短尺動画における健康偽情報の特定方法の理解:PLS-SEMとfsQCAを用いた統合分析
Ruojia Wang1, Huiwen Yang2,3, Yue Wang1
1School of Management, Beijing University of Chinese Medicine, Beijing, China.
Frontiers in public health
|December 24, 2025
まとめ
ユーザーは、情報の質、ユーザーの特性、外部要因を通じて、短尺動画における健康偽情報を特定します。これらの要素を理解することは、オンラインでの健康に関する誤った情報の拡散に対抗するのに役立ちます。
科学分野:
- デジタルヘルス
- ヘルスコミュニケーション
- 情報科学
背景:
- 短尺動画プラットフォームは、健康情報の主要な情報源です。
- AI生成コンテンツと低い作成障壁は、健康偽情報を悪化させます。
- 健康偽情報のユーザーの識別を理解することは不可欠です。
主な方法:
- 47回の詳細なインタビューのグラウンデッド・セオリー分析。
- 279件の有効なサンプルのアンケート調査。
- 偏最小二乗構造方程式モデリング(PLS-SEM)およびファジィ集合質的比較分析(fsQCA)。
結論:
- ユーザーは、偽情報を識別するために、分析的評価、コンテンツキューへの依存、または認知的キューへの依存を利用します。
- 調査結果は、健康偽情報と戦うための多層的な介入(個人、プラットフォーム、ポリシー)を示唆しています。
- 効果的な戦略には、情報の質、ユーザーの特性、および外部からの影響に対処する必要があります。
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