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 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:

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.).