README.md

---
configs:
- config_name: default
  data_files:
  - split: AskAcademia
    path: extracted/pairs/sub-AskAcademia.jsonl
  - split: AskBaking
    path: extracted/pairs/sub-AskBaking.jsonl
  - split: AskCulinary
    path: extracted/pairs/sub-AskCulinary.jsonl
  - split: AskDocs
    path: extracted/pairs/sub-AskDocs.jsonl
  - split: AskEngineers
    path: extracted/pairs/sub-AskEngineers.jsonl
  - split: AskHistorians
    path: extracted/pairs/sub-AskHistorians.jsonl
  - split: AskStatistics
    path: extracted/pairs/sub-AskStatistics.jsonl
  - split: Coffee
    path: extracted/pairs/sub-Coffee.jsonl
  - split: DIY
    path: extracted/pairs/sub-DIY.jsonl
  - split: German
    path: extracted/pairs/sub-German.jsonl
  - split: JapanTravel
    path: extracted/pairs/sub-JapanTravel.jsonl
  - split: LanguageTechnology
    path: extracted/pairs/sub-LanguageTechnology.jsonl
  - split: LearnJapanese
    path: extracted/pairs/sub-LearnJapanese.jsonl
  - split: Sewing
    path: extracted/pairs/sub-Sewing.jsonl
  - split: Shoestring
    path: extracted/pairs/sub-Shoestring.jsonl
  - split: askphilosophy
    path: extracted/pairs/sub-askphilosophy.jsonl
  - split: askscience
    path: extracted/pairs/sub-askscience.jsonl
  - split: bicycling
    path: extracted/pairs/sub-bicycling.jsonl
  - split: gardening
    path: extracted/pairs/sub-gardening.jsonl
  - split: golang
    path: extracted/pairs/sub-golang.jsonl
  - split: homeimprovement
    path: extracted/pairs/sub-homeimprovement.jsonl
  - split: houseplants
    path: extracted/pairs/sub-houseplants.jsonl
  - split: languagelearning
    path: extracted/pairs/sub-languagelearning.jsonl
  - split: learnjavascript
    path: extracted/pairs/sub-learnjavascript.jsonl
  - split: learnpython
    path: extracted/pairs/sub-learnpython.jsonl
  - split: rust
    path: extracted/pairs/sub-rust.jsonl
  - split: solotravel
    path: extracted/pairs/sub-solotravel.jsonl
  - split: tea
    path: extracted/pairs/sub-tea.jsonl
  - split: woodworking
    path: extracted/pairs/sub-woodworking.jsonl
---
# personalization-reddit

Per-subreddit `(query, preferred_answer)` pairs mined from Reddit using an
**OP-thanks-reply** heuristic: when the original poster (OP) replies to a
comment with thanks/gratitude, that parent comment is treated as their
preferred answer to their own question.

## Source

Raw post + comment dumps from the
[arctic_shift](https://github.com/ArthurHeitmann/arctic_shift) Pushshift
mirror, fetched per-subreddit (entire history through the fetch date) and
extracted with the pipeline in `may_15/reddit_pipeline/` of the
`personalization` repo.

Raw NDJSON dumps are kept locally and are not redistributed here.

## Splits

One split per subreddit; pick from the dropdown in the data viewer or load
programmatically:

```python
from datasets import load_dataset
ds = load_dataset("dipikakhullar/personalization-reddit", split="AskHistorians")
```

## Files

```
extracted/
  pairs/sub-<subreddit>.jsonl   # one (query, preferred_answer) per line
  stats/sub-<subreddit>.json    # funnel counts per subreddit
```

## Record schema (`pairs/sub-*.jsonl`)

| field | type | description |
|---|---|---|
| `user_id` | str | anonymized OP id (HMAC of Reddit username, see `anon.py`) |
| `timestamp` | str | post creation, ISO 8601 UTC |
| `subreddit` | str | source subreddit name |
| `query` | str | post title, with selftext appended if present |
| `preferred_answer` | str | body of the comment OP thanked (via parent of the thanks reply) |
| `top_comment` | str \| null | body of the highest-scoring non-OP comment (may equal preferred) |
| `op_metadata` | object | OP user fields captured at post time; see below |
| `answerer_metadata` | object | answerer user fields captured at comment time; see below |
| `metadata` | object | see below |

`op_metadata` sub-object (mirrors the multiturn dataset):

| field | type | description |
|---|---|---|
| `user_id` | str | same as top-level `user_id` |
| `author_flair_text` | str \| null | OP's flair text at post time |
| `author_flair_css_class` | str \| null | flair css class |
| `author_flair_type` | str \| null | e.g. "text", "richtext" |
| `author_flair_background_color` | str \| null | hex color |
| `author_flair_text_color` | str \| null | "light" / "dark" |

`answerer_metadata` sub-object:

| field | type | description |
|---|---|---|
| `user_id` | str | same as `metadata.answerer_anon_id` |
| `author_flair_text` | str \| null | answerer's flair text at comment time |
| `author_flair_css_class` | str \| null | flair css class |

`metadata` sub-object:

| field | type | description |
|---|---|---|
| `post_id` | str | Reddit submission id |
| `post_score` | int | submission score at fetch time |
| `answer_comment_id` | str | comment id of the preferred answer |
| `answer_score` | int | preferred-answer score at fetch time |
| `answerer_anon_id` | str | anonymized author id of the preferred answer |
| `top_comment_id` | str \| null | comment id of the top-scoring non-OP comment |
| `top_comment_score` | int \| null | top comment score |
| `top_comment_anon_id` | str \| null | anonymized top-comment author id |
| `top_equals_preferred` | bool | whether the preferred answer is also the top comment |
| `thanks_reply_id` | str | OP's thanks-reply comment id (the signal that triggered the pair) |
| `thanks_reply_score` | int | score of OP's thanks reply |
| `thanks_reply_text` | str | body of OP's thanks reply |
| `thanks_reply_timestamp` | str | thanks reply creation, ISO 8601 UTC |

## Heuristic — OP thanks-reply

For each post that passes a question filter:
1. Find comments whose author == OP and whose body matches a "thanks"
   pattern (see `signals.py::is_thanks_reply`).
2. The parent of each such reply is recorded as a candidate "preferred answer".
3. Materialize each candidate into a pair, joining post metadata + answer body
   + thanks-reply context.

Bots and deleted/removed authors are filtered out before pair emission
(`signals.py`, `subreddits.py::BOT_AUTHORS`).

## Anonymization

Reddit usernames are hashed via HMAC-SHA256 with a per-run secret salt
(`anon.py::anon_user_id`) before being written. Post/comment ids and text
bodies are kept verbatim — content from public Reddit threads can still be
re-identified by searching the post id or quoting the body.

## Subreddits included in this snapshot

See `extracted/stats/` for the list and per-sub funnel counts
(`rs_records_scanned`, `keep_posts`, `thanks_refs`, `pairs_emitted`, etc.).