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    <abstract>Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent–reply relations, turning points, scoped subtrees, cross-branch contrasts, and participant trajectories. To test this structure-aware social reasoning, we introduce **LongSocialBench**, a benchmark of 1,462 verified human-authored multiple-choice items drawn from 94 complete Hacker News, Stack Exchange, and Reddit r/ChangeMyView episodes, with a median length of \ensuremath\approx73K tokens. Each item pairs a complete serialized discussion and reply structure with a four-option question, requiring models to recover structured social evidence. Released items are verified for answerability, option uniqueness, and evidence grounding.\par Across 18 models and 29 evaluation settings, current long-context workflows remain far below human performance. The best individual result comes from Claude-Opus-4.7, which reaches 63.0% when prompted to eliminate incorrect options before answering, compared with 72.4% for independent human readers. Averaged across all 18 models, the full-context Baseline scores 43.9%. Supplying the gold evidence scope raises this to 55.0%, showing that substantial errors remain even after the relevant thread region is identified. LongSocialBench shows that the missing capability is not context access or prompting alone, but social understanding over structured reply trees.</abstract>
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%0 Conference Proceedings
%T LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?
%A Liu, Xinyi
%A Khaziev, Rinat
%A Hakkani-Tür, Dilek
%A Abdelzaher, Tarek F.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-longsocialbench
%X Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent–reply relations, turning points, scoped subtrees, cross-branch contrasts, and participant trajectories. To test this structure-aware social reasoning, we introduce **LongSocialBench**, a benchmark of 1,462 verified human-authored multiple-choice items drawn from 94 complete Hacker News, Stack Exchange, and Reddit r/ChangeMyView episodes, with a median length of \ensuremath\approx73K tokens. Each item pairs a complete serialized discussion and reply structure with a four-option question, requiring models to recover structured social evidence. Released items are verified for answerability, option uniqueness, and evidence grounding.\par Across 18 models and 29 evaluation settings, current long-context workflows remain far below human performance. The best individual result comes from Claude-Opus-4.7, which reaches 63.0% when prompted to eliminate incorrect options before answering, compared with 72.4% for independent human readers. Averaged across all 18 models, the full-context Baseline scores 43.9%. Supplying the gold evidence scope raises this to 55.0%, showing that substantial errors remain even after the relevant thread region is identified. LongSocialBench shows that the missing capability is not context access or prompting alone, but social understanding over structured reply trees.
%U https://aclanthology.org/2026.emnlp-main.896/
%P 20638-20660

Markdown (Informal)

[LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?](https://aclanthology.org/2026.emnlp-main.896/) (Liu et al., EMNLP 2026)

• LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads? (Liu et al., EMNLP 2026)

ACL

• Xinyi Liu, Rinat Khaziev, Dilek Hakkani-Tür, and Tarek F. Abdelzaher. 2026. LongSocialBench: Do Long-Context LLMs Understand Online Discussion Threads?. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 20638–20660, Budapest, Hungary. Association for Computational Linguistics.