ReDepress Dataset

The ReDepress Dataset originates from the paper
"ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media".

This dataset is designed to support research on detecting depression relapse using social media text.
It provides rich temporal user data and cognitive bias annotations, allowing systems to monitor conversational dynamics and make timely inferences.

Important Notice

• By requesting, accessing, downloading, or using this dataset, you acknowledge that you have read, understood, and agreed to comply fully with the terms specified in the Dataset Usage Agreement, including all conditions under Permitted Uses.
• Access to and use of the dataset are strictly conditional upon acceptance of the Dataset Usage Agreement.
• Any use of the dataset that does not comply with the Dataset Usage Agreement is prohibited.

Dataset Usage Agreement

Permitted Uses

• The information may only be used for research purposes.
• Portions of the data may be copyrighted and may also have commercial value as data. It must be used strictly for research purposes.
• Summaries, analyses, and interpretations of the linguistic properties of the information may be derived and published, provided that it is not possible to reconstruct the original information from these summaries.
• You may not attempt to identify the individuals whose texts are included in this dataset.
• You may not cross-reference individuals in this dataset with any other dataset or collection of data.
• You may not attempt to establish any form of contact with individuals represented in this dataset.
• You are not permitted to publish any portion of the dataset, such as example posts, other than summary statistics.
• You may not share the dataset with anyone else.
• You are granted the right to access the collection's content solely in the manner described in this agreement.
• You may not make unauthorized commercial use of, reproduce, prepare derivative works from, distribute copies of, perform, or publicly display the collection or any part of it.
• You may present research findings based on knowledge obtained from the collection, provided that such presentation remains within the limits of this agreement.
• Any scientific publication derived from the use of this collection must explicitly refer to and cite the following publication:
  • Agarwal, Aakash Kumar, Saprativa Bhattacharjee, Mauli Rastogi, Jemima S. Jacob, Biplab Banerjee, Rashmi Gupta, and Pushpak Bhattacharyya. "ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media." arXiv preprint arXiv:2509.17991 (2025).
• You shall not use results obtained through the use of the collection for profitable purposes, including advertisement, or for defamatory or slanderous purposes.
• If the copyright holders request that you discontinue use of the collection, or if your use is deemed to violate this agreement, you must immediately discontinue use and promptly delete the collection and all data derived from it from any computer or storage medium.

Copyright

• The copyright holders retain ownership and reserve all rights pertaining to the use and distribution of the information.
• Except as specifically permitted above and as necessary to use and maintain the integrity of the information on computers, the display, reproduction, transmission, distribution, or publication of the information is strictly prohibited.
• Violations of these copyright restrictions may result in legal liability.
• The copyright holders of the information contained in the collection include a wide variety of online Internet users.

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Overview

• Total Users: 204
• File Format: .parquet files (one per user)
• Ground Truth File: ground_truth_labels.csv
• agreement to access dataset: ReDepress_agreement.docx

Each user is assigned a binary label in the ground_truth_labels.csv file:

• 0 → No Relapse
• 1 → Relapse

The dataset contains two categories of users:

1. Relapsed Users
2. Non-relapsed Users

Each user file includes a chronological sequence of posts, containing the user’s own submissions.

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Data Structure

1. User Files (.parquet)

Each user file contains the following fields:

Column	Description
post_id	Unique string identifier for the post
author	Anonymous identifier of the user who created the post
date	ISO 8601 timestamp of post creation
selftext	Main content (body text) of the submission
title	Title summarizing the submission
avg_attention_bias	Mean of all three annotators’ attention bias scores
avg_interpretation_bias	Mean of all three annotators’ interpretation bias scores
avg_memory_bias	Mean of all three annotators’ memory bias scores
avg_rumination	Mean of all three annotators’ rumination bias scores
majority_attention_bias	Majority label for attention bias
majority_interpretation_bias	Majority label for interpretation bias
majority_memory_bias	Majority label for memory bias
majority_rumination	Majority label for rumination bias

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2. Label Mapping

The majority label for each cognitive bias was mapped to numerical values as follows:

Memory Bias

Label	Mapped Value
Positive	1
Negative	-1
No Bias	0

Attention Bias

Label	Mapped Value
Positive	1
Negative	-1
No Bias	0

Interpretation Bias

Label	Mapped Value
Positive	1
Negative	-1
No Bias	0

Rumination

Label	Mapped Value
Reflection	1
Brooding	-1
No Rumination	0

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Ground Truth Labels

The ground_truth_labels.csv file contains the final gold standard relapse annotations:

Column	Description
author	Anonymous user identifier
relapse_label	0 for no relapse, 1 for relapse

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Use Cases

Researchers can use this dataset to:

• Train and evaluate relapse detection models based on text data.
• Study cognitive bias patterns preceding depression relapse.
• Analyze temporal evolution of users’ social media language and behavior.

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Citation

If you use this dataset in your research, please cite:

ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media Aakash Kumar Agarwal, Saprativa Bhattacharjee, Mauli Rastogi, Jemima S. Jacob, Biplab Banerjee, Rashmi Gupta, and Pushpak Bhattacharyya. 2025. ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 34652–34670, Suzhou, China. Association for Computational Linguistics. https://aclanthology.org/2025.emnlp-main.1758/

BibTeX

@inproceedings{agarwal-etal-2025-redepress,
    title = "{R}e{D}epress: A Cognitive Framework for Detecting Depression Relapse from Social Media",
    author = "Agarwal, Aakash Kumar  and
      Bhattacharjee, Saprativa  and
      Rastogi, Mauli  and
      Jacob, Jemima S.  and
      Banerjee, Biplab  and
      Gupta, Rashmi  and
      Bhattacharyya, Pushpak",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1758/",
    pages = "34652--34670",
    ISBN = "979-8-89176-332-6",
    abstract = "Almost 50{\%} depression patients face the risk of going into relapse. The risk increases to 80{\%} after the second episode of depression. Although, depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored due to the lack of curated datasets and the difficulty of distinguishing relapse and non-relapse users. In this work, we present ReDepress, the first clinically validated social media dataset focused on relapse, comprising 204 Reddit users annotated by mental health professionals. Unlike prior approaches, our framework draws on cognitive theories of depression, incorporating constructs such as attention bias, interpretation bias, memory bias and rumination into both annotation and modeling. Through statistical analyses and machine learning experiments, we demonstrate that cognitive markers significantly differentiate relapse and non-relapse groups, and that models enriched with these features achieve competitive performance, with transformer-based temporal models attaining an F1 of 0.86. Our findings validate psychological theories in real-world textual data and underscore the potential of cognitive-informed computational methods for early relapse detection, paving the way for scalable, low-cost interventions in mental healthcare."
}

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Ethical Considerations

• All user data is anonymized to protect privacy.
• The dataset must be used solely for research purposes related to mental health, language, and computational social science.
• Researchers are encouraged to handle the data with sensitivity and adhere to ethical AI and mental health research guidelines.

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License

To access this dataset please fill the ReDepress_agreement.docx document and send an email to aakash.agarwal@iitb.ac.in

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Contact

For questions or collaboration inquiries, please refer to the contact information provided in the ReDepress paper.