This dataset contains pseudonymized but re-identifiable social-media content about substance use. Access is reviewed manually and granted only for public-health / harm-reduction research. Requests without a verifiable institutional affiliation and a specific research purpose will be declined.

DATA USE AGREEMENT — read in full before requesting access.

This corpus contains Reddit posts and comments about drug use, including opioids. Author usernames are replaced by salted HMAC pseudonyms, but the dataset retains original Reddit post/comment identifiers (id, link_id, parent_id). Those identifiers can be resolved on reddit.com to the original content and, where not deleted, to the author's real account. Treat every record as identifiable personal data concerning health and potentially criminal conduct.

By requesting access you represent that you have read this agreement and agree to be bound by it.

1. Permitted use. Aggregate public-health, harm-reduction, epidemiological, computational-social-science or NLP research, conducted under the oversight of an IRB / research ethics committee (or a documented determination that such oversight is not required in your jurisdiction).

2. Prohibited uses. You will NOT: (a) attempt to re-identify, deanonymize, unmask or determine the real-world identity of any individual, including by resolving retained Reddit identifiers, cross-referencing external data, or querying any API or web service with dataset content; (b) contact, message, survey, recruit, profile, monitor or surveil any individual represented in the data; (c) use the data for law-enforcement, prosecutorial, immigration, insurance, credit, employment or any other adverse determination about an individual; (d) use it for commercial purposes, advertising, or targeting; (e) use it to facilitate the acquisition or distribution of controlled substances; (f) publish, present or otherwise disclose any verbatim quotation, username, pseudonymous user_id, Reddit identifier, or any other detail that could reasonably permit identification of an individual.

3. No redistribution. You will not republish, mirror, share, sublicense, post to any public repository or model hub, or otherwise transfer the data or any substantial derivative of it, in whole or in part, to any third party. Access is personal to you. Collaborators must request access individually. Models trained on this data must not be released if they can reproduce identifying content.

4. Security. You will store the data on access-controlled systems, restrict access to named personnel covered by this agreement, and not upload it to third-party services (including commercial LLM APIs) that may retain, train on, or disclose it.

5. Underlying rights. Content remains the intellectual property of its original authors and is subject to the Reddit User Agreement. This dataset is a research derivative; nothing here grants you rights in the underlying content. You are responsible for your own compliance with Reddit's terms and with all applicable law, including GDPR, HIPAA and equivalents.

6. Deletion. You will delete all copies upon completion of the stated research, upon withdrawal of access, or on request of the maintainers.

7. Incident reporting. You will report any accidental disclosure, re-identification, breach, or loss of control of the data to the maintainer within 72 hours.

8. Publication. Report results in aggregate. Paraphrase rather than quote. Cite the dataset and the upstream arctic_shift project.

9. Termination. Access may be revoked at any time, with or without cause. Breach terminates your rights immediately and obliges you to delete all copies.

10. No warranty. Provided "as is", without warranty of any kind. The maintainers accept no liability arising from your use. The data is a non-representative convenience sample and must not be used for clinical, diagnostic, or individual decision-making purposes.

OP-Reddit-User — per-user interaction structure

1,132,208 users from 37 drug-related subreddits, 2016-01-01 → 2026-07-27. Each row is one user: their profile, an interaction summary, and the structure of every thread they participated in.

This dataset contains no post text. It stores ids only, and is designed to be joined against OP-R1/OP-Reddit-Post to materialize the text on demand. That keeps this dataset small enough to download whole while the 47M-row text corpus stays remote.

Access. Gated — requests are reviewed manually and granted only for public-health / harm-reduction research under ethics oversight. See the Data Use Agreement on the access request form. IRB-gated human-subjects material.

Why user-level

OP-R1 classifies a user's role in the opioid ecosystem (buyer / seller / user) with a supporting rationale. The unit of prediction is therefore a person, not a post — and the evidence is a person's messages in context.

"How much for a G?" is meaningless alone and damning under a sourcing ad. So this dataset preserves who replied to whom, not just a flat list of a user's text.

Schema

Column	Type	Meaning
`user_id`	`string`	Salted-HMAC pseudonym. Joins to `OP-Reddit-Post.user_id`.
`n_items` / `n_posts` / `n_comments`	`int64`	Activity counts
`n_threads`	`int64`	Distinct threads participated in
`first_seen` / `last_seen`	`timestamp[ms, UTC]`	First/last activity
`active_days`	`int64`	Distinct days with activity
`subreddits`	`list<struct>`	`{name, n, first, last}` per community, desc by `n`
`interactions`	`struct`	See below
`threads`	`list<struct>`	The evidence — see below
`threads_truncated`	`bool`	True if the 500-thread cap applied
`role`	`string` (null)	Label slot — buyer / seller / user
`rationale`	`string` (null)	Label slot — free-text justification

`interactions`

Field	Meaning
`threads_started`	User authored the thread's anchor submission
`replies_to_others`	Replies the user made to other people
`replies_to_self`	Self-replies — characteristic of bumping one's own ad
`replies_received`	Replies the user received — the strongest relational signal
`distinct_repliers`	How many distinct people replied to them
`distinct_users_replied_to`	How many distinct people they replied to

`threads[]`

Field	Meaning
`link_id`	Thread id (`t3_…`)
`subreddit`	Community
`user_started` / `anchor_by_user`	Did this user author the thread root?
`anchor_id`	Bare id of the root submission — join key for its text
`items[]`	This user's own contributions, chronological

`threads[].items[]`

Field	Meaning
`id`	The record's Reddit id — join key for its text
`kind`	`post` or `comment`
`created_utc`	`timestamp[ms, UTC]`
`score`	Score at archival (nullable)
`parent_id`	What it replied to (`t3_…` post / `t1_…` comment)
`parent_kind`	`post` / `comment` / null
`parent_by_user`	Was the parent authored by this same user?
`reply_ids[]`	Ids of items replying to this item (capped at 20)

Materializing text

Ids join to OP-Reddit-Post, which ships lookup indexes so you can fetch just what you need rather than the whole 47M-row corpus:

See Retrieval helper below — the module ships inside this repo and is loaded with importlib.

Manually, the join keys are:

From this dataset	Fetch from OP-Reddit-Post
`threads[].items[].id`	the user's own message text
`threads[].anchor_id`	the thread's opening post
`threads[].items[].parent_id`	what they were replying to
`threads[].items[].reply_ids[]`	what people replied back

Retrieval helper (`retrieve.py`)

retrieve.py ships inside this dataset repo, so approved users get it with the data — no separate install, and it is always versioned against the index layout it was built for.

Note. The datasets library removed loading scripts (trust_remote_code no longer exists as of datasets v5), so load_dataset will not import this file. Download and load it explicitly:

import importlib.util, sys
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "OP-R1/OP-Reddit-Post", "retrieve.py", repo_type="dataset",
)

spec = importlib.util.spec_from_file_location("opr1_retrieve", path)
mod  = importlib.util.module_from_spec(spec)
sys.modules["opr1_retrieve"] = mod   # REQUIRED before exec_module (see below)
spec.loader.exec_module(mod)

r = mod.OpR1Retriever()                       # uses HF_TOKEN, or pass token=...
user = r.get_user("18a38e35a00f09e39207")     # profile + threads + text

⚠️ sys.modules[...] = mod is not optional. retrieve.py uses @dataclass(slots=True), whose class construction looks the module up in sys.modules. Omitting that line fails with:

AttributeError: 'NoneType' object has no attribute '__dict__'

Requirements

pip install huggingface_hub fsspec pyarrow

CLI

The same file runs standalone:

python retrieve.py --user-id 18a38e35a00f09e39207 --out user.json

It reports bytes fetched and the number of HTTP range requests on stderr, so you can confirm it is doing ranged reads rather than pulling whole files.

What it does

Looks the user up in index/user_index.parquet → file + contiguous row range
Reads only the row groups covering that range via HTTP range requests
Resolves anchor posts, parents and replies through index/id_index.parquet
Returns one nested structure with text filled in

Measured on a real user: 11.87 MB in 6 ranged reads (1.9 s) versus a 7.3 GB full download — about 615× less data.

Statistics

Metric	Value
Users	1,132,208
Items covered	39,762,155
Threads started (total)	3,025,121
Coverage	2016-01-01 → 2026-07-27
Subreddits	37

Activity is heavily skewed — plan splits and batching accordingly:

Percentile	Items/user	Threads/user	Replies received
median	8	5	8
p90	65	40	65
p99	453	—	—
max	41,571	26,108	28,577

59,681 users (5.3%) received zero replies — isolated posters, a useful negative class.

Inclusion rules and caps

--min-items 3: users with fewer than 3 records are excluded (1,063,112 dropped). The median user in the raw corpus has only 2 items, so this removes the long tail that carries no usable evidence.
Deleted / removed content is retained as thread structure. [deleted] and [removed] authors get no user row (they cannot be labeled), but their items still appear as other users' parent_id, anchor_id and reply_ids — the edge survives even when the body does not. This matters: moderation removes sourcing language hardest, so discarding tombstones would thin the seller class specifically.
Caps (recorded, not silent): reply_ids capped at 20 per item (101,693 lists affected); threads capped at 500 per user (4,321 users, flagged via threads_truncated).

De-identification

user_id is HMAC-SHA256(secret_salt, username)[:20], using the same salt as OP-Reddit-Post, so the two datasets join. The salt is never distributed.

⚠️ Reddit ids are retained in cleartext (id, link_id, parent_id, anchor_id). They resolve on reddit.com to the original content and, where not deleted, to the author's real account. No salt is required to do this. The pseudonym prevents casual identification, not a determined linkage attack. Treat every row as identifiable personal data concerning health and potentially criminal conduct, and see the Data Use Agreement.

Limitations

No labels yet. role and rationale are null placeholders.
Survivorship bias, most consequential here: moderation removes exactly the sourcing/transaction language distinguishing buyer from seller, so those signals are systematically under-represented.
Not a population sample — self-selected, skews young, Western, English.
Text is not included; anything requiring it needs OP-Reddit-Post access.

Provenance

Derived from arctic_shift Reddit archives via the OP-R1 pipeline (src/data/build_user_threads.py). Reddit content belongs to its original authors.

Maintainer: Tianyi (Billy) Ma · tma2@nd.edu · University of Notre Dame

Downloads last month: 7

Total file size:

1.96 GB

Request access to the OP-R1 Reddit corpus