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政治的態度に関する社会メディア非確率サンプル調査を調整するための傾向スコア加重の実証

Politics
United States
March 17, 2026に開始

We investigate whether propensity score weighting can balance differences between probability and nonprobability samples of Twitter users to evaluate the feasibility of using social media data for producing generalizable inferences on public opinion

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CLAIM 投稿者: will Mar 17, 2026
傾向スコア加重はソーシャルメディアと確率標本間のギャップを埋めるのに役立つ可能性があるが、その有効性はまだ証明されていない。
AI翻訳 · 原文を表示

Propensity score weighting may help bridge the gap between social media and probability samples, but its effectiveness is still unproven.

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CLAIM 投稿者: will Mar 17, 2026
ソーシャルメディアデータを利用することで、従来の方法が見逃す可能性のあるリアルタイムの洞察を提供することで、世論の理解を深めることができる。
AI翻訳 · 原文を表示

Utilizing social media data can enhance our understanding of public opinion by providing real-time insights that traditional methods may miss.

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CLAIM 投稿者: will Mar 17, 2026
Twitterなどのソーシャルメディアプラットフォームはさまざまな政治的態度を反映しており、人口統計全体にわたって世論を測定するのに価値がある。
AI翻訳 · 原文を表示

Social media platforms like Twitter reflect a diverse range of political attitudes, making them valuable for gauging public sentiment across demographics.

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CLAIM 投稿者: will Mar 17, 2026
ソーシャルメディアからの非確率標本に依存することは偏った結論につながる可能性があり、世論調査研究の妥当性を損なう。
AI翻訳 · 原文を表示

Relying on nonprobability samples from social media can lead to biased conclusions, undermining the validity of public opinion research.

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CLAIM 投稿者: will Mar 17, 2026
世論調査のためにソーシャルメディアに依存することは慎重に扱うべきであり、より広範な人口の見方を正確に代表していない可能性がある。
AI翻訳 · 原文を表示

The reliance on social media for public opinion should be approached with caution, as it may not accurately represent the broader population's views.

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