ISAAC is released under the project Data Use Agreement: https://github.com/BabakHemmatian/Illinois_Social_Attitudes/blob/main/Data_Use_Agreement.md By requesting access you agree to those terms and to cite the project in any resulting work.

ISAAC: Illinois Social Attitudes Aggregate Corpus

ISAAC is a large, labeled corpus of Reddit discourse about six identity-based social categories. This is the gated Hugging Face mirror; the project also offers plain HTTP direct downloads and a no-code web app.

Access: this dataset is gated. Request access (you'll be asked to accept the Data Use Agreement); approval grants load_dataset access with your HF token.

Configurations (social groups)

One config per social group, one file per month from 2007-01 to 2023-12 (204 months each, 1224 files in total).

Config	Rows	Size
`ability`	22,955,382	21.3 GB
`age`	280,203,455	198.3 GB
`race`	82,348,611	41.9 GB
`sexuality`	79,567,199	41.7 GB
`skin_tone`	39,628,662	23.7 GB
`weight`	22,357,610	14.0 GB
total	527,060,919	341 GB

from datasets import load_dataset

# one group, streaming (no full download):
ds = load_dataset("BabakScrapes/isaac-reddit", "race", split="train", streaming=True)
for row in ds.take(3):
    print(row["text"], row["score"])

# or materialize a group:
race = load_dataset("BabakScrapes/isaac-reddit", "race", split="train")

A single month can be pulled without touching the rest of a config:

from huggingface_hub import hf_hub_download
import pyarrow.parquet as pq

path = hf_hub_download("BabakScrapes/isaac-reddit", "race/ALL_2019-01.parquet",
                       repo_type="dataset")
tbl = pq.read_table(path, columns=["id", "text", "Moralization", "location"])

Schema

Each row is a Reddit submission or comment, in 59 columns:

Core: id, parent id, text, author (a pseudonymous numeric id), time (GMT), subreddit, score, type (comment or submission), and matched patterns: the keywords that flagged the post as potentially relevant to the group before AI-based pruning.
Moralization: Moralization, a binary AI estimate.
Sentiment: sentence counts from Stanza (Sentiment_Stanza_pos/neu/neg), Sentiment_Vader_compound, and Sentiment_TextBlob_Polarity / _Subjectivity.
Generalization: clauses and generalization_clause_labels (one clause per line, same order in both), plus counts and proportions for genericity, eventivity, boundedness, habituality and NA.
Emotion: <model_no>_<emotion> for three models × seven emotions. Model 1 is j-hartmann/emotion-english-distilroberta-base, model 2 is SamLowe/roberta-base-go_emotions, model 3 is tae898/emoberta-base. Models 1 and 3 are softmax probabilities summing to one across the seven categories; model 2 scores are independent per-category probabilities and need not sum to one.
Location: location, location_prob, contender_location, contender_location_prob: a user-level estimate, so every post by the same account carries the same label.

Two column names are easy to mistype: matched patterns and parent id contain a space, and Moralization is capitalized.

The authoritative, column-by-column data dictionary is variable_list.md in the project repository.

File format

Parquet, ZSTD-compressed, with ~100,000 rows per row group. These files decode to tables identical to the SNAPPY-compressed copies served from the direct-download endpoint: same schema, same row groups, same values, but they are about 40% smaller on the wire, so the bytes themselves are not interchangeable with those copies.

Confidence recalibration (location)

location_calibration_maps.json in this repository recalibrates the location_prob column. The location labeler is consistently under-confident, so the raw score ranks users well but understates the probability that a label is correct.

Six isotonic fits are provided, one per condition and geographic tier:

Map	Expected calibration error, before -> after
`standard/top`	0.0900 -> 0.0112
`standard/region`	0.1149 -> 0.0070
`standard/state`	0.5221 -> 0.0116
`masked/top`	0.1208 -> 0.0044
`masked/region`	0.1239 -> 0.0174
`masked/state`	0.3191 -> 0.0139

Isotonic regression is monotone, so recalibration never reorders users or changes any label. It changes only the interpretation of the score. The maps are fitted on a validation split of held-out authors and scored on a test split, so the improvement is out of sample.

Apply a map with linear interpolation; no ISAAC code is needed:

import json, numpy as np

maps = json.load(open('location_calibration_maps.json'))['maps']
m = maps['masked/top']            # condition/tier
calibrated = np.interp(raw_score, m['x'], m['y'])

Pick the tier from the label itself: a two-letter code is state, EUROPE/AMERICAS/ASIA_OCEANIA/AFRICA is region, US/NON_US is top, and UNK has no score. Use the masked maps unless you know the authors state their location explicitly; masked is the conservative choice and the closer analogue for corpus authors who never self-disclose. The file's own method, how_to_apply, choosing_a_condition and limitations fields document this alongside the knots.

Calibration holds in aggregate on the population the maps were fitted on, namely authors whose location was recoverable from explicit self-disclosure. Per-state and per-subgroup calibration were not assessed. The location model weights themselves are not distributed here; see the paper's Code Availability section.

Provenance & related access

Web app + direct download: https://isaac.psychology.illinois.edu/
Direct-download docs & manifest: https://isaac.psychology.illinois.edu/direct-download/
Python loader (isaac-data) and source: https://github.com/BabakHemmatian/Illinois_Social_Attitudes
Questions, access problems, or corpus errata: isaac.corpus.support@gmail.com

Citation

Please cite the ISAAC paper. One citation covers the whole project: the corpus, the pipeline, and every model. Please do not cite this dataset repository separately; keeping references in one place is what allows the project's citations to be found together.

@article{hemmatian2026isaac,
  author  = {Hemmatian, Babak and Hadjarab, Sarah and Chen, Jessica and Kurdi, Benedek},
  title   = {The {Illinois} Social Attitudes Aggregate Corpus ({ISAAC}): An Open Tool and Reproducible Pipeline for Analyzing Social Group Discourse at Scale},
  year    = {2026},
  journal = {arXiv},
  eprint  = {2609.27059},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  doi     = {10.48550/arXiv.2609.27059},
  url     = {https://arxiv.org/abs/2609.27059}
}

Data Use Agreement

Use of ISAAC is governed by the Data Use Agreement, which you accept when requesting access. Agreeing to cite the project in any resulting work is one of its terms.

Downloads last month: 59

Total file size:

341 GB

Paper for BabakScrapes/isaac-reddit

The Illinois Social Attitudes Aggregate Corpus (ISAAC): An Open Tool and Reproducible Pipeline for Analyzing Social Group Discourse at Scale

Paper • 2609.27059 • Published 7 days ago