Evidence-Based Alignment of Reddit Peer Advice for Depression and Anxiety: Qualitative Content Analysis

Introduction

People increasingly turn to digital platforms to understand and manage their health concerns, especially when they face uncertainty or limited access to professional care [1]. Online environments now function as informal “health-information ecosystems,” where individuals seek peer discussions, shared experiences, and community-generated advice. Global evidence shows rapid growth in online health-information seeking, driven by convenience, anonymity, and real-time access to user-generated content. These platforms support both symptom interpretation and emotional reassurance, making them central to contemporary health-seeking behavior [2]. However, the extent to which such peer-generated advice aligns with evidence-based recommendations remains an important public-health concern.

Mental health topics, particularly depression and anxiety, feature prominently across online forums, reflecting the high global prevalence of these conditions. Depression and anxiety are among the most common mental health conditions worldwide, affecting an estimated 5.7% and 4.7% of the global adult population [3]. Online platforms allow users to share symptoms, articulate distress, and seek reassurance from others with similar experiences. The anonymity and low barrier to participation enable open disclosure of emotional difficulties, while peer responses often blend empathy, experiential narratives, and diverse forms of lay advice [4]. Increasingly, individuals disclose psychological distress online long before seeking formal support.

Among online platforms, Reddit is the largest forum, attracting over 1 billion users worldwide in 2025 [5]. Research shows that Reddit users frequently express anxiety, loneliness, and uncertainty through highly personal narratives that incorporate slang, metaphor, and nonstandard language, making posts difficult to interpret without structured methods [6]. Evaluating how these peers’ interpretations compare with evidence-based guidance is therefore essential to prevent the spread of misinformation or misleading of the users on such a sensitive topic.

Beyond individual posts, online mental-health communities exhibit dynamic patterns of interaction. Studies mapping behavior in depression-related subreddits show that users’ emotional trajectories fluctuate in response to the tone and content of peer exchanges, rather than following a linear pathway of deterioration or recovery [7]. Such exchanges can reinforce or alleviate symptoms depending on the nature of the advice and support offered. These patterns likely extend to anxiety, where individuals rely on Reddit to interpret symptoms, trial coping strategies, and evaluate the experiences of others, highlighting the importance of determining whether community-endorsed advice reflects or diverges from evidence-based best practice.

Despite their popularity, mental health forums are not formally regulated. Anxiety disorders affect an estimated 301 million people globally [8]. Depressive disorders also account for a substantial global burden; more than 332 million people worldwide suffer from depression, which contributes significantly to disability-adjusted life years, with many individuals experiencing long delays before receiving appropriate assessment or treatment [9]. Many people first consult digital communities before engaging with mental-health professionals, relying on Reddit as an informal source of guidance [10].

Because early interpretations of symptoms strongly shape help-seeking behavior, treatment choices, and the progression of distress, assessing the accuracy and relevance of community-endorsed, evidence-based guidance is essential for public-health literacy.

Existing research on online mental health content has largely taken 2 directions. First, most computational studies use Reddit posts to identify or classify depression or anxiety-related posts, often focusing on detecting mental health states or linguistic markers rather than evaluating the appropriateness or accuracy of the advice based on evidence [11]. Second, research evaluating mental health information on websites and video platforms demonstrates variability in quality, frequent omissions of guideline-concordant recommendations, and inconsistent sourcing [12,13]. However, these studies assess static, professionally produced content rather than interactive peer-support environments, and they rarely examine anxiety-specific or depression-specific lay advice.

Online mental health advice can also be understood through theories of peer support and information behavior. Social support theory distinguishes informational support, such as advice and service recommendations, from emotional support, including empathy, reassurance, and validation. The information adoption model proposes that individuals are more likely to accept information when they perceive it as useful and credible, while social cognitive theory highlights the roles of observational learning, social reinforcement, and self-efficacy in shaping health-related behavior. Together, these perspectives offer a conceptual basis for examining not only whether peer advice aligns with clinical guidance but also how online communities may collectively generate, evaluate, and amplify forms of mental health advice [14,15].

To date, there is minimal evidence on whether the concrete self-management and help-seeking recommendations shared in Reddit’s mental-health discussions align with evidence-based guidelines. The aim of this study was to systematically compare highly endorsed Reddit advice for depression and anxiety, drawn from top-ranked comments, with evidence-based clinical guidance to identify areas of concordance and divergence between community-generated advice and guideline-based recommendations.

Methods

Study Design

This study used a cross-sectional qualitative content-analysis design, supplemented by quantitative descriptive summaries to characterize patterns in Reddit discussions about depression and anxiety. We analyzed publicly available posts and their top-voted comments from the r/mentalhealth subreddit to examine two key questions: (1) what kinds of advice or expressions users provide and (2) to what extent action-based guidance aligns with evidence-based recommendations.

The study proceeded in 3 stages: automated collection and filtering of relevant Reddit posts; classification of posts into action-based or nonaction-based advice; and thematic categorization and guideline comparison.

This design enabled the systematic description of online mental-health guidance at a scale while retaining interpretive depth consistent with qualitative content analysis. This study adopted a postpositivist and pragmatic orientation. Publicly available Reddit posts and comments were treated as naturally occurring text data and analyzed through a deductive and inductive direct content analysis approach.

Data Collection

To collect data, we used Reddit’s API via the Python Reddit API Wrapper (PRAW) library. Using this script, we searched r/mentalhealth for posts containing keywords related to “depression” or “anxiety.” r/mentalhealth is a large, general-purpose peer-support subreddit open to any registered Reddit user. It is moderated by volunteer community moderators rather than mental-health professionals, and posts and comments are not clinically reviewed prior to publication. This context is relevant to interpreting both the content and community-ranking dynamics of the advice examined.

To comply with Reddit data collection policy, data were collected in weekly batches over the period from Aug 13, 2025, to Sep 14, 2025. In each weekly session, we retrieved up to 500 new posts (Reddit’s maximum for a single query) that contained the related terms in the post.

Posts were fetched and sorted by recency (newest first) to capture the latest discussions each week. This ensured coverage of ongoing conversations while controlling the volume per batch and avoiding API rate limits or data overload.

To refine the search results to posts truly relevant to depression or anxiety (and not just containing the words in passing), we used a lexicon filtering step. We defined 2 sets of indicator terms (lexicons) for each mental health topic, drawing on clinical terminology and colloquial language commonly associated with depression and anxiety [16,17]. The lexicons included terms as shown in Table 1.

A post was categorized as depression-related if its text contained any one of the depression lexicon terms and similarly as anxiety-related if it contained any term from the anxiety lexicon. Many posts contained both sets of terms and were accordingly tagged in both categories (eg, a post mentioning feeling “depressed and anxious” would be labeled with both flags). Posts that did not contain any of the lexicon terms (for either category) were excluded following lexicon-based filtering.

For each post that passed the filtering process described above, we extracted the single highest-ranked visible comment at the time of data collection. Using PRAW, comments were ordered according to Reddit’s “top” sorting method, which prioritizes comments based primarily on their voting score and relative community engagement. The highest-ranked comment was then selected as the response with the greatest apparent community endorsement and prominence within the discussion.

Where the highest-ranked comment had been deleted or removed, the extraction script automatically selected the next-highest-ranked visible comment. Only 1 comment was collected per post, meaning complete comment threads and replies were not included. This produced a one-to-one pairing between each eligible post and a single representative comment. Posts with no comments, or no remaining visible comments, were excluded from the final analytic sample.

This sampling decision reflected the study’s specific aim of evaluating the advice that users were most likely to encounter when first opening a discussion, rather than conducting an exhaustive analysis of the entire comment thread. Because highly ranked comments are displayed more prominently and generally indicate stronger relative community endorsement than comments appearing lower in the discussion, they may disproportionately shape readers’ initial perceptions of what constitutes relevant, acceptable, or useful advice. Accordingly, the analysis focused on the top-ranked comment associated with each post.

Across weekly collection cycles, datasets were accumulated and subsequently aggregated at the conclusion of the data collection period to form the final analytic dataset. This dataset serves as the basis for subsequent analysis.

Table 2 provides an overview of the volume of data collected for each search term over the study period. In total, we gathered 1065 unique posts containing the target keywords, with 348 posts containing both depression- and anxiety-related keywords.

Data Analysis

Data analysis was conducted in three sequential stages: (1) binary content categorization, (2) thematic categorization of action-based advice, and (3) assessment of alignment with evidence-based guidelines (Figure 1).

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Binary Classification of Reddit Posts

All collected posts were first classified into 1 of 2 categories: (1) action-based advice (posts offering concrete suggestions or directives such as recommending medication, lifestyle strategies, or coping behaviors) and (2) nonaction-based expression (posts focused on emotional sharing, empathy, or narrative reflection without proposing specific actions). Comments containing both emotional support and actionable advice were classified as action-based advice if they included at least one clear recommendation, directive, or suggested course of action. Comments that contained only empathy, reassurance, emotional validation, or personal reflection without a specific recommendation were classified as nonaction-based expressions. A subset of 25 records from the depression-related subset and 25 records from the anxiety-related subset were randomly selected and independently classified by 2 researchers (EA and PB). The researchers agreed on the classifications of all 50 records, corresponding to 100% agreement and a Cohen κ of 1.00. The 50 human-labeled records were then embedded within a structured prompt as examples of the 2 categories.

The finalized prompt instructed GPT-3.5 Turbo (OpenAI) to classify each new record as either action-based advice or nonaction-based expression [18]. It contained the category definitions, classification instructions, required output labels, and the 50 human-coded exemplars. The prompt also specified the decision rules for classification and instructed the model to return a single category label for each record. Inference was conducted with temperature set to 0 to maximize consistency and minimize variation across repeated classifications.

GPT-3.5 Turbo was applied to the full dataset to generate a preliminary classification for each record. Classifier performance was evaluated against human-consensus classifications using a separate random validation sample of 100 records. The model demonstrated high accuracy, precision, recall, F₁-score, and agreement with human coding across the evaluation measures presented in Table 3.

The model-generated classifications were treated as preliminary rather than final decisions. All records classified by GPT-3.5 Turbo as action-based advice were subsequently reviewed by the research team. Ambiguous, borderline, or inaccurate classifications were discussed by 2 researchers and corrected through consensus. The model therefore supported the initial screening process, while final decisions regarding inclusion in the action-based advice dataset remained under human researcher control. Following this AI-assisted classification and manual verification process, 327 anxiety-related and 243 depression-related records were retained as containing action-based advice.

Manual Checking and Thematic Categorization of Action-Based Advice

Posts classified as action-based advice underwent further qualitative content analysis to identify the specific types of actions being recommended. Using a multistep approach, we first selected a random sample of 100 action-based posts for inductive open coding. Two researchers (PB and YT) independently reviewed this subset and generated preliminary codes reflecting the nature of the advice (eg, medication use, lifestyle modification, therapy seeking, maladaptive coping, and peer-support strategies).

Through iterative comparison, constant comparison, and discussion, the 2 sets of preliminary codes were merged, refined, and organized into a stable set of higher-order thematic subcategories, consistent with accepted methods for developing and consolidating thematic frameworks [19]. This coding framework was then applied to the full action-based dataset. GPT-generated outputs were used only as a first-pass grouping tool, after which all assigned subcategories were manually reviewed for accuracy, coherence, and conceptual fit.

To assess the reliability of subcategory coding beyond the initial 50-post training set, a random 10% subsample of the action-based dataset (n=57) was independently double-coded by 2 researchers against the finalized 26-subcategory framework, yielding 91% agreement (Cohen κ=0.87). Discrepancies were resolved through discussion and, where necessary, consultation with a third team member. The finalized codebook was then applied to the remaining dataset by consensus-trained coders.

Frequency counts were calculated across subcategories to describe the distribution of advice types in depression-related posts, anxiety-related posts, and posts referencing both conditions. These descriptive summaries supported the interpretation of thematic patterns within the dataset.

Evidence-Based Alignment Assessment

To evaluate whether the action-based advice aligned with evidence-based mental-health practices, each subcategory was mapped against recommendations from established clinical guidelines, including the National Institute for Health and Care Excellence (NICE), the Australian Psychological Society (APS), Beyond Blue, and the World Health Organization Mental Health Gap Action Programme (WHO mhGAP) Intervention Guide. For each subcategory, alignment was coded as aligned, partially aligned, or nonaligned based on concordance with recommended clinical strategies for depression and anxiety.

We combined international guidelines (eg, WHO mhGAP and NICE) with Australian frameworks (eg, APS, RANZCP, and Beyond Blue) because Australian guidance is largely built on international recommendations but adds local service-pathway detail. Advice was coded as aligned when consistent with both, partially aligned when consistent with only one, and nonaligned when consistent with neither. We did not find direct contradictions between the 2 guideline sets for the subcategories examined; differences were mostly about level of detail, not conflicting advice. Guideline alignment assessments were independently reviewed by 2 members of the research team. Where disagreements or borderline cases arose, classifications were discussed and resolved through consensus, with consultation from a third team member when required. Formal interrater reliability statistics were not calculated for the guideline-alignment stage.

Ethical Considerations

This study analyzed publicly available, anonymized user-generated content from the Reddit subreddit r/mentalhealth. Ethical approval for the project was granted by the Flinders University Human Research Ethics Committee, Australia (Project ID: 8797), which approved the use of publicly accessible online data and issued a waiver of consent. Approval was provided on August 7, 2025, and remains valid until August 7, 2030, subject to standard annual reporting. All procedures complied with the National Statement on Ethical Conduct in Human Research [20], Reddit’s Terms of Service, and conditions outlined by the Committee, with no attempts made to identify individual users.

Collected posts and comments were stored on a secure, access-controlled institutional server accessible only to the research team. Reddit usernames, profile links, and other directly identifying metadata were removed prior to analysis and are not reported in this manuscript. Where illustrative excerpts are presented in the Results (Table 4), each entry is a paraphrased summary that may include a single short (under 15-word) direct quotation from the original comment. Examples were selected to exclude posts from self-identified minors and to avoid reproducing detailed crisis narratives, to reduce the risk of reidentification.

Researcher Characteristics and Reflexivity

The coding team brought complementary backgrounds: PB in public health and digital health systems, EA in computational text analysis and NLP, and YT in psychology, including prior clinical experience as a counseling psychologist. This mix supported both the technical side of the large language model-assisted classification and the clinical judgment needed to assess guideline alignment. None of the coders had any prior relationship with r/mentalhealth or its users, and their interaction with the platform was limited to data extraction. To reduce the influence of individual assumptions, initial coding was done independently by 2 researchers before a consensus discussion, with disagreements resolved through discussion and, where needed, by a third team member. We recognize that YT clinical training likely shaped judgments of guideline alignment more than the other coders’ nonclinical backgrounds. This is discussed further in the Limitations section.

Results

Distribution of Action-Based Advice Categories

The model classified 327 anxiety-related and 243 depression-related action-based Reddit posts into six overarching categories: (1) seeking professional medical help, (2) behavioral and lifestyle changes, (3) emotional regulation and cognitive restructuring, (4) interpersonal and social dynamics, (5) self-prioritization and life direction, and (6) crisis and immediate safety.

The proportional distribution of these categories for anxiety-related and depression-related posts is presented in Tables 5 and 6.

Across both conditions, seeking professional medical help emerged as the most common form of community-endorsed advice, representing 33% of anxiety posts and 34% of depression posts. The ranking of subsequent categories differed between the 2 datasets.

Among anxiety-related posts, emotional regulation and cognitive restructuring was the second most frequent category, followed by interpersonal and social dynamics. In contrast, among depression-related posts, behavioral and lifestyle changes formed the second most common category, with emotional regulation and cognitive restructuring ranking third.

Across the full dataset, thematic analysis identified 26 subcategories nested within the 6 major categories, capturing the diversity of advice types offered by Reddit users, with corresponding international and Australian clinical guidelines summarized in Table 7.

Alignment With Global and Australian Intervention Guidelines

The alignment between each action-based advice subcategory and established clinical guidelines is summarized in Table 7. Most subcategories corresponded to recommendations found across multiple global and Australian intervention frameworks, including evidence-based guidance for depression and anxiety management. This mapping illustrates the extent to which community-generated advice on Reddit reflects (or diverges from) recognized best-practice standards. To illustrate the nature of advice captured within each category, representative examples of top-ranked comments for each major category and condition are provided in Table 4.

Discussion

Principal Findings

This study evaluated whether community-endorsed mental health advice shared on Reddit aligns with evidence-based guidance for depression and anxiety. By analyzing 1065 posts from r/mentalhealth and their top-ranked comments, the findings indicate that a substantial proportion of action-based advice reflects recognized clinical strategies, particularly in relation to professional help-seeking, behavioral and lifestyle modifications, and emotional regulation techniques. At the same time, variability in alignment across advice categories highlights both the supportive potential and inherent limitations of informal, peer-driven mental-health guidance.

Beyond documenting guideline alignment, these findings describe patterns in the types of mental health information that become highly visible and community endorsed within an online peer-support setting. The observed prominence of professional help-seeking, behavioral strategies, emotional regulation, and social support is consistent with highly endorsed Reddit responses combining experiential knowledge, emotional reassurance, behavioral modeling, and informal guidance [21]. However, the study did not measure users’ credibility assessments, information-adoption processes, observational learning, reinforcement, or self-efficacy. The theoretical perspectives discussed below are therefore offered as possible interpretive lenses for the observed content patterns, not as mechanisms demonstrated by the findings.

These findings contribute to a broader body of work examining the quality and safety of digital mental health information provided outside formal clinical settings. While evidence from Bowie-Dabreo et al [22] indicates that many mental-health apps incorporate evidence-informed treatment components, important gaps remain in clinical validity, treatment fidelity, and safety. The authors emphasize the need for developers to consult relevant clinical guidelines and standards and to engage in ongoing critical reflection regarding treatment design and risk mitigation to improve intervention quality [22].

Similarly, a comprehensive scoping review of Reddit-based studies on depression and anxiety found that most prior research prioritized predictive classification, linguistic markers of distress, or professionally oriented monitoring and outreach applications, with limited attention to the quality or guideline alignment of peer-generated advice [11]. By contrast, the present study directly evaluates highly endorsed recommendations contained within top-ranked comments against established International and Australian guidelines, thereby addressing a gap explicitly identified in the existing literature.

Social support theory offers 1 possible interpretive lens for the predominance of guideline-consistent advice. Online peer-support communities can provide several forms of support, including informational support through practical recommendations and referrals, emotional support through empathy and reassurance, appraisal support through validation and feedback, and companionship through shared experience. In the present study, recommendations to seek professional care, adopt behavioral strategies, regulate emotions, and draw on social relationships are consistent with these overlapping support functions. Lived experience may also make such advice more relatable and acceptable by translating formal mental health principles into language grounded in everyday experience; however, perceived relatability and acceptability were not directly assessed in this study.

The findings may also be interpreted through the information adoption model [23], which proposes that information uptake is shaped by perceptions of information quality, usefulness, and source credibility. Although Reddit users may not possess formal clinical authority, the platform’s voting and ranking mechanisms provide visible signals of collective endorsement. One possible interpretation is that highly ranked comments could be perceived as more credible, useful, or socially validated than less visible responses. Reddit’s ranking system should not be understood as a guarantee of clinical accuracy. It could be hypothesized to function as an informal community-level filtering process through which advice regarded by users as constructive, relevant, or acceptable becomes more visible. Nevertheless, the present study did not measure readers’ perceptions of credibility or usefulness, information adoption, or the reasons comments received upvotes. The information adoption model therefore provides a plausible interpretation of the observed pattern that requires direct testing, rather than an explanation established by these data.

Social cognitive theory provides another possible interpretation of how guideline-consistent advice might be socially reinforced within online communities. Descriptions of coping strategies, professional help-seeking, emotional regulation, and behavioral change could function as models for possible action. Similarly, upvotes, supportive replies, visibility, and community recognition could act as forms of social reinforcement, while narratives of successful coping might support readers’ perceived self-efficacy. Contributors might also be motivated by altruism, recognition, and feedback to provide advice they regard as useful [24]. These propositions are theoretically consistent with social cognitive self-efficacy, but observational learning, reinforcement, self-efficiency, and contributor motivations were not measured. They should therefore be treated as hypotheses for future research rather than mechanisms demonstrated by the present findings. Taken together, these perspectives raise the possibility that the observed guideline alignment reflects more than individual users independently reproducing clinical information. The pattern may be interpreted as consistent with a collective process involving experiential knowledge, public mental health messages, prior encounters with health care services, and platform-based endorsement. This interpretation constitutes a theoretical proposition arising from the study rather than an empirically tested account of how particular comments became prominent. Future studies should directly examine whether perceived support, credibility, visibility, and reinforcement predict readers’ evaluations, information adoption, or behavioral responses.

Comparative analysis of anxiety-related and depression-related posts further revealed condition-specific differences in the types of advice endorsed by the community. While professional help-seeking emerged as the dominant category across both conditions, anxiety-related discussions more frequently emphasized emotional regulation and cognitive strategies, whereas depression-related discussions showed a stronger focus on behavioral activation and lifestyle changes. These patterns are consistent with the differing symptom profiles and lived experiences associated with anxiety and depressive disorders and mirror distinctions commonly reflected in clinical guidance. One possible interpretation is that the observed alignment reflects broader dissemination of mental health concepts through public-health campaigns, prior clinical encounters, or widely available self-help resources. The study did not examine where contributors acquired this knowledge, so this interpretation remains to be tested.

Mapping advice subcategories to International and Australian clinical guidelines demonstrated substantial overlap, particularly given that many Australian frameworks are closely aligned with global recommendations. This convergence supports the robustness of the observed alignment and indicates that community-endorsed advice often reflects widely accepted best-practice principles rather than idiosyncratic or locally specific norms. Nevertheless, partial or nonaligned advice was observed in more complex domains, including crisis situations, interpersonal conflict, and deeply individualized coping strategies. In these cases, advice was sometimes overly general, insufficiently contextualized, or lacking appropriate safety framing, underscoring the risks of oversimplification in informal peer-support environments [16].

The findings have several practical implications. While not all content posted on Reddit can be assumed to be reliable or clinically appropriate, the results suggest that highly ranked comments, those most visible and most likely to be encountered by users, frequently align with evidence-based recommendations and may offer meaningful support to individuals experiencing depression or anxiety. These findings suggest that highly rated comments may represent a useful area for future investigation regarding the quality of online mental health advice. From a public-health perspective, this underscores the value of understanding platform-specific information hierarchies rather than treating online mental-health content as uniformly unreliable. Sharing these insights through public-facing summaries, educational resources, and collaboration with online communities could support safer help-seeking and contribute to improved mental health and well-being in the wider community. However, further research is needed to determine whether such approaches influence help-seeking behavior or mental health outcomes.

Beyond these implications, the findings suggest a potential contribution of lived experience within online mental health support spaces. Many highly ranked comments drew on personal experience to offer empathetic, practical guidance that reinforced or contextualized evidence-based recommendations. Because the study did not assess readers’ responses or formally evaluate lived-experience roles, it cannot establish whether these contributions affected perceived credibility, support, or behavior. The observed pattern nevertheless identifies lived-experience contributions in informal mental health settings as an important area for future research, including how such contributions might be appropriately supported, integrated, and aligned with clinical guidance.

This study has several notable strengths. It used a highly efficient and scalable content-analysis approach by combining large language model–assisted classification with comprehensive manual human verification, enabling systematic analysis of a large dataset while maintaining interpretive accuracy. The focus on top-ranked comments enhances ecological validity by analyzing advice most likely to influence readers, and the use of multiple international and Australian clinical guidelines strengthens the credibility of the alignment assessment.

Limitations

Several limitations should also be acknowledged. Data collection was limited to approximately 1 month, which may constrain the breadth of perspectives captured and limit generalizability across time or changing community dynamics. The analysis was restricted to a single subreddit and English-language content, and only one comment per post was examined, which does not capture the full diversity of viewpoints within discussion threads. In addition, the lexicon-based filtering strategy may have excluded relevant posts describing symptoms or experiences of depression and anxiety without using predefined keywords. Consequently, some relevant discussions may not have been captured in the final dataset. Reddit users are also not representative of the general population. Compared with the broader internet-using public, they skew younger and more male, and subreddit-specific norms and moderation practices shape the tone and content of the advice exchanged [25]. These characteristics may limit the generalizability of the findings to other online communities or platforms with different demographic compositions and cultures. In addition, it was not possible to determine whether individuals offering advice were qualified mental health professionals, drawing on formal training, lived experience, or a combination of both. Nonetheless, these design choices are consistent with the study’s aim of evaluating community-endorsed advice and providing a focused assessment of the most visible and relatively community-endorsed guidance.

Furthermore, reliability for the thematic subcategory coding was checked on a subsample rather than the full dataset. Applying full-dataset, multirater coding in future work would help test how much this influenced the results. In addition, although interrater reliability was assessed for the thematic subcategory coding, formal reliability statistics were not calculated for the guideline-alignment assessments. These assessments were independently reviewed by 2 researchers and disagreements were resolved through consensus. Future studies could strengthen this stage by incorporating formal reliability testing of guideline-alignment judgments.

In addition, the analysis was limited to the single highest-ranked visible comment for each post. However, the highest-ranked comment should be interpreted as the most visible and relatively community-endorsed response at the time of data collection, rather than as evidence of unanimous community consensus or the overall quality of the discussion. Accordingly, the findings should be interpreted as reflecting highly endorsed advice rather than the full spectrum of advice exchanged within Reddit discussions. Future research should examine complete comment threads and changes in comment rankings over time to better understand how competing advice, misinformation, and corrective responses develop within online mental-health discussions.

Finally, the quantitative summaries were intended to describe patterns within the qualitatively coded dataset rather than test prespecified hypotheses. Inferential comparisons across depression- and anxiety-related subsets were not conducted because these groups were not mutually exclusive; posts containing terms relating to both conditions contributed to both subsets. Future research could classify posts into mutually exclusive depression-only, anxiety-only, and co-occurring groups and use prespecified inferential analyses to examine whether advice distributions differ across these categories.

Conclusions

In summary, this study demonstrates that highly endorsed mental health advice on Reddit frequently aligns with evidence-based clinical guidance, particularly regarding professional help-seeking and commonly recommended self-management strategies. Although limitations and risks remain, especially in complex or high-risk contexts, the findings highlight the potential value of online peer-support spaces as complementary sources of encouragement and guidance. Future research should examine longer timeframes, additional platforms, and longitudinal outcomes to better understand how online advice exposure influences help-seeking behavior and mental health trajectories. Given the demographic and cultural specificity of Reddit’s user base, future work should also extend this analysis to platforms with different demographic compositions and community cultures to establish whether the observed patterns of alignment generalize beyond Reddit.