Pith. sign in

REVIEW 5 cited by

Dreaddit: A Reddit Dataset for Stress Analysis in Social Media

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1911.00133 v1 pith:WTA3TG54 submitted 2019-10-31 cs.CL

Dreaddit: A Reddit Dataset for Stress Analysis in Social Media

classification cs.CL
keywords stressdatadatasetdomainsdreadditidentificationmediaposts
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Stress is a nigh-universal human experience, particularly in the online world. While stress can be a motivator, too much stress is associated with many negative health outcomes, making its identification useful across a range of domains. However, existing computational research typically only studies stress in domains such as speech, or in short genres such as Twitter. We present Dreaddit, a new text corpus of lengthy multi-domain social media data for the identification of stress. Our dataset consists of 190K posts from five different categories of Reddit communities; we additionally label 3.5K total segments taken from 3K posts using Amazon Mechanical Turk. We present preliminary supervised learning methods for identifying stress, both neural and traditional, and analyze the complexity and diversity of the data and characteristics of each category.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A Survey of Large Language Models for Perception and Measurement of Human Psychology

    cs.CY 2026-05 unverdicted novelty 5.0

    A survey proposing a three-pillar framework to evaluate LLMs as tools for measuring latent psychological constructs and reviewing applications in personality and mental health.

  2. Beyond Semantics: An Evidential Reasoning-Aware Multi-View Learning Framework for Trustworthy Mental Health Prediction

    cs.CL 2026-05 unverdicted novelty 4.0

    A multi-view evidential framework combines semantic and reasoning information to improve accuracy and provide trustworthy uncertainty estimates for mental health prediction on text data.

  3. User Perceptions of an LLM-Based Chatbot for Cognitive Reappraisal of Stress: Feasibility Study

    cs.HC 2026-01 conditional novelty 4.0

    A GPT-4o chatbot guiding employees through an 11-step reappraisal script was associated with small short-term reductions in self-reported stress and improved stress mindset in an uncontrolled feasibility study.

  4. Scaling behavior of large language models in emotional safety classification across sizes and tasks

    cs.CL 2025-09 conditional novelty 4.0

    Fine-tuning a 1B LLaMA model on a synthetic emotional-safety benchmark matches or beats 70B few-shot performance and a BERT baseline on three high-data categories, using under 2GB VRAM.

  5. Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review

    cs.AI 2025-10 conditional novelty 1.0

    This paper is a survey: it organizes existing foundation-model work in neuroscience into five application domains and lists public datasets, without presenting new experiments.