README.md

---
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."
}
```