癌 オンライン 健康 コミュニティ の 定量 的 言語 分析
Mengque Liu1, Xinyan Fan2, Shuangge Ma3
1School of Journalism and New Media, Xi'an Jiaotong University.
The annals of applied statistics
|August 29, 2025
まとめ
肺がんに関するオンラインのサポート投稿を分析すると,患者の感情が大きく変化し,しばしばCOVID-19の変化と一致しています. 先進的な言語ネットワーク分析を通して 患者の健康状態の理解を深める研究です
科学分野:
- コンピュータ言語学
- ソーシャルネットワークの分析
- 公共衛生情報学
背景:
- オンラインヘルスケアコミュニティ (OHC) は,患者支援と議論のための貴重なプラットフォームを提供します.
- OHCの言語学的な分析は 患者の感情的・精神的な状態を 洞察することができます
- OHCを分析するための既存の方法には,特に単語共存ネットワーク分析において,限界があります.
研究 の 目的:
- LUNGevity財団の肺がん支援コミュニティ (LCSC) のデータにおける言語パターンを分析する.
- 先進的な単語共演ネットワーク分析モデルを開発し,適用する.
- 肺がん患者とその関係者の感情的・精神的な状態の 時間の変化を特定する.
主な方法:
- ネットワークの重みを含む2つの部分の隠れた空間モデルを使用した.
- 未知の変化点を特定し,時間変動のネットワークを推定するために,罰せられた融合アプローチを開発した.
- 2018年4月から2022年2月までの間,LCSCの21,028件のポストを分析しました.
主要な成果:
- 言語データにおける多重な変化点を特定した.
- これらの変化点は,肺がん患者とその関係者の感情的/精神的な状態の変化と相関しています.
- 特定された変化点と主要なCOVID-19関連イベントの間の一致が検出されました.
結論:
- この研究は,オンラインの肺がんコミュニティ内の患者の感情の時間的変化を成功裏に特定しました.
- 先進的なネットワーク分析モデルは,OHCのダイナミックな変化を分析するための堅固な方法を提供します.
- この調査結果は,COVID-19のパンデミックのような外部イベントが患者の健康に与える影響を明らかにしています.
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