Pith Number

pith:TVOB3BCZ

pith:2026:TVOB3BCZYMLJWR3W5B6WQ52MMA

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Are LLMs More Skeptical of Entertainment News?

Huiqian Lai

Some large language models misclassify legitimate entertainment news as fake at higher rates than hard news.

arxiv:2605.01727 v1 · 2026-05-03 · cs.AI · cs.CY

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\usepackage{pith}
\pithnumber{TVOB3BCZYMLJWR3W5B6WQ52MMA}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Across four frontier models, we find a clear but model-specific genre asymmetry: DeepSeek-V3.2 and GPT-5.2 show false-positive-rate gaps of 10.1 and 8.8 percentage points, respectively (both p < .001), whereas Claude Opus 4.6 and Gemini 3 Flash show no comparable difference.

C2weakest assumption

That the within-dataset design on GossipCop sufficiently isolates genre effects from confounding differences in topic, source, or unverifiability of private-life claims between entertainment and hard news.

C3one line summary

Certain frontier LLMs exhibit higher false-positive rates on legitimate entertainment news than hard news, with model-specific patterns not explained by style alone.

References

13 extracted · 13 resolved · 0 Pith anchors

[1] SoK: Machine Learning for Misinformation Detec- tion.arXiv preprint arXiv:2308.12215. Horne, B.; and Adali, S. 2017. This Just In: Fake News Packs A Lot In Title, Uses Simpler, Repetitive Content in T 2017

[2] A Survey on the Use of Large Language Models (LLMs) in Fake News.Future Internet, 16(8). Pelrine, K.; Mosber, A.; Zheng, J.; Yang, J.-Y .; Peng, A.; Rabbany, R.; and Cheung, J. C. K. 2023. Towards Re- 2023

[3] P´erez-Rosas, V .; Kleinberg, B.; Lefevre, A.; and Mihalcea, R 2018

[4] In Palmer, M.; Hwa, R.; and Riedel, S., eds.,Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2931–2937 2017

[5] Hard and Soft News: A Review of Concepts, Oper- ationalizations and Key Findings.Journalism, 13(2): 221– 239. Roberts, S. T. 2019.Behind the screen : content modera- tion in the shadows of social medi 2019

Receipt and verification

First computed	2026-06-12T01:09:28.441944Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9d5c1d8459c3169b4776e87d68774c6030de5400f389f7a90d79e8a487ed94f3

Aliases

arxiv: 2605.01727 · arxiv_version: 2605.01727v1 · doi: 10.48550/arxiv.2605.01727 · pith_short_12: TVOB3BCZYMLJ · pith_short_16: TVOB3BCZYMLJWR3W · pith_short_8: TVOB3BCZ

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/TVOB3BCZYMLJWR3W5B6WQ52MMA \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9d5c1d8459c3169b4776e87d68774c6030de5400f389f7a90d79e8a487ed94f3

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b33bfca04197a985789bd26384ab3aa2b6da4a7a023b19323b7ceb1d2a820dd3",
    "cross_cats_sorted": [
      "cs.CY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-05-03T05:55:00Z",
    "title_canon_sha256": "2ef56be54bbb5714c76770fb1cf31921624db556df7a9770379a8ee77bdbed52"
  },
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  "source": {
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    "kind": "arxiv",
    "version": 1
  }
}