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International Conference on Information

iConference 2022: Information for a Better World: Shaping the Global Future pp 211–227Cite as

Using Local Community to Ease Long Haul Uncertainty During the COVID-19 Pandemic

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13193))

Abstract

The COVID-19 pandemic ushered in an era of unprecedented hardship worldwide, bringing uncertainty to new levels as people’s routines were disrupted and what was once considered normal was called into question. Citizens initiated online local communities to support information-seeking amidst the pandemic. In this paper, we explore what types of information were sought and how people engaged in uncertainty reduction with others in their area during the initial phase of COVID-19. We conducted content analysis on a pandemic-relief online local community. We found that people leveraged local networks to get updates about timely situations in local areas, clear confusion around local COVID-19 regulations, and seek confirmation on emerging social norms. However, there existed inaccurate information exchange about regulations and conflicting opinions on social norms. We provide design suggestions to increase the potentials of uncertainty management through online local communities.

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Notes

  1. 1.

    pseudonym for anonymity.

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Authors and Affiliations

  1. Pennsylvania State University, University Park, PA, USA

    Jeongwon Jo, Tiffany Knearem & John M. Carroll

  2. University of Nebraska at Omaha, Omaha, USA

    Chun-hua Tsai

Authors
  1. Jeongwon Jo
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  2. Tiffany Knearem
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  3. Chun-hua Tsai
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  4. John M. Carroll
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Corresponding author

Correspondence to Jeongwon Jo .

Editor information

Editors and Affiliations

  1. Humboldt-Universität zu Berlin, Berlin, Germany

    Malte Smits

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Jo, J., Knearem, T., Tsai, Ch., Carroll, J.M. (2022). Using Local Community to Ease Long Haul Uncertainty During the COVID-19 Pandemic. In: Smits, M. (eds) Information for a Better World: Shaping the Global Future. iConference 2022. Lecture Notes in Computer Science(), vol 13193. Springer, Cham. https://doi.org/10.1007/978-3-030-96960-8_15

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  • DOI: https://doi.org/10.1007/978-3-030-96960-8_15

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  • Online ISBN: 978-3-030-96960-8

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