Insights on electronic cigarette products from reviews on the
Reddit forum

Allem, Jon-Patrick; Majmundar, Anuja; Dharmapuri, Likhit; Unger, Jennifer B.; Cruz, Tess Boley

doi:10.18332/tpc/111533

Cited by 8 publications

(2 citation statements)

References 44 publications

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“…To the best of our knowledge, this is the first cross-platform semi-supervised attempt in tobacco research. (Allem et al, 2019;Carroll et al, 2012;Yanamandra et al, 2020;Zhang et al, 2018). While explored a thematic analysis of hookah Twitter posts, Allem et al (2017a) explored e-cigarette trends with an emphasis on social bot behaviour.…”

Section: Neutralmentioning

confidence: 99%

Towards Sentiment Analysis of Tobacco Products’ Usage in Social Media

Yanamandra¹,

Pant

Mamidi

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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Contemporary tobacco-related studies are mostly concerned with a single social media platform while missing out on a broader audience. Moreover, they are heavily reliant on labeled datasets, which are expensive to make. In this work, we explore sentiment and product identification on tobacco-related text from two social media platforms. We release SentiSmoke-Twitter and SentiSmoke-Reddit datasets, along with a comprehensive annotation schema for identifying tobacco products' sentiment. We then perform benchmarking text classification experiments using state-of-the-art models, including BERT, RoBERTa, and DistilBERT. Our experiments show F1 scores as high as 0.72 for sentiment identification in the Twitter dataset, 0.46 for sentiment identification, and 0.57 for product identification using semi-supervised learning for Reddit.

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Section: Neutralmentioning

confidence: 99%

Towards Sentiment Analysis of Tobacco Products’ Usage in Social Media

Yanamandra¹,

Pant

Mamidi

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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“…One analysis of Reddit threads indicated that primary motivations for vaping among individuals with mental illness include self-medication, freedom and control, vaping as a hobby, social connectedness, as well as vaping to quit smoking [ 32 ]. Other studies have used Reddit data to analyze public responses and concerns about vape bans [ 33 ], communities supporting e-cigarette cessation [ 34 ], and attitudes and reviews toward e-cigarette products [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use–Associated Lung Injury Outbreak: Comparative Study

Wu¹,

Kasson²,

Singh³

et al. 2022

J Med Internet Res

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Background Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use–associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment. Objective This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media. Methods Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts). Results Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P<.001, 95% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P<.001, 95% CI –0.4289 to −0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3%) than Reddit (184/613, 30%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting. Conclusions Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms.

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