Datasetpublic

dipikakhullar/personalization-reddit-user-histories

personalization-reddit-user-histories Per-user chronological histories of answered questions across all subreddits. Derived from dipikakhullar/personalization-reddit: every (query, preferred_answer) pair a user authored as OP, grouped by user and sorted by time, slimmed to the four fields needed to model a user's timeline. Each record is one user. Users with a single interaction are dropped (a timeline needs more than one point). Selection: seen-the-top… See the full description on the dataset page: https://huggingface.co/datasets/dipikakhullar/personalization-reddit-user-histories.

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Dataset Card

personalization-reddit-user-histories

Per-user chronological histories of answered questions across all subreddits. Derived from dipikakhullar/personalization-reddit: every (query, preferred_answer) pair a user authored as OP, grouped by user and sorted by time, slimmed to the four fields needed to model a user's timeline.

Each record is one user. Users with a single interaction are dropped (a timeline needs more than one point).

Selection: seen-the-top, genuine-disagreement pairs

An OP often thanked more than one answer to the same post. We keep only the query-answer pairs where the OP had plausibly seen the top community answer before endorsing a different preferred answer. Concretely, a pair is kept only when all three hold:

—preferred_answer was created after the top community answer existed,
—the top community answer existed before the OP posted their thanks-reply,
—preferred_answer differs (by text) from top_answer.

A post with no qualifying pair contributes no interactions. As a result every interaction here has a non-null top_answer that differs from preferred_answer — i.e. the timeline is entirely cases where the OP preferred a non-top answer they could already compare against the community's top answer.

Load

python

from datasets import load_dataset
ds = load_dataset("dipikakhullar/personalization-reddit-user-histories", split="train")

Record schema

field	type	description
`user_id`	str	anonymized OP id (HMAC; joins with the other two datasets)
`n_interactions`	int	number of interactions in this timeline (top-level for sorting)
`active_subreddits`	list[str]	the set of subreddits the user appears in, most-active first
`author_metadata`	obj	activity + flair summary; see below
`interactions`	list[obj]	the timeline, ascending by `timestamp`

author_metadata:

field	type	description
`n_interactions`	int	number of interactions in this timeline
`n_subreddits`	int	distinct subreddits the user appears in
`top_subreddit`	str	subreddit the user is most active in
`subreddit_activity`	dict[str,int]	per-subreddit interaction counts, most-active first
`first_seen`	str	earliest interaction timestamp, ISO 8601 UTC
`last_seen`	str	latest interaction timestamp, ISO 8601 UTC
`active_span_days`	int	days between first and last interaction
`interactions_per_30d`	float	cadence within this dataset (n / span × 30)
`flair_by_subreddit`	dict[str,str]	most-recent non-null OP flair per subreddit (often empty)

Each interactions[i] object:

field	type	description
`timestamp`	str	post creation, ISO 8601 UTC
`subreddit`	str	community the question was asked in
`query`	str	the question (post title + selftext)
`preferred_answer`	str	the comment the OP thanked, created after `top_answer` and before their thanks-reply
`top_answer`	str	highest-scoring non-OP comment on the post (always present and different from `preferred_answer`; see Selection)

A note on "activity" metadata

This dataset only contains posts where the OP later thanked an answer, so the author_metadata activity stats describe the user's cadence within this dataset, not their full Reddit history. Reddit's dumps carry no account-level profile fields (karma / account age); the only profile signal on the records is flair, which is per-subreddit and frequently null.

Provenance

Same OP-thanks-reply heuristic and HMAC anonymization as dipikakhullar/personalization-reddit; see that dataset card for details.