---
title: 'ReDepress: Modeling Depression Relapse on Social Media'
url: https://www.emergentmind.com/topics/redepress
type: topic
---

# ReDepress: Modeling Depression Relapse on Social Media

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 [2509.17991]. 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 [2509.17991].

## 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 [2509.17991].

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 [2509.17991].

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** [2509.17991].

## 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 [2509.17991].

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 [2509.17991].

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 [2509.17991].

## 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 [2509.17991].

| 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 [2509.17991].

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: \(r = 0.69\)**, **attention bias vs rumination: \(r = 0.69\)**, **interpretation bias vs rumination: \(r = 0.65\)**, and **memory bias vs attention bias: \(r = 0.52\)**—but no correlation exceeds **0.80**. This is presented as evidence that the four dimensions are related yet non-redundant [2509.17991].

## 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** [2509.17991].

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 [2509.17991].

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 [2509.17991].

## 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, \(p = 0.000109\)**; **interpretation bias: stat 0.963059, \(p = 0.000036\)**; **memory bias: stat 0.963968, \(p = 0.000046\)**; **rumination: stat 0.983268, \(p = 0.015915\)**—the paper used **Mann–Whitney U** tests. The resulting p-values were all highly significant: **attention bias: \(p = 0.000000\)**, **interpretation bias: \(p = 0.000002\)**, **memory bias: \(p = 0.000212\)**, and **rumination: \(p = 0.000014\)**. The authors interpret this as evidence that relapse users exhibit **more negative attention, interpretation, and memory biases, together with higher brooding rumination** [2509.17991].

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 [2509.17991].

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 [2509.17991].

Ablation-style analyses show that no single cognitive dimension exhausts the signal. In the **information imbalance** analysis, **Rumination (Mean)** has \(\Delta II = +0.014\) and **Interpretation Bias (Mean)** has \(\Delta II = +0.008\), 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 [2509.17991].

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 [2509.17991].

## 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 [2310.01418]. 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 [2508.03399].

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 [2503.01315]. 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 [2308.08472]. 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 [2509.17991].

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 [2509.17991].

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 [2509.17991].

Source: https://www.emergentmind.com/topics/redepress