Datasets:
Tasks:
Text Classification
Languages:
English
Size:
1K<n<10K
Tags:
sentiment-analysis
mental-health
psychology
safety-alignment
crisis-detection
suicide-prevention
License:
| license: cc-by-nc-sa-4.0 | |
| task_categories: | |
| - text-classification | |
| language: | |
| - en | |
| tags: | |
| - sentiment-analysis | |
| - mental-health | |
| - psychology | |
| - safety-alignment | |
| - crisis-detection | |
| - suicide-prevention | |
| pretty_name: "HiddenSignals: Implicit Suicidal Ideation Dataset" | |
| size_categories: | |
| - "1K<n<10K" | |
| # ⚠️ Content Warning: High-Risk Mental Health Triggers | |
| **This dataset contains text related to suicidality, self-harm, and acute psychological distress.** It is intended solely for the purpose of training safety models and researching crisis intervention. Reader discretion is advised. | |
| --- | |
| ## Dataset Description | |
| **HiddenSignals-v1** is a specialized corpus designed to address the "Clinical Gap" in current AI safety models. While standard datasets focus on explicit clinical terminology (e.g., *"I want to commit suicide"*), this dataset aggregates **implicit, slang-based, and evasive distress signals** (e.g., *"I'm checking out,"* *"sewerslide,"* *"buying a ticket to Switzerland"*). | |
| The data is collected via **MindBridge**, an anonymous peer-support platform, and annotated by a team of clinical psychology researchers using the **Columbia-Suicide Severity Rating Scale (C-SSRS)**. | |
| * **Curated by:** MindBridge Research Lab | |
| * **Funded by:** [Proposed] OpenAI AI Mental Health Research Grant | |
| * **Language:** English (Internet Vernacular / Gen-Z Slang focus) | |
| * **License:** CC-BY-NC-SA 4.0 (Non-Commercial, Research Use Only) | |
| ### Research Goal | |
| To enable Large Language Models (LLMs) to detect "False Negatives" in crisis scenarios—identifying users who are at risk but are using algorithmic evasion techniques or sub-cultural slang to mask their intent. | |
| --- | |
| ## Dataset Structure | |
| ### Data Instances | |
| A typical data point consists of an anonymized chat segment, the specific slang term identified, and a verified clinical risk label. | |
| ```json | |
| { | |
| "id": "mb_7a8b9c_2025", | |
| "text": "honestly i think i'm just gonna minecraft myself tonight, i'm so cooked.", | |
| "context_tag": "gaming_metaphor", | |
| "detected_slang": ["minecraft myself", "cooked"], | |
| "standard_model_prediction": "neutral", | |
| "clinical_risk_label": 4, | |
| "risk_description": "Active Ideation with Method (c-ssrs-4)" | |
| } | |
| ``` | |
| ### Data Fields | |
| * `id`: Unique hash for the segment (k-anonymity enforced). | |
| * `text`: The raw text segment (PII stripped). | |
| * `context_tag`: The linguistic category (e.g., `gaming_metaphor`, `TikTok_slang`, `algorithmic_evasion`). | |
| * `standard_model_prediction`: The baseline output from GPT-4o-mini (used to highlight the gap). | |
| * `clinical_risk_label`: Integer (0-5) based on the C-SSRS scale. | |
| * **0:** No Risk / Venting | |
| * **1:** Wish to be Dead | |
| * **2:** Non-Specific Active Ideation | |
| * **3:** Active Ideation with Method (Implicit) | |
| * **4:** Active Ideation with Method (Explicit) | |
| * **5:** Active Ideation with Plan & Intent (Imminent) | |
| --- | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| Standard safety filters often over-censor vague sadness while missing high-risk slang. This dataset is curated specifically to capture the "Long Tail" of distress language that commercial models miss. | |
| ### Source Data | |
| * **Platform:** MindBridge Web App (Peer-to-Peer Chat). | |
| * **Collection Process:** Users opt-in to the "Research Contribution" mode. Conversations are filtered for high-sentiment velocity using **MentalBERT**. Segments containing potential slang are flagged for human review. | |
| ### Annotation Process | |
| All data is annotated by a two-person team: | |
| 1. **Primary Annotator:** Graduate Clinical Psychology Researcher (St. Petersburg State University). | |
| 2. **Validator:** Lead Investigator (Clinical Psychology Candidate). | |
| * *Inter-Rater Reliability:* Disagreements are resolved via a third-party consensus review. | |
| --- | |
| ## Ethics & Safety (Critical) | |
| ### PII & Anonymity | |
| We utilize a strict **K-Anonymity** pipeline. | |
| 1. **Pre-Processing:** All text is run through a Named Entity Recognition (NER) scrubber to redact names, locations, phone numbers, and emails. | |
| 2. **Unlinking:** Chat logs are stripped of IP addresses and user IDs before entering the dataset. | |
| ### Usage Restrictions | |
| * **Permitted Use:** Academic research, AI safety alignment, training crisis detection classifiers. | |
| * **Prohibited Use:** Generating toxic content, training "uncensored" models to mock mental health, or commercial insurance risk profiling. | |
| ### "Red Switch" Protocol | |
| During data collection, if a user exhibits "Imminent Risk" (Level 5), the data collection is immediately suspended, and the user is routed to emergency services via the MindBridge Safety Protocol. This data is **excluded** from the public dataset to protect the privacy of acute crisis events. | |
| --- | |
| ## Citation | |
| If you use this dataset, please cite the following: | |
| ```bibtex | |
| @dataset{mindbridge_hiddensignals_2025, | |
| author = {MindBridge Research Lab}, | |
| title = {HiddenSignals-v1: A Dataset of Implicit Suicidal Ideation}, | |
| year = {2025}, | |
| publisher = {Hugging Face}, | |
| url = {https://huggingface.co/datasets/mindbridge/hiddensignals} | |
| } | |
| ``` | |
| ``` |