LongSocialBench

LongSocialBench is a multiple-choice benchmark for long-context understanding of online discussion threads. It contains 1,462 questions over 94 complete discussion episodes from Hacker News, Reddit r/ChangeMyView, and Stack Exchange. Each question has four options, one correct answer, and one capability label (C1–C6).

Paper: LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads? (EMNLP 2026, Main Conference)

Authors: Xinyi Liu, Rinat Khaziev, Dilek Hakkani-Tür, Tarek F. Abdelzaher

Usage

import json
from datasets import load_dataset

questions = load_dataset(
    "Tracyyyy1997/LongSocialBench",
    "questions",
    split="test",
)  # 1,462 rows

episodes = load_dataset(
    "Tracyyyy1997/LongSocialBench",
    "episodes",
    split="test",
)  # 94 rows

episode = {e["episode_id"]: e for e in episodes}[questions[0]["episode_id"]]
nodes = json.loads(episode["nodes_json"])  # list of comments with reply structure

The two configs join on episode_id. The evaluation metric is accuracy of the predicted option letter against gold_letter.

Contents

Platform	platform	Episodes	Questions
Hacker News	hackernews	43	696
Reddit r/ChangeMyView	reddit	30	470
Stack Exchange	stackexchange	21	296
Total		94	1,462

Capability	Name	Tests	Questions
C1	Local social meaning	One comment's role in its nearby context.	165
C2	Reply-pair interaction	How a reply relates to the parent comment it answers.	89
C3	Collective structure	Positions or roles organizing a scoped discussion region.	687
C4	Temporal dynamics	Turning points in an ordered exchange or reply chain.	185
C5	Participant profile	One author's stance or strategy across multiple comments.	218
C6	Cross-subdiscussion variation	Similarity or change across separate branches or sub-discussions.	118

Fields

questions

Column	Description
episode_id	Episode the question is about.
platform	hackernews, reddit, or stackexchange.
question_id	Unique within the episode. (episode_id, question_id) is the global key.
capability, capability_name	C1 to C6, and its name.
question	Question text.
option_a, option_b, option_c, option_d	The four answer options.
gold_letter	Correct option: A, B, C, or D.
gold_answer	Text of the correct option.

episodes

Column	Description
episode_id	Episode ID.
platform	hackernews, reddit, or stackexchange.
site	Stack Exchange site name; null otherwise.
title	Thread title.
post_author	Root post author (pseudonym); null for Stack Exchange.
post_time	Root post time; same format as comment time.
post_content	Root post: body text (Reddit), thread link (Hacker News), empty (Stack Exchange).
url	Source link: discussed page (Hacker News), thread (Reddit), site (Stack Exchange).
source_question_ids	Stack Exchange only: JSON list of source question IDs (<url>/q/<id>).
num_nodes	Number of comments.
num_questions	Number of questions.
nodes_json	JSON list of all comments in thread order.

Comments in nodes_json

Field	Description
node_id	Unique within the episode.
parent_id	Parent's node_id. Top-level comments are null (Hacker News, Stack Exchange) or "OP" (Reddit).
author	Pseudonym user_NNN, or a platform marker such as [deleted].
time	Relative string (Hacker News), ISO-8601 UTC (Reddit), null (Stack Exchange).
text	Comment text.
depth	Reply depth.
score, is_accepted, type	Stack Exchange only: vote score, accepted-answer flag, and question / answer / comment.

License and data rights

• Benchmark questions, answer options, and labels created by the authors are released under CC BY 4.0.
• Discussion content originates from publicly accessible pages on Hacker News, Reddit, and Stack Exchange. Copyright and other rights in the source content remain with the respective rightsholders and are subject to the applicable platform terms and licenses. The authors do not grant additional rights to third-party content.
• Stack Exchange content is subject to the applicable CC BY-SA version associated with each contribution.
• Usernames are replaced with per-episode pseudonyms (user_001, user_002, ...) that are not linked across episodes.

To request removal of content, open a discussion on this repository.

Citation

@inproceedings{liu2026longsocialbench,
  title     = {LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?},
  author    = {Liu, Xinyi and Khaziev, Rinat and Hakkani-T{\"u}r, Dilek and Abdelzaher, Tarek F.},
  booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},
  year      = {2026}
}