Identifying Topics for E-Cigarette User-Generated Contents: A Case Study From Multiple Social Media Platforms

• Published in: Journal of medical Internet research Vol. 19; no. 1; p. e24
• Main Authors: Zhan, Yongcheng, Liu, Ruoran, Li, Qiudan, Leischow, Scott James, Zeng, Daniel Dajun
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 01.01.2017; JMIR Publications
• Subjects: Case studies; Computational linguistics; Datasets as Topic; Electronic cigarettes; Electronic Nicotine Delivery Systems - statistics & numerical data; Humans; Internet; Language processing; Natural language interfaces; Original Paper; Social media; Social Media - statistics & numerical data; Social networks; Web site management software; Latent Dirichlet Allocation; topic modeling; social media; infodemiology; electronic cigarettes
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/jmir.5780

Electronic cigarette (e-cigarette) is an emerging product with a rapid-growth market in recent years. Social media has become an important platform for information seeking and sharing. We aim to mine hidden topics from e-cigarette datasets collected from different social media platforms. This paper aims to gain a systematic understanding of the characteristics of various types of social media, which will provide deep insights into how consumers and policy makers effectively use social media to track e-cigarette-related content and adjust their decisions and policies. We collected data from Reddit (27,638 e-cigarette flavor-related posts from January 1, 2011, to June 30, 2015), JuiceDB (14,433 e-juice reviews from June 26, 2013 to November 12, 2015), and Twitter (13,356 "e-cig ban"-related tweets from January, 1, 2010 to June 30, 2015). Latent Dirichlet Allocation, a generative model for topic modeling, was used to analyze the topics from these data. We found four types of topics across the platforms: (1) promotions, (2) flavor discussions, (3) experience sharing, and (4) regulation debates. Promotions included sales from vendors to users, as well as trades among users. A total of 10.72% (2,962/27,638) of the posts from Reddit were related to trading. Promotion links were found between social media platforms. Most of the links (87.30%) in JuiceDB were related to Reddit posts. JuiceDB and Reddit identified consistent flavor categories. E-cigarette vaping methods and features such as steeping, throat hit, and vapor production were broadly discussed both on Reddit and on JuiceDB. Reddit provided space for policy discussions and majority of the posts (60.7%) holding a negative attitude toward regulations, whereas Twitter was used to launch campaigns using certain hashtags. Our findings are based on data across different platforms. The topic distribution between Reddit and JuiceDB was significantly different (P<.001), which indicated that the user discussions focused on different perspectives across the platforms. This study examined Reddit, JuiceDB, and Twitter as social media data sources for e-cigarette research. These mined findings could be further used by other researchers and policy makers. By utilizing the automatic topic-modeling method, the proposed unified feedback model could be a useful tool for policy makers to comprehensively consider how to collect valuable feedback from social media.