CoViNAR: パンデミック重症度の予測と分析のための文脈に配慮したソーシャルメディアデータセット
Soofi Shafiya1, Mudasir Ahmad Wani2, Suraiya Jabin1
1Department of Computer Science, Faculty of Sciences, Jamia Millia Islamia, New Delhi, India.
Frontiers in artificial intelligence
|September 5, 2025
まとめ
この研究は,COVID-19のリソースのニーズをリアルタイムで追跡し,パンデミックへの準備とリソースの配分を改善するために,ソーシャルメディアのデータを使用しています. ソーシャル・メディアとの強い相関が示されています.
科学分野:
- 公衆衛生
- コンピュータ社会科学
- データサイエンス
背景:
- COVID-19 パンデミックは,世界の医療資源管理と需要予測の欠陥を明らかにした.
- 効果的なパンデミック対策には,リアルタイムのデータ分析が不可欠です.
研究 の 目的:
- リアルタイムで医療資源の必要性について ソーシャルメディアを分析することで パンデミックへの備えを強化する.
- 保健危機の際に資源不足と利用可能性の検出とモニタリングのための方法を開発する.
主な方法:
- SnScrapeを使用してCOVID-19に関連する2750万件以上のツイートを収集しました.
- 文脈に配慮したフィルタリングのためにBERTopicを使用して,14,000の注釈されたツイートの CoViNAR データセットを作成しました.
- ツイート分類にDistilBERTを埋め込んだ 機械学習分類器を訓練し評価した.
主要な成果:
- 精度,リコール,F1スコアが96%を超えました.
- タイム分析では",必要性/利用可能性"のツイートと米国,英国,インドのCOVID-19症例の急増との間に強い相関関係があることが明らかになりました.
- リアルタイムのリソースモニタリングのためのソーシャルメディア分析の有効性を実証しました.
結論:
- ソーシャルメディアの分析は 積極的な公衆衛生監視の有効なツールです
- このアプローチにより 資源の配分が改善され パンデミック時に早期の危機介入が可能になります
- この方法は 将来の健康上の緊急事態に備えて グローバルな健康管理システムを強化できます
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