支持者と懐疑者:ビデオ共有プラットフォームの (誤った) 情報コンテンツとの関わり方のLLMベースの分析

Viet Cuong Nguyen¹, Mini Jain¹, Abhijat Chauhan¹

¹Georgia Institute of Technology.

Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media

|August 22, 2025

まとめ

オンラインのメンタルヘルスに関する誤った情報は YouTube Shorts や Bitchute などのプラットフォームで急速に広がっていますこの研究では大規模な言語モデルが誤った情報を検知できることが分かりましたが聴衆のコメントはしばしば汚名を悪化させ公衆衛生上の懸念を強調しています

科学分野:

デジタル・ヘルス
公衆衛生
コンピュータ言語学

背景:

オンラインの短いビデオコンテンツは精神的健康に関する情報源として増加しています
誤った情報が急速に広まることは正確な診断と治療にリスクをもたらします
オンラインで発せられる誤った情報の影響を理解し軽減する必要があるのです

研究の目的:

YouTube Shorts と Bitchute での誤った情報 (MHMisinfo) を定量的に研究する.
MHMisinfoのビデオを検出する方法を開発し評価する.
MHMisinfoの視聴者への関わり方を分析する.

主な方法:

YouTube Shorts と Bitchute から 739 件の動画と 135,372 件のコメントを含む MentalMisinfo データセットの作成.
MHMisinfoのビデオ検出のために,大規模な言語モデル (LLM) を使って,少数のショットでコンテキスト学習を行います.
MHMisinfoとの関わりを理解するために,ユーザーコメントの言語分析.

主要な成果:

Few-shot LLMはMHMisinfoのビデオを検出する上で有効性を示しています.
MHMisinfoの視聴者への関わりを明らかにする独特の言語パターンです.
MHMisinfoの動画へのコメントはメンタルヘルスに対する汚名を悪化させることがあり,一部のグループでは敏感性が高まります.

結論:

LLMはMHMisinfoを特定するための有望なツールです.
MHMisinfoの視聴者への関わりは重要な公衆衛生上の課題です
オンラインMHMisinfoに対抗するには適応的な技術と公衆衛生戦略が必要である.

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