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---
dataset_info:
features:
- name: subreddit
dtype: string
- name: post_id
dtype: string
- name: sentence_range
dtype: string
- name: text
dtype: string
- name: id
dtype: int64
- name: label
dtype: int64
- name: confidence
dtype: float64
- name: social_timestamp
dtype: int64
- name: social_karma
dtype: int64
- name: syntax_ari
dtype: float64
- name: lex_liwc_WC
dtype: int64
- name: lex_liwc_Analytic
dtype: float64
- name: lex_liwc_Clout
dtype: float64
- name: lex_liwc_Authentic
dtype: float64
- name: lex_liwc_Tone
dtype: float64
- name: lex_liwc_WPS
dtype: float64
- name: lex_liwc_Sixltr
dtype: float64
- name: lex_liwc_Dic
dtype: float64
- name: lex_liwc_function
dtype: float64
- name: lex_liwc_pronoun
dtype: float64
- name: lex_liwc_ppron
dtype: float64
- name: lex_liwc_i
dtype: float64
- name: lex_liwc_we
dtype: float64
- name: lex_liwc_you
dtype: float64
- name: lex_liwc_shehe
dtype: float64
- name: lex_liwc_they
dtype: float64
- name: lex_liwc_ipron
dtype: float64
- name: lex_liwc_article
dtype: float64
- name: lex_liwc_prep
dtype: float64
- name: lex_liwc_auxverb
dtype: float64
- name: lex_liwc_adverb
dtype: float64
- name: lex_liwc_conj
dtype: float64
- name: lex_liwc_negate
dtype: float64
- name: lex_liwc_verb
dtype: float64
- name: lex_liwc_adj
dtype: float64
- name: lex_liwc_compare
dtype: float64
- name: lex_liwc_interrog
dtype: float64
- name: lex_liwc_number
dtype: float64
- name: lex_liwc_quant
dtype: float64
- name: lex_liwc_affect
dtype: float64
- name: lex_liwc_posemo
dtype: float64
- name: lex_liwc_negemo
dtype: float64
- name: lex_liwc_anx
dtype: float64
- name: lex_liwc_anger
dtype: float64
- name: lex_liwc_sad
dtype: float64
- name: lex_liwc_social
dtype: float64
- name: lex_liwc_family
dtype: float64
- name: lex_liwc_friend
dtype: float64
- name: lex_liwc_female
dtype: float64
- name: lex_liwc_male
dtype: float64
- name: lex_liwc_cogproc
dtype: float64
- name: lex_liwc_insight
dtype: float64
- name: lex_liwc_cause
dtype: float64
- name: lex_liwc_discrep
dtype: float64
- name: lex_liwc_tentat
dtype: float64
- name: lex_liwc_certain
dtype: float64
- name: lex_liwc_differ
dtype: float64
- name: lex_liwc_percept
dtype: float64
- name: lex_liwc_see
dtype: float64
- name: lex_liwc_hear
dtype: float64
- name: lex_liwc_feel
dtype: float64
- name: lex_liwc_bio
dtype: float64
- name: lex_liwc_body
dtype: float64
- name: lex_liwc_health
dtype: float64
- name: lex_liwc_sexual
dtype: float64
- name: lex_liwc_ingest
dtype: float64
- name: lex_liwc_drives
dtype: float64
- name: lex_liwc_affiliation
dtype: float64
- name: lex_liwc_achieve
dtype: float64
- name: lex_liwc_power
dtype: float64
- name: lex_liwc_reward
dtype: float64
- name: lex_liwc_risk
dtype: float64
- name: lex_liwc_focuspast
dtype: float64
- name: lex_liwc_focuspresent
dtype: float64
- name: lex_liwc_focusfuture
dtype: float64
- name: lex_liwc_relativ
dtype: float64
- name: lex_liwc_motion
dtype: float64
- name: lex_liwc_space
dtype: float64
- name: lex_liwc_time
dtype: float64
- name: lex_liwc_work
dtype: float64
- name: lex_liwc_leisure
dtype: float64
- name: lex_liwc_home
dtype: float64
- name: lex_liwc_money
dtype: float64
- name: lex_liwc_relig
dtype: float64
- name: lex_liwc_death
dtype: float64
- name: lex_liwc_informal
dtype: float64
- name: lex_liwc_swear
dtype: float64
- name: lex_liwc_netspeak
dtype: float64
- name: lex_liwc_assent
dtype: float64
- name: lex_liwc_nonflu
dtype: float64
- name: lex_liwc_filler
dtype: float64
- name: lex_liwc_AllPunc
dtype: float64
- name: lex_liwc_Period
dtype: float64
- name: lex_liwc_Comma
dtype: float64
- name: lex_liwc_Colon
dtype: float64
- name: lex_liwc_SemiC
dtype: float64
- name: lex_liwc_QMark
dtype: float64
- name: lex_liwc_Exclam
dtype: float64
- name: lex_liwc_Dash
dtype: float64
- name: lex_liwc_Quote
dtype: float64
- name: lex_liwc_Apostro
dtype: float64
- name: lex_liwc_Parenth
dtype: float64
- name: lex_liwc_OtherP
dtype: float64
- name: lex_dal_max_pleasantness
dtype: float64
- name: lex_dal_max_activation
dtype: float64
- name: lex_dal_max_imagery
dtype: float64
- name: lex_dal_min_pleasantness
dtype: float64
- name: lex_dal_min_activation
dtype: float64
- name: lex_dal_min_imagery
dtype: float64
- name: lex_dal_avg_activation
dtype: float64
- name: lex_dal_avg_imagery
dtype: float64
- name: lex_dal_avg_pleasantness
dtype: float64
- name: social_upvote_ratio
dtype: float64
- name: social_num_comments
dtype: int64
- name: syntax_fk_grade
dtype: float64
- name: sentiment
dtype: float64
splits:
- name: train
num_bytes: 3929762
num_examples: 2838
- name: test
num_bytes: 988933
num_examples: 715
download_size: 2297873
dataset_size: 4918695
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- stress
- social-media
- reddit
pretty_name: 'Dreaddit: A Reddit Dataset for Stress Analysis in Social Media'
size_categories:
- 1K<n<10K
language:
- en
---
# Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
Consists of 190K posts from five different categories of Reddit communities.
## Citation
```
@inproceedings{turcan-mckeown-2019-dreaddit,
title = "{D}readdit: A {R}eddit Dataset for Stress Analysis in Social Media",
author = "Turcan, Elsbeth and
McKeown, Kathy",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6213/",
doi = "10.18653/v1/D19-6213",
pages = "97--107",
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."
}
``` |