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• W4319456086 endingPage "100042" @default.
• W4319456086 startingPage "100042" @default.
• W4319456086 abstract "Online science communication, especially on social media, has become a constant and controversial type of scientific discourse. Past research has shown that how scientific information is presented can impact how the information is interpreted and accepted by the public. However, an aspect of scientific communication that has been relatively understudied is the use of modal verbs to communicate scientific uncertainty and how scientists convey the responsibility of agents to complete actions pertaining to scientific information. To address this gap, this study presents a corpus-based research approach to analyzing how modal verbs convey scientific information to the public on Twitter. The study's corpus consisted of 9,422 tweets posted between 2019 and 2022 collected from the official Twitter accounts of three scientific governmental agencies (SGAs): the Centers for Disease Control and Prevention (CDC), the Environmental Protection Agency (EPA), and the National Oceanic and Atmospheric Administration (NOAA). The results showed that each SGA varied in terms of which modal verbs they used most frequently and which agents and actions were most commonly addressed. A qualitative analysis using Conrad et al.’s (2002) categories of modal verbs—permission/ability, obligation/necessity, and volition/prediction—found that the frequent use of the modal verb can by all three SGAs suggests an emphasis on the ability or permission of specific agents to take or complete actions without conveying a sense of obligation or necessity. The method presented in this paper illustrates how corpus linguistics approaches can be used to analyze the role of modal verbs in agent + action structures to represent scientific uncertainty and responsibility in public-facing science communication, and this paper presents the implications of a preliminary application of these approaches." @default.
• W4319456086 created "2023-02-09" @default.
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• W4319456086 date "2023-04-01" @default.
• W4319456086 modified "2023-09-29" @default.
• W4319456086 title "Using corpus methods to analyze modal verbs in government science communication on Twitter" @default.
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• W4319456086 doi "https://doi.org/10.1016/j.rmal.2023.100042" @default.
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