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DOI 10.1016/j.ijmedinf.2021.104574

Медицинская библиотека · International journal of medical informatics · 2021

Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak.

Kasson Erin, Singh Avineet Kumar, Huang Ming, Wu Dezhi, Cavazos-Rehg Patricia

СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

INTRODUCTION

Vaping product use (i.e., e-cigarettes) has been rising since 2000 in the United States. Negative health outcomes associated with vaping products have created public uncertainty and debates on social media platforms. This study explores the feasibility of using social media as a surveillance tool to identify relevant posts and at-risk vaping users.

METHODS

Using an interdisciplinary method that leverages natural language processing and manual content analysis, we extracted and analyzed 794,620 vaping-related tweets on Twitter. After observing significant increases in vaping-related tweets in July, August, and September 2019, additional human coding was completed on a subset of these tweets to better understand primary themes of vaping-related discussions on Twitter during this time frame.

RESULTS

We found significant increases in tweets related to negative health outcomes such as acute lung injury and respiratory issues during the outbreak of e-cigarette/vaping associated lung injury (EVALI) in the fall of 2019. Positive sentiment toward vaping remained high, even across the peak of this outbreak in July, August, and September. Tweets mentioning the public perceptions of youth risk were concerning, as were increases in marketing and marijuana-related tweets during this time.

DISCUSSION: The preliminary results of this study suggest the feasibility of using Twitter as a means of surveillance for public health crises, and themes found in this research could aid in specifying those groups or populations at risk on Twitter. As such, we plan to build automatic detection algorithms to identify these unique vaping users to connect them with a digital intervention in the future.

Для цитирования: Kasson Erin, Singh Avineet Kumar, Huang Ming, Wu Dezhi, Cavazos-Rehg Patricia. Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak.. International journal of medical informatics. 2021. DOI: 10.1016/j.ijmedinf.2021.104574.

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