ReDepress: Modeling Depression Relapse on Social Media
- ReDepress is a cognitively informed dataset and framework focusing on detecting depression relapse from Reddit posts after a period of remission.
- It integrates four key cognitive constructs—attention bias, interpretation bias, memory bias, and rumination—to capture subtle behavioral shifts in language.
- Transformer-based temporal models enriched with these markers achieve a state-of-the-art user-level F1 score of 0.86 for relapse detection.
ReDepress is a cognitively informed dataset and modeling framework for depression relapse detection from social media, introduced as a Reddit-based benchmark centered on the period after remission rather than on generic depressed-versus-control classification (Agarwal et al., 22 Sep 2025). Its defining premise is that relapse detection is a distinct problem: both relapse and non-relapse users already have a history of depression, both may exhibit residual vulnerability, and the signal of renewed deterioration is therefore subtler than the signal separating depressed users from healthy controls. To address that distinction, ReDepress combines clinician annotation with four constructs drawn from cognitive theories of depression—attention bias, interpretation bias, memory bias, and rumination—and uses them in both descriptive analysis and predictive modeling. In the reported experiments, transformer-based temporal models enriched with these cognitive markers achieved an F1 of 0.86 on user-level relapse detection (Agarwal et al., 22 Sep 2025).
1. Clinical target and conceptual framing
ReDepress treats relapse detection as a longitudinal question: after a user reports a period of remission, can subsequent Reddit posts support classification into relapse or no relapse? The paper motivates this target by citing high recurrence risk in depression: about 50% of patients face relapse after one episode, rising to 80% after a second episode and 90% after a third. Within this framing, social media is positioned as a scalable, low-cost source of longitudinal self-expression, but one whose signals require more theory-guided interpretation than ordinary depression screening (Agarwal et al., 22 Sep 2025).
A central methodological decision is terminological simplification. Because the relapse literature does not fully agree on strict boundaries among episode, remission, recovery, relapse, and recurrence, ReDepress uses relapse as an umbrella term covering both relapse and recurrence, and remission as an umbrella term covering both remission and recovery. Operationally, a user is considered in remission when a post mentions feeling better and/or asymptomatic; a user is considered in relapse when a later post explicitly mentions the return of symptoms and feeling worse than before. This operationalization is crucial because it anchors the dataset in explicit self-disclosure rather than in generic affective drift (Agarwal et al., 22 Sep 2025).
The paper further argues that relapse detection is harder than standard depression detection because relapse and non-relapse users can remain linguistically similar. Both groups have prior depression, both may retain vulnerability, and both may discuss mental health. ReDepress therefore does not rely solely on broad text representations. Instead, it embeds the task in cognitive theory, with the claim that depressive relapse may become visible through changes in what users attend to, how they interpret ambiguity, how they recall experience, and whether they engage in brooding rather than reflective processing (Agarwal et al., 22 Sep 2025).
2. Dataset construction and expert annotation
The dataset is built from Reddit dumps from 2012 to 2022. Candidate users were required to have posted at least once in r/depression and to have between 100 and 500 total posts. Self-reported depression diagnosis was first filtered using an expanded version of the SMHD diagnosis regex patterns, augmented with GPT-4o assistance. A multistage LLM-assisted pipeline then refined the pool: Llama 3.2 3B identified mental-health-related content at lower computational cost, and Llama 3.1 70B was used for higher-accuracy filtering and extraction of remission- and relapse-related evidence. For each retained user, the post identified as remission was treated as the starting post, and subsequent posts were examined for up to one and a half years for explicit relapse evidence (Agarwal et al., 22 Sep 2025).
This process yielded 208 user timelines for manual review. Final annotation was performed by three clinical psychologists, who labeled both user-level remission/relapse status and post-level cognitive dimensions. Majority vote determined the user label: if at least 2 of 3 annotators marked any post of a user as relapse, that user was labeled relapse; otherwise no relapse. A further exclusion rule removed users whose first couple of posts were not identified as remission. After annotation, 4 users remained inconclusive, leaving a final dataset of 204 users, comprising 83 relapse and 121 no-relapse cases. Only the timeline from remission onward is included; the earlier portion from diagnosis to remission is used for sample selection but is excluded from the final released data (Agarwal et al., 22 Sep 2025).
The dataset is explicitly user-level and longitudinal in organization, but its post counts remain modest. Relapse users contribute between 4 and 37 posts, with an average of 15.44; no-relapse users contribute between 3 and 35 posts, with an average of 11.24. The paper presents this as a clinically validated social-media dataset focused specifically on relapse rather than on generic depression. It also reports agreement statistics that distinguish between fine-grained cognitive annotation and user-level outcome labeling. Full-agreement Fleiss’ kappa values are modest for post-level constructs—0.39 for attention bias, 0.18 for memory bias, 0.29 for interpretation bias, and 0.17 for rumination—while user-level relapse/no-relapse labeling reaches 0.76. Majority-agreement kappas are much higher: 0.97, 0.98, 0.99, 0.92, and 0.99, respectively. The contrast indicates that cognitive labels are nuanced and subjective, whereas the final relapse distinction is substantially more stable (Agarwal et al., 22 Sep 2025).
3. Cognitive schema and feature representation
ReDepress operationalizes four constructs from cognitive theories of depression at the post level, using a dominant label approach in which annotators assign the single most prominent label for each dimension. These labels are not auxiliary metadata; they are the organizing structure of the framework, used for corpus analysis, handcrafted feature aggregation, and multimodal input construction for sequence models (Agarwal et al., 22 Sep 2025).
| Construct | Labels | Numeric encoding |
|---|---|---|
| Memory bias | Positive, Negative, No bias | $1, -1, 0$ |
| Attention bias | Positive, Negative, No bias | $1, -1, 0$ |
| Interpretation bias | Positive, Negative, No bias | $1, -1, 0$ |
| Rumination | Reflection, Brooding, No rumination | $1, -1, 0$ |
The definitions are clinically specific. Memory bias distinguishes happy or hopeful recall from sad or pessimistic recall. Attention bias captures selective focus on positive or negative aspects of present experience. Interpretation bias tracks optimistic versus pessimistic interpretation of ambiguous situations. Rumination separates maladaptive brooding from more constructive reflection. At the user level, these post-level values are aggregated by mean, median, minimum, and maximum, producing feature vectors of dimension 4, 8, 12, or 16 depending on the aggregation combination used (Agarwal et al., 22 Sep 2025).
This design gives ReDepress a hybrid ontology: the raw material is free-form Reddit text, but the analytical vocabulary is cognitive rather than merely topical or lexical. The paper argues that these constructs are not interchangeable proxies for generic negativity. Correlation analysis at the user level shows moderate positive associations—attention bias vs interpretation bias: , attention bias vs rumination: , interpretation bias vs rumination: , and memory bias vs attention bias: —but no correlation exceeds 0.80. This is presented as evidence that the four dimensions are related yet non-redundant (Agarwal et al., 22 Sep 2025).
4. Modeling pipeline
ReDepress evaluates three modeling layers. The first comprises non-neural user-level classifiers trained on aggregated cognitive markers. The paper evaluates Random Forest, XGBoost, Logistic Regression, Gradient Boosting, SVM, KNN, and a Neural Network (MLP). Class imbalance is handled by trying none, random oversampling, random undersampling, and SMOTE. Model selection uses 5-fold cross-validation on 80% of the data, followed by evaluation on the remaining 20% with Accuracy, Precision, Recall, and F1-score (Agarwal et al., 22 Sep 2025).
The second layer is a family of transformer-based temporal sequence models that operate over user timelines from remission onward. Each post is embedded using MentalBERT, MentalRoBERTa, or MPNet, and the resulting chronological post sequence is fed into a transformer encoder. The final valid hidden state is used for binary relapse classification. Three input constructions are compared: No CM, using only the pretrained post embedding; CM, using Embedding(<post>) + <Cognitive Markers>; and CM-Emb, using Embedding(<post> + <Cognitive Markers>). The paper does not print attention equations or a loss function, but the architecture is clearly described as a post-sequence transformer rather than a token-level encoder over concatenated user text (Agarwal et al., 22 Sep 2025).
The third layer evaluates whether the cognitive labels themselves can be automated. Separate three-class classifiers are trained for attention bias, interpretation bias, memory bias, and rumination using bert-base-uncased, clinicalbert, mentalbert, and mentalroberta. In addition, zero-shot LLM baselines are tested with Gemma-3-27B, Llama-3.1-70B, and Qwen-2-72B. This is important because ReDepress uses expert cognitive labels as a central resource, but also treats their partial automation as necessary for any larger-scale extension (Agarwal et al., 22 Sep 2025).
5. Empirical findings
The first empirical result is inferential rather than predictive: all four cognitive markers differ significantly between relapse and no-relapse groups. Because Shapiro–Wilk tests showed non-normality—attention bias: stat 0.967189, ; interpretation bias: stat 0.963059, ; memory bias: stat 0.963968, $1, -1, 0$0; rumination: stat 0.983268, $1, -1, 0$1—the paper used Mann–Whitney U tests. The resulting p-values were all highly significant: attention bias: $1, -1, 0$2, interpretation bias: $1, -1, 0$3, memory bias: $1, -1, 0$4, and rumination: $1, -1, 0$5. The authors interpret this as evidence that relapse users exhibit more negative attention, interpretation, and memory biases, together with higher brooding rumination (Agarwal et al., 22 Sep 2025).
Predictively, aggregated cognitive markers alone are informative but not sufficient for best performance. Among the classical user-level models, the strongest result comes from GradientBoosting, using mean+min+max+median aggregation with SMOTE, which reaches Accuracy 0.80, Precision 0.74, Recall 0.82, and F1 0.78. Other classical models cluster below this level: for example, RandomForest reaches 0.71 / 0.59 / 0.94 / 0.73, and Neural Network reaches 0.78 / 0.70 / 0.82 / 0.76. These results establish that the cognitive markers are predictive in their own right, but they also set a ceiling that later sequence models exceed (Agarwal et al., 22 Sep 2025).
The best results come from temporal transformers. For MPNet, the text-only baseline (No CM) reaches Accuracy 0.86, Precision 0.86, Recall 0.86, F1 0.81. Adding cognitive markers improves performance sharply: CM reaches 0.88 / 0.88 / 0.88 / 0.86, and CM-Emb reaches 0.88 / 0.89 / 0.88 / 0.86. The paper identifies this as the strongest overall configuration, and the improvement from 0.81 to 0.86 F1 is its clearest evidence that explicit cognitive structure adds value beyond chronological text embeddings alone. MentalBERT also improves when cognitive markers are added, from F1 0.80 to 0.82, whereas MentalRoBERTa benefits less consistently, leading the authors to note that mental-health-adapted encoders are not automatically optimal for relapse detection (Agarwal et al., 22 Sep 2025).
Ablation-style analyses show that no single cognitive dimension exhausts the signal. In the information imbalance analysis, Rumination (Mean) has $1, -1, 0$6 and Interpretation Bias (Mean) has $1, -1, 0$7, marking them as uniquely informative. In the model-based F1 ablation with the best GradientBoosting classifier, the largest F1 drops come from removing Memory Bias (Mean) (0.159), Attention Bias (Max) (0.111), and Rumination (Median) (0.111). This pattern supports the paper’s broader claim that all four constructs matter, but in different analytic senses: some separate the groups most cleanly, others contribute more uniquely, and others matter most to a particular classifier (Agarwal et al., 22 Sep 2025).
The post-level cognitive labelers are also nontrivial but workable. Best supervised F1 scores are 0.73 for attention bias with MentalBERT, 0.89 for interpretation bias with MentalRoBERTa, 0.82 for memory bias with MentalRoBERTa, and 0.76 for rumination with MentalRoBERTa. Zero-shot LLMs are substantially weaker: best F1 scores are 0.66 for attention bias, 0.33 for interpretation bias, 0.47 for memory bias, and 0.57 for rumination. ReDepress therefore treats expert annotation as a substantive necessity rather than something off-the-shelf prompting can reproduce reliably (Agarwal et al., 22 Sep 2025).
6. Position within the broader literature
ReDepress occupies a distinct position within depression-related NLP because it is organized around relapse after remission, not generic depression status, symptom severity, or symptom mention. This contrasts with Reddit systems such as “Cordyceps@LT-EDI: Depression Detection with Reddit and Self-training”, which formulate a three-way classification problem over low, moderate, and severe depression in Reddit posts and use balanced pseudo-label selection from unlabeled Reddit data (Ninalga, 2023). ReDepress also differs from “ReDSM5: A Reddit Dataset for DSM-5 Depression Detection”, which grounds long-form Reddit posts in the nine DSM-5 symptoms with sentence-level expert rationales. ReDSM5 is symptom-specific and explanation-oriented, whereas ReDepress is timeline-oriented and centered on recurrence risk after an explicit remission anchor (Bao et al., 5 Aug 2025).
It also differs from explainability work in clinical interview settings. “Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation” introduces RED, which retrieves PHQ-8-aligned evidence from transcripts and augments it with social intelligence knowledge for grounded explanation (Zhang et al., 3 Mar 2025). ReDepress, by contrast, does not retrieve evidence from interviews or knowledge graphs; its explanatory structure comes from post-level annotation of cognitive distortions and their aggregation over time. In that sense, ReDepress is less retrieval-based and more explicitly theory-parameterized.
The closest conceptual neighbor is “An Ambient Intelligence-based Approach For Longitudinal Monitoring of Verbal and Vocal Depression Symptoms”, which frames relapse as similarity between a subject’s speech encoding and depressed-reference speech in a Siamese architecture (Othmani et al., 2023). That work is relapse-aware in intention but uses DAIC-WOZ as a proxy dataset without true remission-to-relapse labels. ReDepress differs by supplying an actual social-media corpus explicitly annotated for remission and relapse transitions, which makes it a stronger benchmark for relapse detection as such rather than for depression-state similarity.
7. Limitations, ethics, and open problems
The paper is explicit that ReDepress does not resolve relapse detection as a clinical problem. The most immediate limitation is annotation subjectivity, reflected in the low full-agreement kappas for post-level cognitive labels. The second is external validity: the corpus is entirely Reddit-based, and Reddit users are not representative of all people with depression. The third is temporal granularity: timestamps indicate when posts were written, not when a relapse clinically began, and users post irregularly. The fourth is self-disclosure incompleteness: remission and relapse are only observable when users explicitly mention them, so the dataset inevitably reflects disclosure behavior as well as psychopathology (Agarwal et al., 22 Sep 2025).
Ethically, ReDepress is cautious but does not eliminate the core risks of mental-health inference from public text. The dataset is anonymized by mapping author names to unique numbers, and access is described as upon request under a user agreement. Even so, the paper explicitly flags the risk of identifying or labeling vulnerable individuals and emphasizes privacy safeguards and responsible use. Its framing is that automated systems should support monitoring and research, not function as clinical diagnosis or as unconsented surveillance (Agarwal et al., 22 Sep 2025).
The main open problems follow directly from these limits. The paper proposes expansion to larger and more diverse populations, addition of multimodal signals such as behavioral and physiological data, and development of finer-grained temporal models that capture both short-term fluctuations and longer cognitive drift. It also points toward personalized models that adapt to individual baselines rather than applying a single global decision structure. In that respect, ReDepress is best understood not as a finished relapse detector but as a benchmark and framework that formalizes relapse as a distinct computational problem and supplies a cognitively interpretable path for modeling it (Agarwal et al., 22 Sep 2025).