negative effects of misinformation exposure. It also suggests that increasing depression literacy and expert community moderation could curb the negative consequences of misinformation exposure related to mental health. Results will guide interventions to mitigate the effects of misinformation in OCMHs, including encouraging health professionals in their administration and implementing health education programs.

online communities

misinformation

mental health

depression literacy

expert moderation

Misinformation and online mental health communities

Due to the increasing popularity of the internet (1, 2), more specifically of social media (3) as a venue for seeking and sharing health information, there is a growing concern about the spread of health misinformation (4, 5). Recently, these concerns have intensified due to the COVID-19 pandemic (6). A recent systematic review of reviews found that the prevalence of health-related misinformation on social media ranged from 0.2–28.8% (7).

Swire-Thompson and Lazer (8) define misinformation as “information that is contrary to the epistemic consensus of the scientific community regarding a phenomenon”. Health misinformation is a specific type of misinformation that refers to a “health-related claim of fact that is currently false due to a lack of scientific evidence” (9). As information on social media is generated by users, it can be subjective or inaccurate, therefore a worrisome source of health misinformation (6, 10) as it can also be archived and persist over time until it is corrected or deleted, becoming a dangerous resource for future health information seekers (11).

Public health researchers and practitioners are increasingly preoccupied with the potential for health misinformation to misinform and mislead the public as it not only creates erroneous health beliefs confusion and reduces trust in health professionals but can also “delay or prevent effective care, in some cases threatening the lives of individuals” (5, 10). Thus, combating its effects has become crucial for public health (9, 12) and can be accomplished only by understanding its psychological drivers (13) and complementary buffers. One particularly prominent finding that helps explain why people are susceptible to misinformation is the ‘illusory truth effect’, according to which repeated information is perceived as more truthful than new information (14–18).

Online communities on social networking sites have been identified as places that can easily spread health misinformation (19, 20), particularly by creating echo chambers where erroneous information is reinforced as frequently repeated (10, 11). The prevalence of information of low relevance and questionable validity in online health communities is well-documented (23–25).

Online communities specifically for mental health symptoms (OCMHs) are increasingly present on social networking sites, especially among younger generations (26). They can also be considered an outlet for misinformation. A recent content analysis [in peer review] has found extremely high levels of misinformation in OCMHs, and even communities moderated by health professionals (expert-led) were not exempt from this issue. This is in line with other studies showing that healthcare professionals can also spread misinformation in various ways (10).

Online communities generally rely on the work of volunteers to police themselves (27), some of them being health professionals, others peers with no expert credentials (28–30). Although healthcare professionals play a critical role in ensuring information quality in online health communities (31, 32), the literature on this topic is scarce, especially regarding the differences that these two types of groups might perform, particularly in relation to misinformation.

Addressing misinformation in mental health is crucial for two key reasons. Firstly, mental health is a growing public health concern (33), which has been underestimated even though about 14% of the global disease burden has been attributed to neuropsychiatric disorders such as depression (25). Secondly, mental health conditions are frequently stigmatized and misunderstood, resulting in a greater prevalence of misinformation online and offline (35–37). Nevertheless, few studies have examined misinformation regarding mental health specifically. Therefore, it is crucial to understand the conditions that can mitigate the outcomes of mental health misinformation exposure.

In the context of the illusory truth effect, it has been found that the effects of repeated exposure to misinformation on perceptions of accuracy disappeared when the receiver knew the actual truth and that people were more likely to believe misinformation when they were unfamiliar with the issue at hand (18). Other studies have shown that knowledge is key to buffering against misinformation exposure (39–41).

Health literacy is at the heart of any discussion of health-related misinformation, which can be defined as “the degree to which people have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions” (42). In a systematic review, results indicated that low health literacy was negatively related to the ability to evaluate online health information (19). Previous studies have shown that knowledge moderated the relationship between exposure and beliefs but focused on other aspects of literacy, such as news literacy (16) or media literacy (17). One study found that a higher level of cancer literacy helped participants identify misinformation and prevented them from being persuaded by it (22).

However, solely concentrating on individual differences in susceptibility to misinformation while ignoring other potential external factors may not be the most effective approach. As we mentioned earlier, OCMH typologies can vary depending on their moderators' expertise. Previous research has demonstrated that the knowledge and guidance provided by peer patients differed significantly from that offered by professional healthcare providers (47). Therefore, it is critical to investigate potential differences between these groups in misinformation exposure outcomes.

Current Research And Hypotheses

Based on the above literature, the present study aims to investigate the relationship between exposure to mental health misinformation in Italian online communities for mental health and related agreement, focusing on two aspects that might impact this relationship and the interplay between them: depression literacy and type of OCMHs moderation.

As in the health context, different types of literacy exist in different contexts, in the present study, we focused on declarative knowledge about depression literacy, depression being one of the most common mental illnesses in Italy (48, 49) and also worldwide (50). Depression literacy is a facet of the mental health literacy concept, defined as an individual’s knowledge regarding mental health (23). Mental health literacy, or lack thereof, has been used as a possible factor to explain uncertainties or lack of knowledge about mental health and the ensuing effects on effective treatment and care (24). Determining whether depression literacy levels can buffer the effects of misinformation exposure is critical, also as identifying which segments of the population are especially vulnerable to health misinformation and developing interventions for individuals at risk.

However, also external factors can influence the relationship between exposure and agreement with misinformation, including aspects related to the expertise of content moderation of the communities.

Whether healthcare professionals or not, moderators have various tools, such as deleting content, suspending users, or correcting inaccurate information. However, given that the quality and accuracy of online health information provided by OCMHs can vary significantly (33), it is crucial to consider the potential implications of participating in OCMHs with different content moderation types, as this may affect the degree of exposure to health-related misinformation and its associated consequences. Furthermore, the interplay between internal (depression literacy) and external (type of OCMHs moderation) factors sheds light on the most vulnerable individuals within online communities. This approach will expand upon previous research on individual differences in the misinformation susceptibility (16, 41) and provide a more comprehensive understanding of the complex dynamics that may influence agreement with misinformation exposure within OCMHs.

Hypotheses And Research Question

We tested the following hypotheses using a Moderated Moderation Model (see Fig. 1 for the hypothesized model).

First, based on the literature on the Illusory truth effect (34), we expect that:

H1: Misinformation Exposure will be positively and significantly associated with Misinformation Agreement).

However, based on the above literature on the influence of the protective role of knowledge and the differences that might emerge in different types of expert content moderation, we hypothesize that:

H2: The positive association between Misinformation Exposure and Misinformation Agreement will be moderated by depression literacy.

H3: The positive association between Misinformation Exposure and Misinformation Agreement will be moderated by type of OCMHs participation.

Then, as a research question, we will test whether Depression literacy and Type of OCMHs participation also interact with each other in the following way:

RQ: The moderating effect of depression literacy on the relationship between Misinformation Exposure and Misinformation Agreement will be further moderated by the type of OCMHs participation.

Participants and procedure

The data for this study were collected through an online survey on Qualtrics in spring 2022. To recruit participants, the principal investigator reached out to administrators of 72 Italian-speaking OCMHs, all identified by entering mental health-related keywords into the Facebook search bar. OCMHs included in the study could have been moderated by mental health professionals (expert-led) or peer-led. In total, 65% of OCMHs (n = 51) agreed to participate, while 12% (n = 9) declined, and 15% (n = 12) did not respond. Participants were recruited through a video presentation shared on the collaborating OCMHs, including a questionnaire link. They were rewarded with a financial incentive and access to artistic/poetry videos. The Ethics Committee of the university approved the study design. In total, we collected 493 responses with all variables of the present study filled out. 74 responses were eliminated from the dataset as participants signed “I do not remember” in the OCMHs they were participating in. Thus, they did not specify the OCMH, making it impossible to code an important variable for the study. In the case of double completion of the wave (n = 16 cases), the earliest wave was included in the study. The Ethics Committee of Università della Svizzera italiana approved the study design (CE_2021_10). All methods were performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants.

Measures

The survey's items are reported in Additional File 1.

Misinformation Exposure

The Italian scale was created ad hoc based on prior studies measuring susceptibility to misinformation. Participants were asked to rate how often they encountered three misinforming claims in the preceding 30 days. The three items were based on the results of a previous content analysis on the groups (28). The response options were from 1 = “Never” to 5 = “Very often”. Other studies measured exposure similarly by asking participants how frequently they had seen or heard misinforming statements in the past 30 days e.g. (35, 36). We averaged scores on the three misinformation items into an overall index as in (56).

Misinformation Agreement

Misinformation Agreement was measured by asking the participants their degree of agreement with the same items presented in the Misinformation Exposure variable (Likert scale: 1 = completely disagree, 7 = completely agree). In line with other researchers, an index score was created by taking the average of all item ratings e.g. (57).

Depression Literacy Questionnaire

The Depression Literacy Questionnaire (58) assesses mental health literacy specific to depression. The questionnaire consists of 22 items which are true or false. Respondents can answer each item with one of three options – true, false, or don’t know. Each correct response received one point, and a sum score represented the extent of depression literacy (α = .74). The Italian version used in the study was validated (unpublished). Translation and back translation were conducted to confirm the scale's accuracy and appropriateness. A bilingual expert panel composed of an expert in the topic and the two translators was convened to identify and resolve the inadequate expressions/concepts of the translation, as well as any discrepancies between the forward translation and the existing previous versions of the questionnaire. The questionnaire was pilot tested with a small sample of participants (n = 10) to ensure the clarity of the items. We then administered the questionnaire to a larger sample of participants (n = 286) and conducted a series of analyses to assess its reliability and validity. Internal consistency was assessed using Cronbach's alpha, and we found good internal consistency for the scale (α = .74). We also examined the convergent validity of the scale by comparing it to well-established health literacy measured: the Newest Vital Sign (r = .135, p < .05) and the HLS-EU-Q16 (r = .231, p < .01).

Type Of Ocmhs Participation

Participants marked from a list the number of OCMHs they participated in the last month preceding the survey. The first author checked the presence of expert moderators in the OCMHs mentioned during the survey. This led to creating a dummy coded variable in which 0 = peer-led only OCMHs participation, 1 = mixed participation, and 2 = expert-led only participation.

Covariates

The frequency of participation in the groups was assessed using a 4-point scale, ranging from "rarely" (less than once a month) to "very often" (almost every day). The frequency of participation was based on previous studies used to measure the frequency of participation (26). Participants were also asked to indicate their age and gender (male, female, other, or prefer not to answer). Due to the scarcity of values in the other categories, the latest was the further dummy coded in 0 = other genders, 1 = male.

Data Analyses

Descriptive statistics were calculated for all measures to assess the normal distribution and detect outliers. Scale reliability was assessed with Cronbach’s alpha for Depression Literacy. However, as its values are quite sensitive to the number of items in the scale, and having two short scales with three items (Misinformation Exposure and Misinformation Agreement), according to (59), we report the inter-item correlation range for the scales. (60) recommends an optimal range for the inter-item correlation of.15 to .50. A collinearity analysis was carried out to check the prerequisites for a regression analysis, which showed no signs of collinearity (tolerance factor > .10 and variance inflation factor < 10) (61). No multivariate outliers were found according to Mahlanobis distance. SPSS 28.0 was used in our study for statistical analyses. The significance level was set at 0.05. Before running the model, we also controlled for differences in the levels of Type of OCMHs participation using MANOVA with Bonferroni post hoc tests. Then, we used PROCESS Model 3 (62) with 95% bias-corrected bootstrap confidence intervals (CIs, with 10,000 bootstrap samples) to test the model. Following suggestions outlined in Hayes (63), mean centering was conducted on variables that defined products.

The effects of gender, age, and frequency were controlled by including them as covariates in the moderation analysis in the PROCESS macro. We controlled for the frequency of participation because Misinformation Agreements might be impacted by the amount of time they spend on OCMHs platforms (64).

Continuous variables were standardized before conducting the model. As the variable Type of OCMHs participation had three levels (peer-led, mixed, expert-led), we used the multi-categorical moderator function in PROCESS. To represent a multi-categorical moderator with k groups, PROCESS creates k − 1 variables and adds them to the model, in addition to k − 1 products representing the interaction (65). Thus, two dummy variables were created to represent the Type of OCMHs participation and two interaction terms to represent the interaction between the Type of OCMHs participation, Depression Literacy, and Misinformation Exposure.

Preliminary results

Survey data were obtained from 403 participants. The sample consisted of 333 (82.6%) females, 67 males (16.6%), and 3 participants (0.7%) who responded with others. Participants were members of either a peer-led (n = 126, 31.3%), expert-led (n = 213, 52.9%), or both peer-led and expert-led OCMHs (‘mixed’ category, n = 64; 15,9%). The descriptive statistics, reliability coefficients, and Pearson correlations for the study variables are presented in Table 1.

Table 1

Descriptive statistics and correlations among variables in the whole sample (N = 403)
Variables	M (SD)	Skewness	Kurtosis	α/IIC	Age	Gender	Frequency	Misinformation Exposure	Misinformation Agreement
Age (18–76)	42.35 (11.99)	0.13	-0.73	NA	-
Gender	0.17 (0.37)	NA	NA	NA	0.145**	-
Frequency	3.16 (0.96)	-0.83	− .446	NA	.026	.029	-
Misinformation Exposure	2.46 (0.78)	.093	-0.173	0.18 - 0.42	0.068	-0.017	.128*	-
Misinformation Agreement	2.32 (1.17)	1.03	0.71	0.20–0.33	.001	0.109*	-0.038	0.157**	-
Depression Literacy	14.28 (3.58)	-0.49	0.26	0.74	0.010	-0.033	0.105*	0.118*	-0.158**
Note. ∗p < .05; ∗∗p < .01. Gender was dummy coded (male = 1; non-male = 0).

Participants were members of 64 groups, of which only five were moderated by health professionals (three psychotherapists, one psychologist, one nurse). The MANOVA results indicated a statistically significant difference in the Volume of Misinformation exposure based on the Type of OCMHs participation, only between the levels peer-mixed and peer-expert, F = 6.281, P < 0.01 partial η² = .30 (see Additional File 2). No differences were found in Misbeliefs or Depression literacy levels.

Main Results

We thus assessed a moderated-moderation model in which the moderation by Depression Literacy of the Misinformation Exposure effect is moderated by the Type of OCMHs participation. A significant model (F(14,387) = 3.448, p < 0.001), explaining about 12% of the variance of the dependent variable, showed that higher Misinformation Exposure predicted a larger Misinformation Agreement (β = .3221, t = 3.8808, p < 0.001). As shown in the table (Table 2), Misinformation Exposure × Depression Literacy (β = − .2179, t = -3.2217, p < 0.01) and Misinformation Exposure x Type of OCMHs participation (contrast: peer-expert; β = − .2322, t = -2.0741, p < 0.05) interactions had a significant effect. The three-way interaction between Misinformation Exposure, Depression Literacy, and Type of OCMHs participation in Misinformation Agreement was also significant (contrast: peer-expert; β = .2497, p < .05; ΔR² = 0.02; F(2,387) = 3.8037, p < .05). The bootstrap CIs indicated significant effects (p < 0.001) for 2(of 3) levels of Depression Literacy: Low (β = .5338) and Medium (β = .3182), with an effect significant only in peer-led communities. See Fig. 2.

Table 2

Coefficients and 95% confidence intervals of moderated-moderation for predicting Misinformation Agreement
			β	SE	t	P
Constant			.1529	.1005	1.6892	.0920
	Focal antecedent: Misinformation Exposure		.3221*	− .0729	3.8808	༜.001
	Depression Literacy		− .2701*	.0818	-3.2933	༜.001
	Depression Literacy x Misinformation Exposure		− .2179**	.0508	-3.2217	༜.01
	Type of OCMHs participation (D1)		− .0174	.1584	− .1529	.8786
	Type of OCMHs participation (D2)		− .2322*	.1179	-2.0741	༜.05
	Type of OCMHs participation (D1) x Misinformation Exposure		− .1031	.1667	− .6224	.5340
	Type of OCMHs participation (D2) x Misinformation Exposure		− .2451*	.1063	-2.2436	༜.05
	Type of OCMHs participation (D1) x Depression Literacy x Misinformation Exposure		.2711	.1526	1.9528	.0516
	Type of OCMHs participation (D2) x Depression Literacy x Misinformation Exposure		.2497*	.0861	2.4578	༜.05
Covariates
	Gender		.0896*	.0483	1.8802	.0608
	Age		− .0292	.0514	− .6278	.5305
	Frequency of participation in OCMHs		− .0571	.0509	-1.1650	.2447
	Test of conditional “Depression Literacy × Type of OCMHs participation” interaction at values of Misinformation Exposure
	Depression literacy	Type of OCMHs participation	β	SE	LLCI	ULCI
	-1 SD	Peer-led	.5338***	.1024	.3324	.7351
	-1 SD	Mixed-led	.1699	.1670	− .1653	.4983
	-1 SD	Expert-led	.0411	.1050	− .1653	.2474
	Mean Depression Literacy	Peer-led	.3182***	.0820	.1570	.4794
	Mean Depression Literacy	Mixed-led	.2254	.1243	− .0189	.4697
	Mean Depression Literacy	Expert-led	.0758	.0705	− .0627	.2144
	+ 1 SD	Peer-led	.1026	.1092	− .1121	.3172
	+ 1 SD	Mixed-led	.2809	.1814	− .0758	.6376
	+ 1 SD	Expert-led	.1030	.1030	− .0919	.3131
	Note. ∗ ∗∗∗p < .001. Gender was dummy coded (male = 1; non-male = 0).
	D1 coding: peer-led OCMHs = 0, mixed OCMHs = 1, expert-led OCMHs = 0. D2 coding: peer-led OCMHs = 0, mixed OCMHs = 0, expert-led OCMHs = 1.

The present study provides compelling evidence that individuals, when repeatedly exposed to misinformation, are more likely to agree with it (H1), which aligns with the theoretical framework of the Illusory truth effect (53). This first result is concerning as members look for information on online communities to make important health decisions (66). However, the study also found that individuals with higher levels of depression literacy were better equipped to resist mental health-related misinformation (H2), consistent with prior research (44–46). It is important to note that there were no significant differences in Depression Literacy or misinformation agreements among participants in the three types of OCMHs studied (peer, mixed, and expert). In other words, although participating in peer-led OCMHs augmented the chances of encountering a higher volume of misinformation exposure, on average, participants in expert-led OCMHs were neither more literate nor more prone to agree to misbeliefs. Therefore, we can conclude that depression literacy is an individual difference that impacts the probability of agreeing (or not) with the misinformation to which people are exposed, highlighting the importance of health literacy education to mitigate the effects of misinformation exposure.

The second buffer: the type of OCMHs participation was not only a moderator of the association Misinformation Exposure-Misinformation Agreement (H3), with participants in expert-led OCMHs having lower chances of agreeing to misinformation, but also a moderator of the moderation between depression literacy and Misinformation Exposure on Misinformation Agreement (RQ). Specifically, members of peer-led OCMH groups with higher levels of Depression Literacy were almost as susceptible to misinformation as those in expert-led OCMH groups. Conversely, those with lower levels of Depression Literacy and lacking the external help of experts in peer-led OCMH groups had the worst outcomes. Future studies should especially address these vulnerable members of OCMHs, which we were able to identify by combining the analysis of internal (literacy) and external resources (Type of participation).

The findings shed light on the efficacy of expert-led OCMHs, highlighting the importance of expert sources to correct health misinformation in the social media (67). Healthcare professionals, in particular, play a critical role in countering misinformation, as suggested by Schulz & Nakamoto (2022), and in line with previous studies, are integral to the success of online communities as they are equipped with the required professional health knowledge to offer trustworthy advice on the causes, prevention, and treatment of disease (69).

One possible strategy to diminish misinformation exposure is to encourage health professionals to administer OCMHs to ensure accurate and trustworthy information and explore possible incentives to promote their involvement or tools that might contribute to their role in the communities, such as automated moderation. Another avenue for future research is to examine the factors that influence individuals to participate in either peer-led or expert-led communities and the perceived differences between these types of communities. Furthermore, understanding the mechanisms behind the success of expert-led OCMHs is also beneficial to improving the quality of peer-led communities and better regulating them.

Furthermore, as suggested by (25)25), through their participation in OCMHs, health professionals might also inform their professional practice about the misinformation and ambiguous discourses regarding symptoms or treatments. To conclude, while they represented a minority compared to other participants, future studies should better explore members' participation patterns in both peer-led and expert-led communities (‘mixed’ category). This will provide a more thorough understanding of their experiences and the potential risks they may face.

Limitations And Future Directions

While our study offers valuable insights into the relationship between exposure to misinformation and mental health misbeliefs, several limitations need to be considered. Firstly, given the cross-sectional nature of the employed data, causative associations between exposure to misinformation and mental health misbeliefs could only be speculated here. Future longitudinal studies are needed to address this. Secondly, our study used a self-selection sampling method, limiting our results' generalizability to larger populations. Future studies should use probability sampling methods to address this limitation. Additionally, our study only measured the volume of misinformation exposure and did not account for the amount of misinformation correction. This is an important variable that should be included in future studies to fully understand the impact of misinformation exposure on mental health misbeliefs.

Furthermore, our use of a depression literacy scale only measured declarative knowledge and may not have captured the full complexity of the literacy construct. Future studies should complement this by investigating the role of critical skills. Another limitation is the potential for recall bias as our measures of misinformation exposure were based on self-reported past experiences. While the specific nature of the questions minimized this bias, future studies should consider using more objective measures of exposure to misinformation. Lastly, we recognize that there might be heterogeneity in the moderation of OCMHs, even among experts. Future studies should explore the quality (debunking, scientific dissemination, censuring) and quantity of moderation in OCMHs as a continuum rather than a dichotomy.

The study examined responses from 403 members of Facebook OCMHs and found that the vaster the quantity of misinformation regarding mental health the people were exposed to, the more they were likely to believe in it. Therefore, efforts to reduce exposure to health misinformation and interventions to reduce its impact are necessary. Depression literacy was found to have a mitigating effect, suggesting that improving depression literacy could serve as a public health goal to counter the negative impact of misinformation on mental health outcomes. Moreover, the study highlighted the potential role of the type of participation in OCMHs (expert- vs. peer-led) in reducing misbeliefs. It emphasized the importance of health professionals in OCMHs and a resource to incentivize. Findings have significant implications for understanding the complex interplay between the variables investigated and informing interventions and policies to reduce misinformation's negative impact on mental health, especially for the most vulnerable members identified.

OCMH

Online community for mental health

Ethics approval and consent to participate

The Ethics Committee of Università della Svizzera italiana approved the study design (CE_2021_10). All methods were performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all subjects.

Availability of data and materials

The datasets used and analysed during the current study available from the corresponding author on reasonable request.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Funding

The Swiss National Science Foundation supported this work under grant 200396.

Authors' contributions

N.B. wrote the main manuscript text. All authors reviewed the manuscript.

Acknowledgments

The authors wish to thank the admins of the OCMHs for their invaluable support of this study.

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Buffering Against Exposure to Mental Health Misinformation in Online Communities: The Interplay of Depression Literacy and Expert Moderation

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Misinformation and online mental health communities

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Misinformation Exposure

Misinformation Agreement

Depression Literacy Questionnaire

Type Of Ocmhs Participation

Covariates

Data Analyses

Results

Preliminary results

Main Results

Discussion

Limitations And Future Directions

Conclusion

Abbreviations

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