Reddit Depression Communities as Spaces of Emotion Regulation: A Data-Informed Analysis of Coping and Engagement

Abstract

Online social platforms increasingly function as informal self-help environments for individuals experiencing depression, offering spaces for emotional expression and peer support outside traditional clinical settings. However, how coping strategies and psychological engagement states—individuals’ emotional and cognitive involvement in managing their condition—are reflected through online self-disclosure remains poorly understood. We analyzed a large-scale dataset from Reddit depression-related communities to investigate how different psycho-linguistic profiles and coping orientations emerge from users’ language. We collected posts and comments from over 300,000 users across six depression-focused subreddits over two years. User-generated text was characterized through multiple psychological and linguistic dimensions capturing emotions, sentiment, subjectivity, and related features, then aggregated at the user-month level and analyzed using unsupervised clustering techniques. Our analysis identifies four distinct groups characterized by different emotional profiles and dominant coping orientations. These states exhibit meaningful correspondences with established theoretical frameworks, including the Coping Orientations to Problems Experienced model and the Patient Health Engagement model. Our findings demonstrate that large-scale textual data from online communities can provide interpretable insights into coping behaviors and engagement patterns, offering a complementary perspective to traditional approaches for studying mental health.

1. Introduction

Depression is a significant global health concern, affecting approximately 280 million people worldwide and representing one of the leading causes of disability [1]. Its prevalence has been steadily increasing over recent decades, placing substantial burdens on healthcare systems that often struggle to provide adequate support to all individuals in need [2,3]. Alarmingly, only about one in four people suffering from mental health disorders receives appropriate treatment [4], highlighting a major gap in care. This treatment gap underscores an urgent need for innovative approaches to reach and support those affected by depression who may not engage with traditional mental health services.

Traditionally, support for depression involved face-to-face therapy or in-person support groups, where clinicians or peers provided guidance and empathy [4]. However, in recent years, technology [5,6,7] and online social platforms (OSPs) [8,9,10] have emerged as promising avenues of mental health support, transforming the way individuals cope with and discuss depression. OSPs, such as Reddit, host dedicated communities (forums known as subreddits) where users can anonymously share personal experiences, vent their emotions, and seek advice. The anonymity and disinhibition afforded by these platforms encourage open self-disclosure [8,9,11], yielding a “live feed” of how people experience and manage their mental health struggles outside formal clinical settings. Within this context, a growing body of evidence indicates that self-help interventions delivered through digital media can effectively improve mental health outcomes [12]. Taken together, these characteristics suggest that digital support networks might offer unprecedented opportunities to observe coping processes in real time and at scale.

At the same time, the literature has yet to fully capture the psychological impact of online peer interactions. Prior research on social media and mental health has largely focused on detecting depression or characterizing static linguistic markers of distress [8,13], rather than examining how coping behaviors develop and how individuals’ psychological engagement with their mental health condition is reflected across different phases of online participation. In other words, we still know little about whether and how participation in online communities influences individuals’ coping strategies or their trajectories toward recovery. This gap in understanding is partly due to methodological challenges: online data are observational in nature and typically lack formal clinical assessments, making it difficult to reconstruct complex psychological constructs like “coping” and “engagement” from textual traces alone [13].

Effective coping strategies are known to be critical for managing depression and improving well-being. Coping refers to the cognitive, emotional, and behavioral methods individuals use to handle stressors and negative emotions [14]. In the context of depression, adaptive coping strategies—for example, actively seeking social support or reframing thoughts in a more positive light—can help mitigate the impact of depressive symptoms and foster resilience [15]. In contrast, maladaptive strategies such as avoidance, denial, or rumination tend to exacerbate feelings of hopelessness and can hinder recovery [15]. Developing a clearer picture of which coping mechanisms people employ (or fail to employ) is important, as it can inform interventions to promote more adaptive coping.

Over the years, psychologists have proposed various frameworks to categorize and understand coping. A particularly influential framework is the Coping Orientations to Problems Experienced (COPE) inventory by Carver et al. (1989) [16], which enumerates a broad range of coping strategies that individuals use when facing stressful life events. These include strategies like planning and active coping, suppression of competing activities, seeking instrumental or emotional support, venting of emotions, denial, behavioral and mental disengagement, restraint, positive reinterpretation, acceptance, and religious coping. Subsequent work has shown that these specific strategies can be organized into higher-level patterns with shared characteristics [17]. For instance, certain strategies cluster into problem-focused, self-sufficient coping (e.g., planning, active problem-solving), others reflect an avoidant coping pattern (e.g., denial, mental disengagement), some involve seeking social support, and others represent emotion-focused coping aimed at self-comfort (e.g., positive reinterpretation, acceptance). This organization of coping strategies, together with their description and grouping into four broader dimensions, is summarized in Figure 1.

Mental health recovery, however, is rarely a straightforward or linear process, especially for complex conditions like depression. Individuals often fluctuate between emotional highs and lows, making progress and then experiencing setbacks in ways that do not conform to a simple stage-like progression [18]. Our own recent analysis of users in Reddit depression forums observed this phenomenon: instead of following a one-directional path from illness to wellness, users tended to navigate their psychological states in spiral-like trajectories, periodically revisiting states of distress even as they moved toward improvement [19]. This dynamic view of the depression journey resonates with the Patient Health Engagement (PHE) model proposed by Graffigna et al. [20]. The PHE model conceptualizes a patient’s engagement in their health as a progression through sequential but overlapping phases. In the context of mental health, individuals may start in a phase of emotional turmoil and disengagement (feeling overwhelmed or in a “blackout”), then move to a phase of arousal (initial awareness of the problem), progress to adhesion (active but fragile engagement in managing their condition), and eventually reach a phase of eudaimonic project (meaningful, empowered self-management and acceptance of the illness)—all while potentially sliding backwards or looping through earlier stages as their emotional state fluctuates. Crucially, the PHE framework emphasizes that emotional engagement drives this process: it is the evolving emotional readiness of the individual that determines how they advance through or retreat from these stages [21]. A schematic overview of the PHE phases is provided in Figure 2.

In this study, we address the above gaps by combining established psychological frameworks with a large-scale analysis of textual data from Reddit depression communities. This work builds upon our previous study on the same dataset [9], which analyzed the longitudinal dynamics of users’ psychological states and the interactional network structures through which they evolve. While that work addressed the question of how individuals transition between states over time, the present study addresses a complementary and distinct question: what those states are constituted of at the level of language. Specifically, we characterize users’ psychological profiles through a multi-dimensional psycholinguistic feature space, combining discrete emotional categories, continuous affective dimensions, sentiment polarity, subjectivity, and taboo language, focusing on how coping strategies are expressed in language and associated with distinct engagement states. We collect a Reddit dataset spanning multiple depression-focused subreddits over a two-year period, comprising posts and comments authored by over 300,000 users. Rather than relying on clinical labels or predefined survey instruments, our approach leverages naturally occurring language to study how individuals describe their experiences, emotions, and ways of coping within peer-support environments. Coping strategies are operationalized through linguistic proxies extracted from users’ written content. Specifically, we analyze the presence of language associated with different coping orientations—such as planning and active problem solving, help-seeking and social support, emotional reframing, or disengagement and resignation—and map these signals onto the categories defined by the COPE framework. This allows us to characterize how different coping styles are expressed in users’ narratives, while maintaining a clear link to established psychological theory. Building on this representation, we apply unsupervised clustering techniques to group users according to similarities in the content and tone of their posts. The resulting clusters capture distinct linguistic profiles proxying psychological states that differ in emotional valence and prevailing coping strategies, ranging from profiles marked by intense distress and maladaptive coping to states characterized by more constructive, problem-focused or acceptance-oriented approaches. While these states are identified through data-driven methods, they can be meaningfully interpreted through the lens of the Patient Health Engagement model, providing a theoretically grounded reading of how different forms of engagement and emotional readiness are reflected in online self-disclosure.

The rest of the paper is organized as follows. In Section 2, we describe the data collection process and the methodological pipeline adopted in this study. Section 3 presents the results of the unsupervised analysis, describing the psychological and linguistic characteristics of the identified clusters, together with their most salient words and discussion topics. In Section 4, we interpret these findings in light of existing literature and theoretical frameworks, with particular reference to the COPE inventory and the Patient Health Engagement model. Finally, Section 5 concludes the paper by outlining limitations, future research directions, and the practical significance of our work.

2. Materials and Methods

In this section, we describe the methodological pipeline adopted in this study, which consists of three main phases: (i) data collection and preprocessing from Reddit depression communities; (ii) extraction of psycho-linguistic features to characterize users’ emotional and expressive patterns; and (iii) unsupervised clustering and topic modeling to identify distinct groups and their associated thematic content. Our dataset is drawn from Reddit, a social platform organized around topic-specific forums called subreddits, where registered users can publish posts, leave comments, and interact with one another. The platform’s pseudonymous nature encourages candid discussion of sensitive personal matters, which has given rise to a wide range of peer-support communities dedicated to mental health topics. From this ecosystem, we collected data from six subreddits centered on depression over a 24-month window running from May 2018 to May 2020. The selected communities—r/depression, r/depressionregimens, r/depression_help, r/EOOD (Exercise Out Of Depression), r/GFD (Gamers Fighting Depression), and r/sad—were chosen for their direct relevance to depressive experiences, including discussions of treatment options and mutual support dynamics. Data were retrieved via the Pushshift API framework [22], yielding a corpus of 378,483 original posts and 1,475,044 comments from 303,016 unique users spread across these communities. The raw data underwent several processing steps aimed at preserving both quality and user privacy: we retained core metadata fields such as content identifiers, textual body, and timestamps, while pseudonymizing all user references. Posts written in languages other than English, duplicate entries, empty submissions, and content produced by bots or moderator accounts were systematically excluded.

2.1. Psychological Dimensions of Online Content

To identify users’ psychological and linguistic profiles, we extracted features from posts and comments along five dimensions: (i) Plutchik’s Primary Emotions: We applied Plutchik’s psychoevolutionary framework [23] to classify emotional expression in text. Implementation utilized the NRC lexicon [24] through the NRCLex Python library (https://github.com/metalcorebear/NRCLex (accessed on 15 January 2025)), which categorizes textual content across eight base emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. Each emotion dimension receives a normalized score between 0–1, where 0 indicates the absence of the emotion and 1 its maximum expression. (ii) PAD Emotional Dimensions: To capture emotional states beyond discrete categories, we implemented the PAD model [25] using the NRC VAD Lexicon [26]. This approach measures text along three continuous dimensions: valence (pleasantness), arousal (intensity/activation), and dominance (control/power). The lexicon assigns standardized scores from 0–1 for each dimension. (iii) VADER Sentiment: For sentiment analysis specifically optimized for social media content, we employed VADER [27] through its Python implementation (https://github.com/cjhutto/vaderSentiment (accessed on 15 January 2025)). This tool evaluates the emotional tone underlying texts, producing separate positive and negative sentiment intensity scores on a 0–1 scale, where higher values indicate stronger sentiment expression. (iv) Taboo Rate: We quantified potentially offensive language using the Tabooness Dataset [28], which contains 1205 English words with annotated taboo ratings. This metric identifies the prevalence of socially inappropriate terminology, with higher scores associated with greater use of offensive language and lower scores reflecting more neutral expression. (v) TextBlob Subjectivity: To distinguish between fact-based and opinion-driven communication, we utilized the TextBlob Python library (https://textblob.readthedocs.io/en/dev (accessed on 15 January 2025)). This tool calculates subjectivity scores ranging from 0 (entirely objective content) to 1 (highly subjective expression) based on lexical indicators of personal perspective versus factual reporting. Prior to feature extraction, all text underwent preprocessing including lowercase conversion, elimination of non-printable characters, removal of formatting elements (XSLT tags, newlines, tabs), URL filtering, punctuation removal, and whitespace normalization.

2.2. Clustering and Topic Detection

The psychological and linguistic features extracted from user posts were used to identify recurring patterns in user behavior through unsupervised machine learning techniques, specifically clustering [29]. Clustering allows us to group users based on similarities in emotional and psycholinguistic expression without imposing predefined categories. Before performing clustering, we conducted statistical checks to assess the quality and distinctiveness of the extracted features. Specifically, we computed pairwise Pearson correlations across the full set of user–month feature vectors, where each vector represents a user’s average scores on the five psychological and linguistic dimensions described above within a given month. This analysis showed that most features were weakly to moderately correlated, indicating that they captured complementary aspects of users’ expression and behavior. In addition, all features exhibited sufficient variance to support meaningful segmentation (see Figure 3A). We then applied the K-Means clustering algorithm [29] to group user behavior profiles. Each observation corresponds to a user’s activity aggregated at the monthly level, allowing us to capture variability in psychological and linguistic expression over time while maintaining a tractable representation of user behavior. To determine the number of clusters, we first employed the elbow method, which evaluates within-cluster variance for increasing values of k (ranging from 2 to 10). The point of diminishing returns was observed at $k=4$ (see Figure 3B), which we retained as the main partition for the analyses reported below. To further assess the robustness of this choice, we additionally computed three internal validity indices across 50 random initializations: the Silhouette coefficient, the Calinski–Harabasz index, and the Davies–Bouldin index. While these indices assign their globally optimal score to $k=2$—a common outcome reflecting the dominant negative-versus-positive affect split in the data—a two-cluster solution is too coarse to differentiate the range of coping-related linguistic profiles central to this study. Among the more fine-grained solutions ($k\ge 3$), the Silhouette coefficient reaches a local maximum at $k=4$ ($s=0.143$, compared to $s=0.138$ at $k=3$ and $s=0.119$ at $k=5$), and the Davies–Bouldin index improves from $k=3$ ($DB=2.14$) to $k=4$ ($DB=2.09$), indicating better compactness at the retained partition. Taken together, these results do not identify a single unequivocal optimum, but they support $k=4$ as a reasonable and analytically useful compromise between parsimony and interpretive resolution.

Subsequently, to characterize the content associated with each cluster, we employed two complementary text analysis techniques. First, we applied Term Frequency–Inverse Document Frequency (TF–IDF) [30] to identify words that were particularly characteristic of each group. This approach highlights terms that appear frequently within a given cluster while remaining relatively rare in others, thus capturing the distinctive vocabulary associated with each psychological profile. Second, we used BERTopic (https://github.com/MaartenGr/BERTopic (accessed on 15 January 2025)) a topic modeling framework, to uncover recurring themes in the textual content of each cluster. The model was configured to process unigrams, bigrams, and trigrams, and we imposed a minimum topic size of 200 documents to ensure stability and interpretability. This procedure yielded ten salient thematic categories per cluster, which were used to qualitatively relate them to coping orientations discussed in Section 4.

3. Results

As described in Section 2, we analyze 378,483 posts and 1,475,044 comments from six depression-focused subreddits, involving 303,016 unique users over a two-year period. We extract five key psychological and linguistic dimensions from this user-generated content to create user profiles, which we then cluster using the K-Means algorithm. In this section, we discuss the four obtained clusters in terms of extracted psychological/linguistic features and the most frequent words and topics that characterize them.

3.1. Cluster 1: Emotionally Turbulent/Under Blackout Users

• Psychological/Linguistic Dimension. As shown in Figure 4A,B,D, users in this cluster display a clear prevalence of negative sentiment (−0.19) relative to positive sentiment (0.12). Among Plutchik’s eight basic emotions, negative ones dominate: sadness (0.14), fear (0.12), anger (0.10), and disgust (0.08) all score higher than joy (0.05), while surprise (0.03), trust (0.06), and anticipation (0.07) remain similarly subdued. These emotional scores co-occur with elevated levels of arousal (0.48), valence (0.54), dominance (0.49), taboo word usage (0.22), and subjectivity (0.65) (see Figure 4C,E,F). Taken together, this configuration points consistently toward a negative emotional orientation, making cluster 1 the group with the strongest negative affective profile across all dimensions [31,32]. Looking at the PAD Emotional Dimensions specifically, this cluster exhibits marginally elevated arousal alongside somewhat reduced dominance and valence scores compared to the other groups (see Figure 4A,C). Furthermore, cluster 1 records the highest negative sentiment and the lowest positive sentiment, as well as the greatest use of taboo words and the highest degree of subjectivity across all clusters (see Figure 4E,F). Turning to within-cluster correlations (Figure 3C), taboo rate shows a significant relationship with subjectivity (r = −0.15; p < 0.001), anger (r = 0.14; p < 0.001), and disgust (r = 0.21; p < 0.001). This pattern may reflect a tendency to use aggressive language not so much to provoke others directly, but as a byproduct of depersonalized expression—that is, language stripped of personal perspective or emotional ownership [23]. The Theory of Deindividuation [33] offers a useful frame here: when individuals operate under conditions of anonymity, their sense of personal accountability and self-monitoring tends to weaken. A parallel dynamic may apply to linguistic depersonalization: by distancing themselves from their own viewpoint, users may feel less inhibited in reaching for offensive or taboo expressions, effectively treating their language as anonymous output rather than personal disclosure. Consistent with this reading, taboo rate is negatively correlated with fear (r = −0.12; p < 0.001, Figure 3C), suggesting that the more a user relies on offensive language, the less fear is present in their writing—as though impersonal expression lowers the perceived social risk of hostile communication. Also notable is the near-absence of both anticipation and surprise in this cluster. Within Plutchik’s framework [23], these two emotions constitute opposing ends of the same dyad; their joint suppression here may signal both a lack of forward-looking engagement (no anticipation) and an absence of reactive alertness to new information (no surprise). This temporal flatness reinforces the picture of a group whose emotional narratives are anchored predominantly in present or past experiences, with little cognitive orientation toward what lies ahead.
• Content Profile. This emotional landscape described previously is reflected in the cluster’s most frequent words (Figure 5A), which are predominantly negative and include offensive terms (e.g., “faggot”) and pessimistic expressions (e.g., “atrocity”, “abhorrent”, “unfounded”). Notably, cluster 1 is the only cluster without any positively associated words among its top 15 most frequent terms, as determined by TF-IDF analysis.

The topics discussed in this cluster (Figure 5B) further illustrate this emotional profile. The emotional tone of the key topics varies significantly: “Suicide” and “Self-Harm” are negative (0.2–0.4), “Appearance”, “Education/Work”, “Meds/Doctor”, and “Relationship” are neutral (0.4–0.6), while “Family”, “Need Support”, and “Sleep Habits” are positive (0.6–0.8). Cluster 1 has the highest number of negatively toned topics across all clusters. An interesting pattern emerges in users’ discussions of “Self-Harm” and “Suicide”. These topics show a notable ambivalence, combining terms suggesting positive feelings (e.g., “like”) with instructions to stop such behaviors (e.g., “help”, “bad”). This phenomenon aligns with the concept of ”surviving moment to moment” [34], reflecting an unstable mental state where life-or-death decisions are made frequently. Despite the overall negative tone, positively associated topics provide insights into users’ support-seeking behaviors. The “Family” topic indicates reliance on parental support, while “Need Support” shows a desire for specific forms of help, primarily someone to talk to. Both support-related topics evoke positive emotions, consistent with research showing improved well-being through sharing experiences [35]. Interestingly, “Sleep habits” also has a positive association. While sleep problems are a common symptom of depression [36], discussing experiences or improvements in this important area of life seems to evoke positive emotions among users, contributing to the topic’s positive tone. In conclusion, while cluster 1 shows some positive elements related to support-seeking and sleep habits, the overall pattern indicates high levels of sadness, anger, and negativity, with a tendency towards disrespectful interactions.

3.2. Cluster 2: Users Living a Eudaimonic Project Phase

• Psychological/Linguistic Dimension. The emotional profile of this cluster stands in marked contrast to cluster 1 at the level of psycholinguistic expression. Positive sentiment reaches a comparatively high level (0.23), while negative sentiment remains low (−0.07). Negative emotions are largely absent: sadness (0.05), fear (0.04), anger (0.03), and disgust (0.03) all register near-floor values, whereas joy (0.14), trust (0.15), and anticipation (0.15) emerge as the defining emotional signals, with surprise (0.06) also contributing (see Figure 4A). On the PAD dimensions (Figure 4C), this emotional pattern is accompanied by moderately elevated arousal (0.46), the highest valence score across all clusters (0.66), and a notable degree of dominance (0.54). The overall picture is one of pronounced positive affective orientation, with cluster 2 scoring highest on positivity among all groups. Taboo word usage is comparatively restrained (0.16), while subjectivity remains relatively high (0.57) (see Figure 4E,F). Within-cluster correlations (Figure 3D) shed further light on how offensive language functions in this group. Taboo rate is positively associated with anger (r = 0.20; p < 0.001), disgust (r = 0.24; p < 0.001), fear (r = 0.13; p < 0.001), and sadness (r = 0.14; p < 0.001), and similarly correlates with negative sentiment (r = 0.24; p < 0.001), while showing a negative relationship with valence (r = −0.27; p < 0.001). Unlike in cluster 1, where taboo language appears decoupled from negative affect, here its use is tightly bound to unpleasant emotional states—suggesting that when users in this group resort to offensive expression, it reflects genuine negative arousal rather than depersonalized venting.
• Content Profile. The analysis of the most frequent words (Figure 5A) highlights a lexical profile characterized by predominantly positive-valence terms, such as “charitable”, “vigorous”, and “archangel”, which are among the most distinctive words in this cluster according to TF-IDF. This positive tone appears closely tied to users’ faith and spiritual beliefs, as evidenced by the prevalence of religion-related words like “krishna” and “elohim”. The connection between religiosity and improved mental health is well-documented in literature [37,38], with researchers attributing this link to enhanced social support, the discovery of meaning, and improved self-regulation that often accompany religious practice. The cluster’s positivity is not solely rooted in spirituality, however. Physical activity and the pursuit of new hobbies emerge as potential contributors to the overall positive mood [39,40]. Moreover, the presence of terms like “dha” and “assuming” suggests a focus on medication adherence, which may play a significant role in maintaining this positive sentiment [41,42]. The positive emotional profile is mirrored in the topics (Figure 5B). The conversation revolves around areas such as “Education/Work”, “Family”, “Relationship”, “Friendship”, “Need Support”, “Therapy”, “Happy moments”, “Music”, and “Pets”. Notably absent are the “Suicide” and “Self-Harm” topics that appear in other clusters. All discussed topics carry a positive valence (0.6–0.8), with “Happy moments” and “Relationship” scoring even higher (exceeding 0.8). Social support, long recognized as a protective factor against depression [43,44,45], is a recurring theme. Users talk about multiple sources of possible support, each offering unique benefits. Psychotherapy, widely employed in depression treatment [46], is discussed in positive terms, suggesting users find it helpful in their mental health journeys. Music emerges as another significant positive element. Users engage with music both passively (listening) and actively (playing). Research has shown such interventions to be effective in reducing depression by enhancing socialization and improving cognitive and emotional processes [47]. The topic of Pets also features prominently in discussions in positive-valence terms, aligning with researches that associate pet ownership with lower levels of depression [48]. In conclusion, Cluster 2 is characterized by a predominantly positive-affect and approach-oriented linguistic profile, with topics less centered on self-harm and suicide than in other clusters. These patterns reflect more engaged and meaning-oriented forms of expression in the analyzed texts.

3.3. Cluster 3: Adherent-Yet-Conflicted Users

• Psychological/Linguistic Dimension. Cluster 3 occupies an intermediate emotional position relative to the preceding groups. Negative sentiment is moderately suppressed (−0.08), while positive sentiment reaches a moderate level (0.14). Among individual emotions, sadness (0.09), trust (0.10), and anticipation (0.11) stand out as the most represented, whereas joy (0.06), fear (0.07), anger (0.05), disgust (0.03), and surprise (0.04) remain peripheral (see Figure 4B). On the PAD dimensions, the cluster shows moderate scores across arousal (0.45), valence (0.60), and dominance (0.51) (see Figure 4C). Most distinctively, cluster 3 records the lowest values for both taboo word usage (0.15) and subjectivity (0.20) of all four groups (see Figure 4E,F). The low subjectivity score is particularly informative when read alongside the within-cluster correlations shown in Figure 3E. Subjectivity is positively associated with joy (r = 0.19; p < 0.001), trust (r = 0.11; p < 0.001), and positive sentiment (r = 0.11; p < 0.001). Given how infrequently these users engage in first-person, opinion-laden expression, moments of self-assertion appear to carry particular emotional weight: the more individuals in this cluster voice their own perspective, the more they tend to report positive affect and a sense of trust—whether directed at themselves upon achieving a personal goal, or at others who respond with genuine attention. The negative correlation between sadness and anticipation (r = −0.17; p < 0.001) points to another characteristic feature of this group. As sadness intensifies, forward-looking engagement diminishes—a pattern well consistent with the temporal disturbances associated with depression. Where non-depressed individuals typically maintain a motivationally driven orientation toward the future, depression can fracture this relationship, leaving the person anchored in the present with no clear sense of what lies ahead. This temporal compression tends to manifest as a diffuse feeling of stagnation, in which past, present, and future lose their ordinary distinctions [49]. Importantly, however, the anticipation present in this cluster does not take on an anxious character: its negative correlations with anger (r = −0.13; p < 0.001), fear (r = −0.11; p < 0.001), and sadness (r = −0.17; p < 0.001) indicate that it is not accompanied by distress, distinguishing it from the ruminative or threat-focused anticipation typically associated with anxiety disorders. Taken together, these features describe a group that is considerably less emotionally polarized than clusters 1 and 2. A quiet but consistent orientation toward trust—both in oneself and in others—combines with a stable, non-distressing sense of anticipation, against a backdrop of mild but persistent sadness. The overall tone is one of restrained engagement rather than either acute distress or pronounced well-being.
• Content Profile. Analysis of the most frequent words (Figure 5A) reveals a strong emphasis on technical terminology related to depression. This lexicon includes medical terms describing medication administration (e.g., “intravenous”), chemical factors associated with depression (“methylated”), names of antidepressants (“aplenzin”, “seropram”), other psychotropic medications (“amisulpiride”), and supplements supporting depression treatment (“dha”). The presence of terms like “glyphosate” suggests discussions about substances that can induce depression-like behaviors [50] or are used in suicide attempts [51]. The words “literacy” and “alertness” also feature prominently, with positive and negative valences respectively. “Literacy” might refer to general reading and writing abilities or more specifically to health literacy, reflecting users’ high level of knowledge about depression-related processes. Conversely, “alertness”, with its negative connotation could indicate discussions about side effects of psychotropic medications. The topics within cluster 3 exhibit more nuanced valences compared to previous clusters (Figure 5B). “Education/Work”, “Meds/Doctor”, “Need support”, “Sleep Habits”, and “Weight” show positive valences (0.6–0.8). “Suicide” and “Relationship” are neutral (0.4–0.6), while “Family” is strongly positive (>0.8), and “Self-Harm” is negatively connotated (0.2–0.4). The “Meds/Doctor” topic’s positive valence likely stems from perceived benefits of regular medication and doctor visits, as evidenced by terms like “effect”, “help”, and “work”. Similarly, “Need support” carries a positive sentiment, possibly reflecting fulfilled desires for sharing experiences and expressing emotions, which has been linked to enhanced well-being [35]. “Sleep habits” are discussed positively, which is noteworthy given the prevalence of sleep disturbances in depression. This positive perspective may be associated with effective medication and professional help, as suggested by the co-occurrence of “Meds/Doctor” and “Sleep habit” topics in this cluster. The “Weight” topic suggests a focus on healthy lifestyle choices, including regular exercise and a nutritious diet. This aligns with research showing the positive impact of diet [52] and physical activity [53] on mental health and depression management. Interestingly, the “Suicide” topic carries a neutral, rather than negative, connotation in this cluster. This could be due to the overall positive tone moderating the emotional impact of suicide-related discussions. The “Relationship” topic also maintains a neutral tone despite potentially negative content, possibly indicating successful resolution of relationship issues. The only topic with a negative valence is “Self-Harm”. This topic’s persistent negative connotation may reflect the complex dynamics of online self-harm communities. While these forums offer support and a sense of community, they may also normalize self-harming behaviors and perpetuate negative emotions [54]. In summary, cluster 3 represents a group with a high level of medical knowledge about depression, actively engaged in treatment and lifestyle improvements. While they discuss challenging topics like suicide and self-harm, the overall tone is more neutral or positive compared to other clusters, suggesting a more balanced perspective on their mental health journey.

3.4. Cluster 4: Users Navigating the Arousal Phase

• Psychological/Linguistic Dimension. The sentiment distribution in this cluster is notably balanced, with positive (0.13) and negative (−0.12) scores nearly mirroring each other (see Figure 4D). Most emotions register at moderate levels (Figure 4B): anticipation (0.09), trust (0.09), joy (0.07), fear (0.10), anger (0.08), and disgust (0.06), with sadness standing out as the most elevated (0.12) and surprise as the least represented (0.04). The PAD scores—arousal (0.46), dominance (0.51), and valence (0.58)—fall in a range comparable to clusters 2 and 3. In terms of taboo word usage (0.19) and subjectivity (0.46), cluster 4 more closely resembles cluster 2 than the remaining groups (see Figure 4B–F). Several noteworthy negative correlations emerge from the within-cluster analysis (Figure 3F): sadness relates inversely to dominance (r = −0.25; p < 0.001), trust (r = −0.30; p < 0.001), and anticipation (r = −0.24; p < 0.001). The inverse relationship between sadness and dominance may reflect how certain coping strategies alter the subjective experience of emotional intensity. When individuals rely on distraction or avoidance, they may report feeling less governed by their sadness even as its objective level rises. This interpretation receives support from Joormann, Siemer, and Gotlib [55], who found that distraction—rather than the retrieval of positive memories—was the more effective strategy for alleviating low mood in both currently and formerly depressed individuals. As noted by Lazarus and Folkman [14], no coping strategy is inherently adaptive or maladaptive; outcomes depend heavily on the individual and the situational context, and the same behavior may serve different regulatory functions across people. The negative link between sadness and trust is consistent with findings by Dunn and Schweitzer [56], who demonstrated that momentary emotional states shape interpersonal trust in systematic ways: happy individuals trust more than sad ones, who in turn trust more than angry ones. The authors attribute this gradient to differences in cognitive appraisal—specifically, the degree of perceived control embedded in each emotional state. Anger, which involves attributing outcomes to other people, and happiness, which involves low control appraisals, both exert stronger effects on trust than sadness, whose appraisal profile is more situationally grounded. In our data, trust is further negatively correlated with anger (r = −0.23; p < 0.001) and positively with joy (r = 0.34; p < 0.001), a pattern that aligns closely with this framework [56]. Finally, the inverse association between sadness and anticipation echoes the temporal disruptions described in prior work on depression [49]: as sadness deepens, motivation to engage with future events weakens. More broadly, this cluster appears to capture individuals managing a heterogeneous mix of everyday stressors, whose cumulative effect depresses both negative and, to a lesser extent, positive emotional perception [57].
• Content Profile. The thematic landscape of cluster 4 includes Relationship”, Education/Work”, Suicide”, Meds/Doctor”, Friendship”, Self-Harm”, Weight”, Music”, and Pets” (Figure 5B). Of these, Self-Harm” is the only topic carrying a negative valence (0.2–0.4); Meds/Doctor” and Suicide” are tonally neutral (0.4–0.6); and all remaining topics skew positive (0.6–0.8). The lexical content of the Self-Harm” topic—including terms such as “like” and “help”—suggests that users may frame such behaviors as a means of managing overwhelming affect rather than purely as destructive acts. The neutral treatment of Suicide” may follow from this: some researchers have proposed that non-suicidal self-injury can function as a short-term buffer against suicidal ideation, temporarily reducing emotional pressure [58], which would explain the ambivalence observed here. Social connection emerges as a prominent coping resource across several topics. Friendship”, anchored by the term “talk”, reflects the value users place on sharing their experiences with peers, who can provide emotional, informational, and practical support [59]. Relationship” similarly suggests that confiding in a partner may serve an emotion-regulating function [60]. Music” and Pets” round out this picture, functioning either as sources of comfort [48] or as means of distraction from depressive thoughts [47]. The neutral valence of Meds/Doctor”—characterized by terms like “side effect”, “help”, “work”, and “try”—points to a pragmatic engagement with pharmacological treatment: users acknowledge its limitations while maintaining adherence. A comparable orientation toward health behavior appears in the Weight” topic, where expressions around eating and body mass likely reflect both the metabolic side effects of antidepressants [61] and the well-documented tendency toward emotional eating in depression [62]. “Education/Work”, by contrast, carries a clearly positive tone, with terms such as “good”, “time”, and “like” suggesting that occupational and academic engagement remains a source of satisfaction despite broader difficulties. Worth noting is that the TF-IDF terms most distinctive of cluster 4 (Figure 5A) are comparatively uninformative and add little to the characterization derived from topic modeling alone. Overall, cluster 4 portrays a group that is emotionally and behaviorally active across multiple life domains, employing a range of coping strategies—some adaptive, some not—while sustaining meaningful engagement with work, relationships, and daily routines.

4. Discussion

In this section, we interpret the four data-driven clusters in light of two complementary theoretical frameworks: the COPE inventory [16] and the Patient Health Engagement (PHE) model [20]. Specifically, we discuss how each cluster’s emotional/linguistic profile and content themes (Section 3) can be read as proxies of (i) a prevailing configuration of coping orientations (e.g., venting, social support, active coping, positive reinterpretation) and (ii) an engagement phase in the PHE model—Blackout, Arousal, Adhesion, and Eudaimonic Project. Importantly, this mapping is interpretative: clusters are obtained from language-based measures, and the COPE/PHE labels are used to provide a theoretically grounded reading of the patterns that emerge from users’ narratives.

4.1. Cluster 1: Emotionally Turbulent/Under Blackout Users

Users in Cluster 1 predominantly exhibit emotionally intense and linguistically dysregulated patterns that, when interpreted through the COPE framework (Figure 1), are compatible with Venting Emotions and coping strategies oriented toward Seeking Emotional and Instrumental Social Support. As discussed by Litman [17], venting and support-seeking strategies are grouped among socially supported coping factors, which tend to co-occur with avoidance tendencies and, in the case of venting, show small positive associations with negative affective traits such as anxiety. This theoretical characterization aligns with the emotional and linguistic profile observed in Cluster 1, which is marked by elevated negative sentiment and high levels of sadness, anger, fear, and disgust (Figure 4), together with the highest taboo rate among all clusters.

Within this profile, the prominent use of offensive and vulgar language can be interpreted as a linguistic manifestation of emotional venting. Prior work suggests that swearing may function as a means of conveying intense emotional states and, in some contexts, may produce short-term cathartic effects [63]. Consistent with this view, we observe a negative correlation between taboo rate and sadness in Cluster 1 (r = −0.18), suggesting that expressing emotions through taboo language may partially alleviate feelings of sadness. However, this effect appears limited and potentially counterbalanced by less adaptive outcomes. As shown in Figure 3C, taboo rate is positively correlated with anger (r = 0.14) and disgust (r = 0.21), while being negatively associated with fear (r = −0.12; p < 0.001). This pattern supports the idea that venting through aggressive language may reduce inhibition and fear, while simultaneously reinforcing hostile emotional states. In line with Vingerhoets et al. [63], these findings suggest that venting is not uniformly effective as a coping strategy and may, in some cases, exacerbate negative emotional activation rather than regulate it.

Alongside venting, Cluster 1 also exhibits signals of coping oriented toward social support, as reflected by the presence of the topics “Family” and “Need Support”. As shown in the content analysis (Figure 5), these topics are characterized by a positive valence, indicating that users perceive emotional or instrumental assistance from others as beneficial. This dual pattern—intense emotional expression coupled with support-seeking—suggests that users in this cluster are not disengaged from their social environment, but rather oscillate between dysregulated emotional expression and early attempts to obtain help. Previous research has shown that seeking social support in response to stressors can predict fewer depressive symptoms over time, whereas venting-oriented strategies are more consistently associated with symptom persistence [64]. The coexistence of both strategies in Cluster 1 may therefore reflect an unstable coping configuration, in which adaptive and maladaptive responses are simultaneously present.

Interpreted through the lens of the Patient Health Engagement model (Figure 2), Cluster 1 is consistent with the Blackout phase described by Graffigna et al. [20]. This phase is characterized by emotional overwhelm, limited cognitive clarity regarding one’s health condition, and difficulty enacting structured self-management behaviors. The high emotional intensity, antagonistic language use, and unstructured help-seeking observed in this cluster align with a state in which individuals are affectively activated but lack the emotional readiness and cognitive resources required for sustained engagement with their condition. In this sense, Cluster 1 captures an early and fragile engagement state, where coping efforts are present but remain largely reactive and insufficiently regulated.

4.2. Cluster 2: Users Living a Eudaimonic Project Phase

The emotional tone and content themes characterizing Cluster 2 point to a pattern of coping strategies oriented toward positive meaning-making, social connectedness, and emotional regulation, which can be coherently interpreted within the COPE framework (Figure 1). In particular, users in this group appear to rely on Instrumental Social Support, Emotional Social Support, Religious Coping, and Positive Reinterpretation. In addition, some activities discussed in this cluster (e.g., “Music”) suggest the presence of Mental Disengagement as a complementary coping orientation. According to Litman [17], seeking social support, both instrumental and emotional, belongs to the class of socially supported coping strategies. These strategies are characterized by a positive association with avoidance tendencies, alongside a small but significant correlation with the approach motive Reward Seeking, defined as the extent to which individuals derive pleasure from rewarding experiences. This dual association suggests that social support may be sought both as a way to confront distress and as a means to buffer against anticipated negative outcomes [17]. The prominence of socially oriented topics in this cluster (e.g., “Family”, “Friendship”, “Need Support”) supports this interpretation, indicating that interpersonal relationships function as emotional resources.

Religious Coping, which also emerges in this cluster, occupies a distinctive position within the COPE structure. As noted by Litman [17], it loads predominantly– though not strongly—on self-sufficient, emotion-focused coping and shows no robust association with either approach or avoidance motives. In this context, religious practices and beliefs may operate as meaning-making resources rather than as direct behavioral responses to stress, a role that aligns with the positive emotional tone characterizing this cluster.

Positive Reinterpretation represents another key coping strategy observed in this group. Classified as a self-sufficient, emotion-focused strategy, it is positively associated with approach motives such as Fun Seeking, Reward Seeking, and Drive Seeking, and shows no relation to avoidance tendencies [17]. This pattern suggests an active effort to cognitively reframe stressful experiences in a constructive manner. Consistently, Positive Reinterpretation has been shown to function as a protective factor against depression, as it promotes emotional regulation and reduces depressive symptomatology through adaptive meaning reconstruction [15].

Alongside these strategies, traces of Mental Disengagement can be identified in leisure-oriented activities discussed by users (e.g., music listening or playing, physical exercise). Although typically classified as an avoidant coping strategy, Mental Disengagement is positively associated not only with avoidance motives but also with the approach motive Fun Seeking [17]. This dual role highlights its potential functionality in specific contexts: while it may not directly address the stressor, it can temporarily alleviate emotional burden and restore motivational resources. As emphasized by Lazarus [14], coping strategies cannot be labeled as universally adaptive or maladaptive; rather, their effectiveness depends on the individual and the situational demands.

Interestingly, this configuration closely mirrors the coping strategies reported by Saravanan et al. [65] among individuals recovering from depression. In that study, participants described a combination of social sharing (“Talking Cure”), engagement in distracting or rewarding activities (“Getting Myself Busy”, “Physical Exercise”), positive thinking, and religious practices. Each of these strategies finds a clear correspondence within the COPE dimensions observed in this cluster, reinforcing the interpretation of these coping patterns as conducive to emotional recovery and psychological well-being.

Taken together, the emotional, linguistic, and thematic features of this cluster depict a group oriented toward positive experiences, supportive relationships, and meaning-making practices. Within the Patient Health Engagement framework (Figure 2), this constellation is consistent with the Eudaimonic Project phase [20], characterized by sense-making, full elaboration of the illness experience, and the enactment of situated, health-promoting behaviors. Rather than merely managing distress, the linguistic patterns of this cluster suggest an orientation toward integrating the health experience into a broader life narrative, with language reflecting adaptive coping and active engagement.

4.3. Cluster 3: Adherent-Yet-Conflicted Users

Cluster 3 is characterized by linguistic and thematic patterns that emphasize treatment adherence, lifestyle regulation, and future-oriented concerns, suggesting the use of structured and problem-focused coping strategies within the COPE framework (Figure 1). Particularly, this pattern is consistent with a combination of Active Coping, Planning, and both Instrumental and Emotional Social Support. Several topics prevalent in this cluster—such as “Sleep Habits”, “Weight”, and “Meds/Doctor”—can be interpreted as coping-related behaviors [66,67]. While health behaviors are often framed within coping and stress models as avoidant or disengaging strategies (e.g., sleeping excessively, eating for comfort, or using substances to regulate mood) [66], this perspective tends to emphasize maladaptive outcomes. In the present case, however, these behaviors are predominantly associated with positive emotional valence, suggesting a more functional role. Rather than reflecting disengagement, attention to medication adherence, diet, physical activity, and sleep may represent deliberate efforts to manage depressive symptoms and regain control over daily functioning.

Within the COPE taxonomy, this orientation aligns closely with Active Coping, defined as taking direct steps to eliminate or mitigate the stressor. According to Litman [17], Active Coping is a self-sufficient, problem-focused strategy that shows robust positive correlations with approach-oriented motivational systems, including Drive, Fun Seeking, and Reward Seeking. The presence of Active Coping in this cluster suggests that users are not only aware of their condition but are also actively attempting to address it through behavioral change and adherence to treatment.

At the same time, the co-occurrence of Instrumental and Emotional Social Support indicates that these efforts are embedded within a social context. Seeking advice, reassurance, or practical help from others (e.g., family members, professionals, or significant others) represents an additional coping layer. As discussed by Litman [17], socially supported strategies occupy an ambivalent position, combining avoidance tendencies with a modest but positive association with Reward Seeking. This duality may reflect a tension between the desire to actively manage depression and lingering uncertainty or vulnerability regarding outcomes.

The elevated levels of anticipation observed in this cluster further support the interpretation of a proactive coping stance. Anticipation, in this context, may signal forward-looking engagement—taking action during periods of relative emotional stability to prevent relapse or deterioration. This pattern is consistent with the COPE strategy of Planning, which involves developing concrete action plans to deal with stressors. Like Active Coping, Planning is classified as a self-sufficient, problem-focused strategy and is positively associated with approach motives such as Drive, Fun Seeking, and Reward Seeking [17].

Despite this generally adaptive coping configuration, the presence of the “Self-Harm” topic introduces a critical element of tension. While most health-related behaviors in this cluster appear functional, self-harm remains a locus of distress and likely contributes to the persistent presence of sadness. This coexistence of constructive engagement and residual maladaptive behavior underscores the conflicted nature of this group: individuals are actively adhering to treatment and lifestyle changes, yet continue to struggle with deeply ingrained coping difficulties.

Within the Patient Health Engagement framework (Figure 2), this constellation of features is consistent with the Adhesion phase [20]. This phase is characterized by cognitive adhesion to the health experience (i.e., sufficient knowledge and awareness of the condition), acceptance of one’s illness, and formal adherence to prescribed treatments and health-related behaviors. However, engagement at this stage remains fragile, as emotional vulnerability and conflicting coping tendencies can still undermine full stabilization.

4.4. Cluster 4: Users Navigating the Arousal Phase

The patterns observed in Cluster 4 reflect a heterogeneous coping configuration, in which attempts to actively manage distress coexist with avoidance-oriented behaviors. Within the COPE framework (Figure 1), this configuration is consistent with a combination of Instrumental and Emotional Social Support, Active Coping, and Mental Disengagement. Seeking Social Support—both instrumental and emotional—is classified by Litman [17] as a socially supported coping strategy and shows a positive association with avoidance tendencies, together with a modest positive correlation with the approach motive Reward Seeking. This suggests that reaching out to others may reflect both an intention to address distress and concerns about potential negative outcomes.

At the same time, several behaviors observed in this cluster point toward Active Coping. Engagement with medical professionals and adherence to treatment (topic “Meds/Doctor”), as well as efforts directed toward weight management (topic “Weight”), indicate attempts to directly address depressive symptoms despite ongoing difficulties. According to Litman [17], Active Coping is a self-sufficient, problem-focused strategy that positively correlates with approach-oriented motives such as Drive, Fun, and Reward Seeking, reflecting behavioral activation and goal-directed effort. The recurring use of expressions such as “try” in both medical and lifestyle-related topics further supports this interpretation, suggesting engagement without full stabilization.

Social support also plays a central role in this cluster. Relationships with friends, partners, and professionals (topics “Friendship”, “Relationship”, and “Meds/Doctor”) represent important sources of emotional and instrumental support. In addition, pets emerge as a relevant form of Emotional Social Support. Companion animals are known to provide a sense of safety, reduce social isolation, and promote emotional regulation, contributing to psychological well-being [48]. Together, these elements suggest that users in this cluster actively seek external resources to cope with distress.

Alongside these adaptive efforts, however, avoidant strategies are also present. Mental Disengagement—an avoidant coping strategy identified by Litman [17]—is evident in the use of distraction-based activities such as listening to music (topic “Music”). While such strategies may provide temporary relief and help sustain moderate levels of joy, they do not directly address the underlying stressor. In this cluster, avoidance becomes particularly problematic when it takes the form of self-harming behaviors (topic “Self-Harm”). Experiential avoidance, especially through self-harm, may initially reduce emotional intensity but ultimately interferes with emotional processing and increases vulnerability to depressive symptoms [58,68]. This coexistence of constructive engagement and maladaptive avoidance highlights the internal tension that characterizes this group.

Taken together, these patterns depict a group that is emotionally activated and partially engaged in managing depression, yet still oscillating between functional coping and risky avoidance strategies. In line with the Patient Health Engagement model [20], cluster 4 can be interpreted as corresponding to the Arousal phase of the illness journey. This phase is marked by increased emotional activation and alertness, basic awareness of the health condition, and behavioral experimentation. Although some healthy behaviors are present, they remain fragmented and inconsistently aligned with long-term treatment goals, reflecting an engagement state that is active but not yet fully organized.

5. Conclusions

This study provides a data-informed exploration of how linguistic profiles associated with different coping orientations are expressed in online depression communities. By analyzing large-scale Reddit data through established psychological frameworks, we show that users’ linguistic and emotional patterns can be organized into distinct profiles that reflect different modes of coping and levels of engagement with their mental health condition. We offer an interpretable, theory-guided reading of empirical patterns emerging from online self-disclosure, contributing to theory-building efforts at the intersection of mental health research and computational social science.

The identified profiles highlight that online depression communities host heterogeneous user experiences, ranging from highly distressed and emotionally turbulent states to more adaptive and acceptance-oriented forms of engagement. Interpreting these profiles through the lenses of the COPE and PHE models allows us to frame online participation as part of a broader space where early, intermediate, and more advanced forms of engagement coexist.

These results point to the potential role of online mental health communities as accessible, low-threshold support environments that may complement traditional mental health care pathways. Similar to stepped-care approaches adopted in other clinical contexts, online communities could function as an initial point of support, offering emotional expression, peer validation, and informal coping resources, before escalation to more structured interventions when needed. From this perspective, computational analyses of online discourse may help characterize patterns of distress and coping at scale, supporting future work on early identification and resource prioritization without replacing clinical assessment.

Despite these contributions, the present findings should be interpreted in light of the following limitations. First, our analysis is based exclusively on linguistic data and does not include psychometric assessments or verified clinical diagnoses. As a result, the identified clusters proxying psychological states should be interpreted as patterns of expression rather than direct indicators of mental health status. Second, participation in depression-related subreddits does not necessarily imply a clinical diagnosis, as users may include individuals experiencing subclinical distress, caregivers, or others seeking information. Third, the use of lexicon-based methods, while transparent and interpretable, may overlook contextual nuances, irony, or culturally specific forms of expression. Finally, the observational nature of the data prevents any causal inference regarding the effects of online participation on mental health outcomes.

Related to data interpretation, the modeling choices adopted in this study reflect specific analytical decisions whose influence on the results should be acknowledged. Nevertheless, each choice was explicitly motivated. The K-means algorithm was run across 50 random initializations, and the resulting cluster assignments proved stable, indicating that the solution is not an artifact of initialization. The number of clusters was determined through the elbow criterion and corroborated by three internal validity indices (Silhouette, Calinski–Harabasz, Davies–Bouldin); as discussed in Section 2, these do not identify a single unequivocal optimum, but collectively support $k=4$ as a reasonable and analytically useful partition. This solution was retained, and only subsequently did the convergence with the four phases of the PHE model emerge from the data, rather than being assumed a priori. Moreover, the lexicon-based feature set was chosen for the sake of interpretability, but remains sensitive to the known limitations of dictionary methods, such as their inability to capture context-dependent meaning. These constraints bound the claims of the present analysis: the four profiles represent a coherent and theoretically grounded interpretation of the data, but not necessarily the only one.

Future research should therefore aim to validate and extend these findings through mixed-method approaches, combining computational analyses with self-report measures, longitudinal designs, and more context-sensitive language models. Integrating these perspectives could help clarify how online coping expressions relate to offline well-being and how digital communities can be responsibly embedded within broader mental health support ecosystems.