Datasets:

OP-R1
/

OP-Reddit-User

---
license: other
license_name: reddit-user-content
license_link: https://redditinc.com/policies/user-agreement
language:
- en
task_categories:
- text-classification
tags:
- reddit
- public-health
- harm-reduction
- opioid
- substance-use
- pseudonymized
- user-level
pretty_name: "OP-Reddit-User — per-user interaction structure (2016–2026)"
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: "data/part-*.parquet"
extra_gated_heading: "Request access to the OP-R1 Reddit corpus"
extra_gated_button_content: "I agree to all terms above"
extra_gated_description: >-
  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.
extra_gated_prompt: >-
  **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.
extra_gated_fields:
  Full name: text
  Institutional email: text
  Institution / organization: text
  Role: 
    type: select
    options: ["Faculty / PI", "Postdoc", "PhD student", "Masters/Undergrad student", "Research staff", "Industry researcher", "Other"]
  Country: country
  Supervisor or PI (if student): text
  Describe your specific research question and intended use: text
  IRB / ethics review status:
    type: select
    options: ["Approved - I can provide the protocol number", "Submitted, pending approval", "Formal determination that review is not required", "Not applicable - please explain above"]
  I will not attempt to re-identify or deanonymize any individual: checkbox
  I will not contact, survey, profile or surveil anyone in the data: checkbox
  I will not use the data for law-enforcement or any adverse determination about an individual: checkbox
  I will not redistribute the data or share my access with others: checkbox
  I will not publish verbatim quotes, usernames or Reddit identifiers: checkbox
  I will not upload the data to third-party services that may retain or train on it: checkbox
  I will delete all copies when my research concludes or access is withdrawn: checkbox
  I agree to the full Data Use Agreement above: checkbox
---
# 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**](https://huggingface.co/datasets/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](https://github.com/Tianyi-Billy-Ma) 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_image_posts` | `int64` | How many of this user's posts carry an image. Counted over **all** their posts, not just the threads retained below. |
| `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) |
| `has_image` | Does this item carry an image? Always `false` for comments. |

### Image signal

`n_image_posts` (top level) and `threads[].items[].has_image` (per item) mark
image-bearing posts, matching the columns of the same name in
**OP-Reddit-Post**.

The two counters are deliberately scoped differently. `n_image_posts` covers
every post the user made, while the per-item flags only cover the threads
retained in `threads[]` — which is capped at 500 threads per user. For the
4,321 users who hit that cap, summing the item flags therefore undercounts.
Across the corpus that is 452,196 flagged items against 457,471 in the
counters. **Use `n_image_posts` for per-user totals; use the item flags to
locate specific posts.**

Note also that these 1,132,208 users account for 457,471 of the corpus's
656,932 image posts. The remaining 199,461 belong to users who fell below the
`--min-items` threshold or whose account was deleted, and so have no row here.

## 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:

```python
from src.data.retrieve import OpR1Retriever
r = OpR1Retriever()                          # OP-R1/OP-Reddit-Post + this dataset
user = r.get_user("18a38e35a00f09e39207")    # profile + threads + text, by ranged read
```

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 |

## 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](https://github.com/ArthurHeitmann/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

	---
	license: other
	license_name: reddit-user-content
	license_link: https://redditinc.com/policies/user-agreement
	language:
	- en
	task_categories:
	- text-classification
	tags:
	- reddit
	- public-health
	- harm-reduction
	- opioid
	- substance-use
	- pseudonymized
	- user-level
	pretty_name: "OP-Reddit-User — per-user interaction structure (2016–2026)"
	size_categories:
	- 1M<n<10M
	configs:
	- config_name: default
	data_files:
	- split: train
	path: "data/part-*.parquet"
	extra_gated_heading: "Request access to the OP-R1 Reddit corpus"
	extra_gated_button_content: "I agree to all terms above"
	extra_gated_description: >-
	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.
	extra_gated_prompt: >-
	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.
	extra_gated_fields:
	Full name: text
	Institutional email: text
	Institution / organization: text
	Role:
	type: select
	options: ["Faculty / PI", "Postdoc", "PhD student", "Masters/Undergrad student", "Research staff", "Industry researcher", "Other"]
	Country: country
	Supervisor or PI (if student): text
	Describe your specific research question and intended use: text
	IRB / ethics review status:
	type: select
	options: ["Approved - I can provide the protocol number", "Submitted, pending approval", "Formal determination that review is not required", "Not applicable - please explain above"]
	I will not attempt to re-identify or deanonymize any individual: checkbox
	I will not contact, survey, profile or surveil anyone in the data: checkbox
	I will not use the data for law-enforcement or any adverse determination about an individual: checkbox
	I will not redistribute the data or share my access with others: checkbox
	I will not publish verbatim quotes, usernames or Reddit identifiers: checkbox
	I will not upload the data to third-party services that may retain or train on it: checkbox
	I will delete all copies when my research concludes or access is withdrawn: checkbox
	I agree to the full Data Use Agreement above: checkbox
	---

	# 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](https://huggingface.co/datasets/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](https://github.com/Tianyi-Billy-Ma) 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_image_posts` \| `int64` \| How many of this user's posts carry an image. Counted over all their posts, not just the threads retained below. \|
	\| `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) \|
	\| `has_image` \| Does this item carry an image? Always `false` for comments. \|

	### Image signal

	`n_image_posts` (top level) and `threads[].items[].has_image` (per item) mark
	image-bearing posts, matching the columns of the same name in
	OP-Reddit-Post.

	The two counters are deliberately scoped differently. `n_image_posts` covers
	every post the user made, while the per-item flags only cover the threads
	retained in `threads[]` — which is capped at 500 threads per user. For the
	4,321 users who hit that cap, summing the item flags therefore undercounts.
	Across the corpus that is 452,196 flagged items against 457,471 in the
	counters. **Use `n_image_posts` for per-user totals; use the item flags to
	locate specific posts.**

	Note also that these 1,132,208 users account for 457,471 of the corpus's
	656,932 image posts. The remaining 199,461 belong to users who fell below the
	`--min-items` threshold or whose account was deleted, and so have no row here.

	## 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:

	```python
	from src.data.retrieve import OpR1Retriever

	r = OpR1Retriever() # OP-R1/OP-Reddit-Post + this dataset
	user = r.get_user("18a38e35a00f09e39207") # profile + threads + text, by ranged read
	```

	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 \|

	## 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](https://github.com/ArthurHeitmann/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