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<p>This study investigates how the COVID-19 pandemic altered patterns of news reporting and public information-seeking related to particulate matter (PM) in South Korea. We assembled daily time-series data from January 1, 2019, to December 31, 2020, including PM levels, PM-related news volume and sentiment, public online search frequency related to PM, and confirmed COVID-19 cases. To examine dynamic and potentially reciprocal relationships, we estimated autoregressive integrated moving average models with exogenous variables (ARIMAX) to predict search frequency and vector autoregressive (VAR) models with Granger causality and impulse response analyses. Before the pandemic, higher PM levels and increased PM-related news coverage were associated with greater search frequency, and more negative news sentiment coincided with higher same-day searches. During the pandemic, these relationships weakened. In particular, the association between news volume and search frequency became conditional on PM levels. VAR analyses indicate that, prior to COVID-19, public search frequency and PM levels more reliably preceded changes in news volume and sentiment, whereas news coverage did not predict subsequent search frequency in either period. These dynamics were attenuated during the pandemic, and changes in COVID-19 case counts were associated with subsequent shifts in PM-related news production. The findings show that a large-scale health crisis can disrupt established relationships among objective risk conditions, public information-seeking, and news production, highlighting how competing public health threats reshape information flows during periods of crisis.</p>

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The impact of COVID-19 on news reporting and public information-seeking about particulate matter: a time-series analysis of issue competition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Sungbin Youk,
  • Hyejung Kim,
  • Yungwook Kim

摘要

This study investigates how the COVID-19 pandemic altered patterns of news reporting and public information-seeking related to particulate matter (PM) in South Korea. We assembled daily time-series data from January 1, 2019, to December 31, 2020, including PM levels, PM-related news volume and sentiment, public online search frequency related to PM, and confirmed COVID-19 cases. To examine dynamic and potentially reciprocal relationships, we estimated autoregressive integrated moving average models with exogenous variables (ARIMAX) to predict search frequency and vector autoregressive (VAR) models with Granger causality and impulse response analyses. Before the pandemic, higher PM levels and increased PM-related news coverage were associated with greater search frequency, and more negative news sentiment coincided with higher same-day searches. During the pandemic, these relationships weakened. In particular, the association between news volume and search frequency became conditional on PM levels. VAR analyses indicate that, prior to COVID-19, public search frequency and PM levels more reliably preceded changes in news volume and sentiment, whereas news coverage did not predict subsequent search frequency in either period. These dynamics were attenuated during the pandemic, and changes in COVID-19 case counts were associated with subsequent shifts in PM-related news production. The findings show that a large-scale health crisis can disrupt established relationships among objective risk conditions, public information-seeking, and news production, highlighting how competing public health threats reshape information flows during periods of crisis.