Vaping discussion in the COVID-19 pandemic: An observational study using Twitter data

Abstract

Background With the spread of COVID-19, significant concerns have been raised about the potential increased risk for electronic cigarette (e-cigarette) users for COVID-19 infection and related syndromes. Social media is an increasingly popular source for health information dissemination and discussion, and can affect health outcomes. Objective This study aims to identify the topics in the public vaping discussion in COVID-19–related Twitter posts in order to get insight into public vaping-related perceptions, attitudes and concerns, and to discern possible misinformation and misconceptions around vaping in the COVID-19 pandemic. Methods Using the tweets ID database maintained by Georgia State University’s Panacea Lab, we downloaded the tweets related to COVID-19 from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to February 12, 2021. We used R to analyze the tweets that contained a list of 79 keywords related to vaping. After removing duplicates and tweets created by faked accounts or bots, the final data set consisted of 11,337 unique tweets from 7,710 different users. We performed the latent Dirichlet allocation (LDA) algorithm for topic modeling and carried out a sentiment analysis. Results Despite fluctuations, the number of daily tweets was relatively stable (average number of daily tweets = 33.4) with a sole conspicuous spike happening on a few days after August 11, 2020 when a research team published findings that teenagers and young adults who vape face a much higher risk of COVID-19 infection than their peers who do not vape. Topic modeling generated 8 topics: linkage between vaping and risk of COVID-19 infection, vaping pneumonia and the origin of COVID-19, vaping and spread of COVID-19, vaping regulation, calling for quitting vaping, protecting youth, similarity between e-cigarette or vaping-associated lung injury (EVALI) and COVID-19, and sales information. Daily sentiment scores showed that the public sentiment was predominantly negative, but became slightly more positive over the course of the study time period. Conclusions While some content in the public discourse on vaping before the COVID-19 pandemic continued in Twitter posts during the COVID-19 time period, new topics emerged. We found a substantial amount of anti-vaping discussion and dominantly negative sentiment around vaping during COVID-19, a sharp contrast to the predominantly pro-vaping voice on social media in the pre-COVID-19 period. Continued monitoring of social media conversations around vaping is needed, and the public health community may consider using social media platforms to actively convey scientific information around vaping and vaping cessation.

Transformasi Vape Di Ruang Sosial: Dari Teknologi Medis Ke Mode (Sebuah Kajian Dengan Perpektif Social Construction of Technology)

“…Lately, there is a lot of buzz about the absence of government regulations in various countries for these cigarette innovations (Lyu et al, 2021). However, apart from that, the audience for vaping continues to grow.…”

“…Several studies state that vaping has adverse effects such as addiction, especially among young people. Therefore, there is a need for accompanying rules (Lyu et al, 2021). The article by Jarmul et al (2017) states that the current state of e-cigarette safety and regulation is very similar to efforts to regulate tobacco smoking in the 1960s when cigarette marketing was widespread, and research on the health effects of smoking was scarce and contested.…”

Modelamiento de tópicos utilizando mensajes de Twitter relacionados al cáncer cervical

“…Twitter) se puede observar como el personal del área de la salud, investigadores e incluso los pacientes comparten temas de interés. Las redes sociales se han convertido en una fuente popular para la difusión y debate de información sobre salud (Lyu et al, 2021), pues estas contienen opiniones que permiten comprender la percepción popular sobre temas como diagnóstico médico, medicamentos, instalaciones y reclamos (Asghari et al, 2019), además que actúan como un sensor del comportamiento de una persona (Hinduja et al, 2022) o incluso para establecer comunidades de soporte a ciertas enfermedades (Janslin, 2022).…”

“…En los trabajos de Kwok et al, (2021) y Bogdanowicz y Guan (2022) aplicaron modelo de tópicos para identificar temas que se discuten respecto al Covid-19 tanto en Australia como Estados Unidos, respectivamente. En Lyu et al, (2021) aplicaron LDA para identificar percepciones, actitudes y preocupaciones relacionadas con el vapeo, y para discernir posibles desinformaciones y conceptos erróneos sobre el vapeo durante la pandemia. Otro reciente estudio publicado por Oliveira et al, (2022), aplicó el mismo algoritmo LDA para analizar nuevos temas en tweets y luego proceder a identificar emociones en respuestas a tuits de noticias.…”