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Depression Risk Assessment in Social Media via Large Language Models
Giorgia Gulino, Manuel Petrucci
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Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 96%
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Summary
This paper proposes a zero-shot depression risk assessment system using Large Language Models (LLMs) to analyze Reddit posts. The method involves multi-label classification of eight depression-associated emotions (anger, cognitive dysfunction, emptiness, hopelessness, loneliness, sadness, suicide intent, and worthlessness) and the calculation of a weighted severity index. The study evaluates several LLMs, finding that gemma3:27b performs best (micro-F1=0.75), performing competitively with fine-tuned models like BART. An in-the-wild analysis of 469,692 Reddit posts from 2024-2025 reveals distinct emotional profiles and risk score distributions across subreddits like r/depression and r/anxiety, demonstrating the scalability of LLMs for longitudinal psychological monitoring.
Entities (8)
Relation Signals (4)
r/depression → contains → depression-associated emotions
confidence 100% · In r/depression, sadness and hopelessness exceed 80–90% of posts
suicide intent → contributesto → depressive severity index
confidence 100% · assign weights to emotions (suicide_intent=3...)
gemma3:27b → evaluatedon → DepressionEmo
confidence 100% · The method is evaluated in a zero-shot setting on the annotated DepressionEmo dataset
gemma3:27b → outperforms → qwen3:0.6b
confidence 90% · gemma3:27b is the best-performing LLM overall... qwen3:0.6b achieves very high precision but very low recall
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Abstract
Abstract:Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The proliferation of social media platforms provides a rich source of naturalistic linguistic signals for the automated monitoring of psychological well-being. In this work, we propose a system based on Large Language Models (LLMs) for depression risk assessment in Reddit posts, through multi-label classification of eight depression-associated emotions and the computation of a weighted severity index. The method is evaluated in a zero-shot setting on the annotated DepressionEmo dataset (~6,000 posts) and applied in-the-wild to 469,692 comments collected from four subreddits over the period 2024-2025. Our best model, gemma3:27b, achieves micro-F1 = 0.75 and macro-F1 = 0.70, results competitive with purpose-built fine-tuned models (BART: micro-F1 = 0.80, macro-F1 = 0.76). The in-the-wild analysis reveals consistent and temporally stable risk profiles across communities, with marked differences between r/depression and r/anxiety. Our findings demonstrate the feasibility of a cost-effective, scalable approach for large-scale psychological monitoring.
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- Source: https://arxiv.org/abs/2604.19887v1
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Depression Risk Assessment in Social Media via Large Language Models Giorgia Gulino 1 Manuel Petrucci 1 1 Guglielmo Marconi University, Department of Human Sciences, Rome, Italy Corresponding: g.gulino1@studenti.unimarconi.it Abstract Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiag- nosed and undertreated. The proliferation of social media platforms provides a rich source of naturalistic linguistic signals for the automated monitoring of psychological well- being. In this work, we propose a system based on Large Language Models (LLMs) for depression risk assessment in Reddit posts, through multi-label classification of eight depression-associated emotions and the computation of a weighted severity index. The method is evaluated in a zero-shot setting on the annotated DepressionEmo dataset (≈6,000 posts) and applied in-the-wild to 469,692 comments collected from four subreddits over the period 2024–2025. Our best model,gemma3:27b, achieves micro-F1 = 0.75 and macro-F1 = 0.70, results competitive with purpose-built fine-tuned models (BART: micro-F1 = 0.80, macro-F1 = 0.76). The in-the-wild analysis reveals consistent and tem- porally stable risk profiles across communities, with marked differences between r/depression and r/anxiety. Our find- ings demonstrate the feasibility of a cost-effective, scalable approach for large-scale psychological monitoring. Keywords: depression, digital mental health, LLM, Red- dit, automatic detection, prompt engineering, severity index. 1 Introduction Depression is one of the most prevalent and debilitating mental disorders globally, recognized by the WHO as a leading cause of disability [1, 2]. Despite the availability of effective treatments, a substantial proportion of cases remains undiagnosed or insufficiently treated, particularly among young adults [1]. Early detection of depressive dis- tress signals is therefore of critical importance for timely intervention. The pervasive digitalization of daily life has made social media a privileged observatory for studying psychological behaviors and emotional expressions. Individuals routinely share thoughts and emotional states online, constituting an indirect yet relevant source of information on psychological well-being [3, 4, 5]. Computational psychology research has demonstrated that textual content published on plat- forms such as Reddit and Twitter contains linguistic markers of emotional distress, enabling the early identification of depressive risk [6, 7, 8]. In parallel, advances in Natural Language Processing (NLP) have made it possible to systematically analyze large volumes of online text. The introduction of Transformer- based architectures [9] and, more recently, Large Language Models (LLMs) has opened new possibilities: these models learn deep linguistic representations from massive text cor- pora, developing a sensitivity to emotional nuance that is difficult to capture with classical approaches [10, 11]. Compared to fine-tuned models, LLMs offer a significant operational advantage: they can be deployed in zero-shot mode without requiring domain-specific labeled data, dras- tically reducing development costs while improving adapt- ability to rapidly evolving linguistic contexts [12]. This paper makes the following contributions: 1.A weighted depressive severity index based on eight clinically relevant emotion categories, inspired by the standardized PHQ-9 and BDI-I scales. 2.A prompt engineering methodology for zero-shot multi- label classification of depressive emotions via LLMs. 3. A systematic evaluation of nine locally-run LLMs (0.6B– 27B parameters) against fine-tuned baselines from the literature on the DepressionEmo dataset [13]. 4.An in-the-wild analysis of 469,692 Reddit posts (2024– 2025), examining emotion distributions, risk profiles, and longitudinal trends. The proposed system is not intended as a replacement for clinical assessment, but as a scalable support and triage tool for large-scale psychological monitoring [14], on limited affordable hardware running models in local. The paper is structured as follows: Section 2 shows the related works in clinical measurement of depression sever- ity, machine learning applied to depression detection and emotions relevant for depression identification. Section 3 presents the our approach with the score and the prompt used to guide LLMs in the detection. Section 4 presents two section of experiments: controlled and in-the-wild, which are then discussed in Section 5. Finally, Section 6 poses the conclusion of this work. arXiv:2604.19887v1 [cs.CL] 21 Apr 2026 2 Related Work This section presents three key areas of prior research that contextualize our approach. First, we review the clinical measurement of depression severity, focusing on standard psychometric scales and the relative clinical weight of specific symptoms (Section 2.1). Second, we trace the evolution of NLP and machine learning techniques for depression detection, from traditional algorithms to large language models (Section 2.2). Finally, we examine the literature concerning the specific emotional states associated with depression and their linguistic taxonomies (Section 2.3). 2.1Clinical Measurement of Depression Severity Depression severity is traditionally assessed through stan- dardized psychometric scales. The DSM-5 defines nine core symptoms of major depressive disorder, including depressed mood, anhedonia, sleep and appetite disturbances, fatigue, difficulty concentrating, feelings of worthlessness or guilt, and recurrent thoughts of death [15, 16]. Instruments such as the Patient Health Questionnaire-9 (PHQ-9) and the Beck Depression Inventory-I (BDI-I) quantify these symp- toms through additive scoring, with established thresholds distinguishing mild, moderate, and severe depression [17]. Although each symptom nominally contributes equally to the total score, clinical practice assigns de facto greater weight to specific indicators. In particular, persistent hope- lessness is recognized as a strong risk factor for suicidal ideation and attempts [18], while suicide intent warrants im- mediate clinical intervention. These clinical considerations informed the weighting scheme adopted in the present work. 2.2NLP and Machine Learning for Depres- sion Detection Early computational approaches to depression detection in social media relied on traditional machine learning meth- ods (SVM, logistic regression) operating on hand-crafted linguistic features: frequency of emotionally negative words, first-person pronouns, and absolutist expressions [6, 8, 19]. While effective in controlled settings, these methods exhib- ited limited generalization to variable and evolving language patterns. The advent of deep learning and Transformer archi- tectures [9] brought substantial improvements. Domain- specialized variants such as BERTweet for social media lan- guage [20] and ClinicalBERT for clinical text [21] achieve 80–90% accuracy in depression identification when trained on in-domain data [22, 10]. More recently, LLMs such as GPT-3.5 and GPT-4 have been explored in zero-shot and few-shot settings, demonstrating notable screening capabil- ity without any fine-tuning [12, 23, 24]. A fundamental limitation of LLMs, however, is the strong correlation be- tween model size and performance, which entails significant computational costs for the largest models [12]. 2.3 Emotions Associated with Depression The literature has identified a recurring set of emotion cate- gories in the language of individuals experiencing depression. The DepressionEmo dataset [13] provides an operational taxonomy of eight emotions annotated on Reddit posts: anger, cognitive dysfunction, emptiness, hopelessness, lone- liness, sadness, suicide intent, and worthlessness. Recent studies confirm that these emotions do not operate as in- dependent signals but tend to co-occur, forming a latent construct attributable to depressive distress [13, 25]. 3 Methodology 3.1 Depressive Severity Index We propose a composite depressive severity index that ex- ploits the emotion labels assigned by an LLM to the eight affective dimensions described in Section 2.3. Each emotion is treated as a binary variable (present = 1, absent = 0) and multiplied by a weightw i proportional to its clinical relevance. The resulting index is: S = 1· anger + 1· cog_dysfunction + 1· emptiness + 2· hopelessness + 1· loneliness + 1· sadness + 3· suicide_intent + 2· worthlessness(1) The weight assignments are grounded in clinical consider- ations: sadness, loneliness, anger, emptiness, and cognitive dysfunction are indicators of distress but non-specific in isolation (w= 1); hopelessness and worthlessness signal advanced suffering and are associated with worse prognosis (w= 2); suicide intent is the highest-criticality indicator and demands immediate attention (w = 3) [18, 15]. The valueSis mapped onto four severity levels, modeled after the clinical thresholds of PHQ-9 and BDI-I: • S = 0–1: minimal or absent depression; • S = 2–4: mild depression; • S = 5–6: moderate depression; • S≥ 7: severe depression (high alert). The theoretical maximum score isS= 13 (all emotions present). 3.2 Prompt Engineering Emotional analysis is performed via prompt engineering: each post is paired with a structured textual instruction that guides the LLM toward a consistent and machine- readable classification output. The base prompt for multi- label classification is: 2 Analyze the sentiment of the following comment from Reddit: "post". Classify which of the following emotions apply: emotions. Answer with a JSON object, with True or False for each emotion. wherepostis the comment text andemotionsis the list of eight emotion labels. To incorporate direct score computation, the prompt is extended as follows: Analyze the sentiment of the following comment from Reddit: "post". Classify which of the following emotions apply: emotions. Answer with a JSON object, with True or False for each emotion. Then, compute a severity score as follows: assign weights to emotions (suicide_intent=3, hopelessness=2, worthlessness=2, cognitive_dysfunction=1, sadness=1, emptiness=1, loneliness=1, anger=1). Return also the field "severity_score" with the sum of the weights for the emotions classified as True. An example of the model output is: "anger": false, "cognitive_dysfunction": true, "emptiness": false, "hopelessness": true, "loneliness": true, "sadness": true, "suicide_intent": false, "worthlessness": true, "severity_score": 7 All models are used in zero-shot mode: no additional examples are provided beyond the prompt instructions. This choice allows us to assess the intrinsic capabilities of LLMs without any domain-specific training [11, 12]. 4 Experiments We evaluated our approach across two distinct phases: a controlled benchmark and a large-scale, in-the-wild analysis. This section outlines the complete experimental framework used for these evaluations. First, we detail the datasets and experimental setup, which include the annotated Depres- sionEmo corpus and a custom collection of nearly 470,000 recent mental-health-related Reddit posts. Next, we present the evaluated models, introducing the nine locally hosted large language models and the fine-tuned baselines selected for comparison. Finally, we define the evaluation metrics, specifically the micro- and macro-averaged variants of preci- sion, recall, and F1-score used to comprehensively quantify multi-label classification performance. Gemma3 27B (LLM) Phi4 14B (LLM) BART (ML/DL) BERT (ML/DL) Model 0.0 0.2 0.4 0.6 0.8 1.0 Score 0.710 0.670 0.700 0.7200.720 0.760 0.810 0.770 0.700 0.690 0.760 0.740 Precision (macro) Recall (macro) F1-Score (macro) Figure 1: Comparison between LLMs (zero-shot) and fine- tuned models in terms of precision, recall, and F1-score. The two best models in each category are highlighted. LLMs trained on general-purpose data achieve competitive results relative to models specifically fine-tuned on depressive-domain data. 4.1 Datasets and Experimental Setup Controlled benchmark: DepressionEmo. The De- pressionEmo dataset [13] contains≈6,000 Reddit posts manually annotated with the eight emotions described in Section 2.3 under a multi-label scheme. We adopt the origi- nal 80/20 train/validation split proposed by the authors and evaluate all LLMs exclusively on the validation partition, without any fine-tuning. In-the-wild benchmark: Reddit 2024–2025. Using a custom-built web scraper, we collected posts from four mental-health-themed subreddits: r/anxiety, r/depression, r/depression_partners, and r/mentalhealth, the same com- munities used to construct the DepressionEmo dataset [13]. The collection spans January 2024 to May 2025, yielding a total of469,692posts (318,000 in 2024 and 151,692 in the first half of 2025). The per-subreddit distribution is reported in Table 1. 4.2 Evaluated Models In the controlled benchmark phase, we compare nine LLMs of varying sizes, run locally via the Ollama Table 1: Post distribution per subreddit in the in-the-wild dataset. Subreddit2024 2025 (H1)Total r/anxiety96,06842,660 138,728 r/depression50,276113,314 163,590 r/depr._partners1,5698782,447 r/mentalhealth107,04957,878 164,927 Total318,000151,692 469,692 3 framework, against literature baselines (SVM, Light- GBM, XGBoost, GAN-BERT, BERT, BART) fine- tuned on the 80% training split of DepressionEmo. The evaluated LLMs are:qwen3:0.6b,phi3:mini, gemma2:2b,llama3.2:3b,phi4:14b,mistral:7b, samantha-mistral:7b,qwen3:14b, andgemma3:27b. For the in-the-wild analysis, we employ the best-performing model identified in the benchmark, namely gemma3:27b. 4.3 Evaluation Metrics Multi-label classification performance is evaluated using precision, recall, and F1-score, computed in both the micro variant (globally aggregated counts across all classes) and the macro variant (arithmetic mean of per-class metrics). Their joint use enables assessment of both overall performance and behavior on less frequent classes, a critical consideration in the mental health domain [13]. 5 Results and Discussion 5.1 Controlled Benchmark Full results of the controlled benchmark are reported in Table 2 and visualized in Figure 1. Results show that the gap between zero-shot LLMs and fine-tuned models is modest:gemma3:27bachieves micro-F1 = 0.75 and macro-F1 = 0.70, matching fine-tuned GAN- BERT and falling only 0.05 F1 points below BERT and BART. Overall it’s the more balanced model among LLMs. The performance decrease relative to fine-tuned models is expected, yet remains small given the complete absence of domain-specific training. Per-model analysis.gemma3:27bis the best-performing LLM overall, exhibiting the lowest divergence between preci- sion (0.73) and recall (0.77), indicative of balanced behavior. qwen3:0.6bachieves very high precision (0.83) but very low recall (0.33), reflecting a conservative labeling strategy. Con- versely,samantha-mistral:7b, a psychologically-adapted variant of Mistral, maximizes recall (0.90) at the expense of precision (0.56), a trade-off consistent with prevention scenarios where the cost of false negatives is high, but with a precision particularly low. In fact, the F1 score it’s lower than other more powerful models (i.e., gemma3:27b). Clinical implications. In screening and prevention con- texts, minimizing false negatives (i.e., undetected critical cases) is the primary objective, making high recall a desirable property. Larger models generally show better precision– recall balance, suggesting that model scale is the primary predictor of performance, consistent with [12]. Advantage of LLMs over fine-tuned models. A key advantage is the ability of LLMs to operate without labeled Anger Brain Dysfunction Emptiness Hopelessness Loneliness Sadness Suicide Intent Worthlessness Anger Brain Dysfunction Emptiness Hopelessness Loneliness Sadness Suicide Intent Worthlessness 1.000.000.100.150.110.120.120.16 0.001.00-0.080.03-0.060.01-0.02-0.03 0.10-0.081.000.460.340.280.340.51 0.150.030.461.000.290.480.370.53 0.11-0.060.340.291.000.290.160.32 0.120.010.280.480.291.000.180.31 0.12-0.020.340.370.160.181.000.42 0.16-0.030.510.530.320.310.421.00 Emotion Co-occurrence Correlation Matrix (Spearman, p < 0.001) 1.00 0.75 0.50 0.25 0.00 0.25 0.50 0.75 1.00 Correlation Coefficient Figure 2: Spearman correlation matrix of detected emotions across the full in-the-wild dataset (p <0.001). Emotions belong- ing to the depressive core (hopelessness, worthlessness, sadness, emptiness) exhibit significant positive correlations (ρ= 0.28– 0.53). r/anxietyr/depressionr/depression_partnersr/mentalhealth Subreddit 0 20 40 60 80 100 Detection Rate (%) Emotion Detection Rates Across Mental Health Subreddits Anger Brain Dysfunction Emptiness Hopelessness Loneliness Sadness Suicide Intent Worthlessness Figure 3: Emotion detection rates across the four subreddits. In r/depression, sadness and hopelessness exceed 80–90% of posts; in r/anxiety, the same emotions appear at significantly lower frequencies. data: fine-tuned models require domain-specific annota- tions and risk becoming obsolete as online language evolves. LLMs, having been exposed to vast quantities of general text, generalize more naturally to novel expressions of depressive distress [11]. 5.2 In-the-Wild Analysis 5.2.1 Emotional Structure of Communities Figure 2 displays the Spearman correlation matrix of de- tected emotions. Hopelessness, worthlessness, sadness, and emptiness show moderate to high positive correlations (ρ≈0.28–0.53,p <0.001), indicating high co-occurrence within the same posts. This pattern supports the hypothe- sis that these emotions are observable manifestations of a shared latent construct corresponding to depressive distress. Conversely, anger and cognitive dysfunction show near- zero correlations with most other categories, suggesting weaker internal coherence with the depressive core and higher discriminant validity relative to the primary domain. 4 Table 2: Comparison of zero-shot LLMs and fine-tuned literature models on the DepressionEmo dataset (micro and macro precision, recall, F1-score). Best results per metric in bold.✓= fine-tuned;✗= zero-shot. MethodFTMicroMacro Prec. Rec. F1 Prec. Rec. F1 SVM [13]✓ 0.77 0.51 0.61 0.72 0.41 0.47 LightGBM [13]✓ 0.52 0.86 0.65 0.48 0.80 0.58 XGBoost [13]✓ 0.69 0.63 0.66 0.63 0.56 0.59 GAN-BERT [13]✓ 0.73 0.77 0.75 0.69 0.72 0.70 BERT [13]✓ 0.76 0.83 0.79 0.72 0.77 0.74 BART [13]✓ 0.74 0.86 0.80 0.70 0.81 0.76 qwen3:0.6b✗ 0.83 0.33 0.47 0.86 0.26 0.35 phi3:mini✗ 0.63 0.85 0.72 0.64 0.78 0.67 gemma2:2b✗ 0.58 0.87 0.70 0.61 0.84 0.65 llama3.2:3b✗ 0.65 0.83 0.73 0.63 0.77 0.67 phi4:14b✗ 0.68 0.82 0.74 0.67 0.76 0.69 mistral:7b✗ 0.66 0.84 0.74 0.65 0.76 0.68 samantha-mistral:7b✗ 0.56 0.90 0.69 0.56 0.86 0.65 qwen3:14b✗ 0.64 0.84 0.73 0.65 0.77 0.67 gemma3:27b✗ 0.73 0.77 0.75 0.71 0.72 0.70 0.02.55.07.510.012.5 Risk Score 0 10000 20000 30000 Number of Posts r/anxiety (N=138,728) Mean: 2.56 Median: 2.00 0.02.55.07.510.012.5 Risk Score 0 10000 20000 Number of Posts r/depression (N=163,590) Mean: 6.97 Median: 7.00 0.02.55.07.510.012.5 Risk Score 0 200 400 Number of Posts r/depression_partners (N=2,447) Mean: 4.83 Median: 5.00 0.02.55.07.510.012.5 Risk Score 0 5000 10000 15000 20000 Number of Posts r/mentalhealth (N=164,927) Mean: 5.07 Median: 5.00 Distribution of Risk Scores by Subreddit Figure 4: Distributions of risk scoresS(Eq. 1) per subreddit. In r/depression the distribution is shifted toward higher values, with mean and median nearly coinciding (S≈7); in r/anxiety the distribution is strongly concentrated at low values (median≈2). Figure 3 shows how emotional profiles vary substantially across communities. In r/depression, sadness and hope- lessness are detected in the vast majority of posts (often >80–90%), whereas in r/anxiety the same emotions are con- siderably less frequent, outlining a distinct affective profile consistent with the differential nature of the two disorders. 5.2.2 Risk Score Distributions Figures 4 and 5 demonstrate thatSeffectively discriminates between communities. In r/depression, the distribution is shifted toward high values (mean≈7, approximately 43% of posts≥7), suggesting a structurally elevated and homo- geneously distributed risk. In r/anxiety, the distribution is strongly left-skewed (median≈2, only≈3% of posts r/anxietyr/depressionr/depression_partnersr/mentalhealth Subreddit 0 2 4 6 8 10 12 Risk Score Distribution of Risk Scores by Subreddit (Box Plot) Median Mean Figure 5: Box plots of risk scores per subreddit. Significant differences emerge across communities; r/depression exhibits both a higher median and greater variability at the extreme values. ≥7), consistent with anxiety rather than depressive profiles. The subreddits r/depression_partners and r/mentalhealth exhibit intermediate profiles with greater variability. 5.2.3 High-Risk Post Analysis Figure 6 confirms that high values ofSare not attributable to random fluctuations, but reflect the systematic conver- gence of multiple critical affective indicators. In posts with S >7, suicide intent, which carries the maximum weight of 3, is markedly overrepresented relative to the global dis- tribution, validating the index’s ability to isolate the most critical cases. 5.2.4 Temporal Analysis Figures 7 and 8 reveal substantial temporal stability of risk scores throughout 2024–2025, with no marked discontinu- ities. This stability is a relevant property for a monitoring 5 AngerBrain DysfunctionEmptinessHopelessnessLonelinessSadnessSuicide IntentWorthlessness Emotion 0 20 40 60 80 100 Detection Rate (%) Comparison of Emotion Detection Rates All Posts High-Risk Posts (Score > 7) Figure 6: Comparison of emotion detection rates between the full post set and the high-risk subset (S >7). In high-risk posts, sadness and hopelessness are detected in nearly all cases; a marked increase in worthlessness, emptiness, and suicide intent is also observed. JanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJul Month 2 4 6 Average Risk Score Monthly Trend of Risk Scores - 2024 and 2025 r/anxiety r/depression r/depression_partners r/mentalhealth Figure 7: Monthly average risk score from January 2024 to July 2025. Values remain stable over time per subreddit, with structural inter-community differences persisting throughout the observation period. A moderate upward trend is observable in the first half of 2025. system: it indicates that the model responds to structured linguistic variation rather than episodic noise. A moderate increase in mean scores during the first half of 2025 is observable in several subreddits. While causal inference is not possible, this trend may reflect an intensifica- tion of depressive language associated with broader social or contextual factors. This reading is consistent with the value of a longitudinal monitoring system capable of detecting drifts in collective language that may carry psychologically meaningful signals [14, 5]. 5.2.5 Limitations This work has several methodological limitations. First, the analysis relies exclusively on public text data and does not account for individual or clinical variables of the post authors, limiting the generalizability of estimates at the indi- vidual level. Second, emotion interpretation depends on the semantic capabilities of the LLM, which, however advanced, remain a simplification of the underlying psychological pro- cess. Finally, the self-referential nature of Reddit content introduces potential self-selection bias: the monitored com- munities tend to attract users with already-manifest distress, limiting the representativeness of the sample with respect to the general population. From a system perspective, the gap of approximately 0.05–0.06 F1 points relative to fine-tuned models leaves room for improvement via hybrid approaches (few labeled 20242025 Year 3 4 5 6 7 Average Risk Score Temporal Trend of Mental Health Risk Scores r/anxiety r/depression r/depression_partners r/mentalhealth Figure 8: Fine-grained temporal evolution of the mean risk score. The absence of erratic oscillations indicates that the system does not overreact to episodic language variation. examples for few-shot prompting) or lightweight fine-tuning (LoRA/QLoRA) on smaller models. Ethical considerations regarding privacy and informed consent require careful at- tention before deploying such tools in real-world applicative contexts. 6 Conclusions We have presented an LLM-based system for automated depression risk assessment in social media, centered on a clinically-grounded weighted severity index derived from eight emotionally relevant dimensions. Evaluation on the DepressionEmo benchmark demonstrates that large locally- run models, in particulargemma3:27b, achieve performance competitive with purpose-built fine-tuned models (micro-F1 = 0.75 vs. 0.80 for BART), operating in zero-shot mode without any domain-specific training. The in-the-wild analysis on 469,692 Reddit posts confirms the ecological validity of the approach: the system pro- duces coherent and interpretable risk profiles, successfully differentiating semantically distinct communities (e.g., r/de- pression vs. r/anxiety) and maintaining stable estimates over time. The proposed index enables automatic identifica- tion of high-risk posts through the systematic convergence of multiple critical affective indicators, providing a scalable psychological triage tool. The main contributions of this work are: (1) the defini- tion of a composite depressive severity index inspired by standardized clinical scales but adapted for automated text analysis; (2) the demonstration that medium-to-large LLMs, runnable locally without specialized hardware, can achieve meaningful performance in depressive emotion recognition; (3) the first large-scale longitudinal analysis of depression risk in major mental-health subreddits over the 2024–2025 period. Our findings suggest that LLMs, by virtue of their domain- agnostic training and ability to generalize to evolving linguis- tic expressions, represent a promising resource for large-scale psychological monitoring, with infrastructure costs substan- tially lower than proprietary API-based models. The pro- posed system is designed to complement rather than replace clinical judgment, serving as a first-pass filter in continuous monitoring scenarios. 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