In recent years, the emerging electronic cigarette (e-cigarette) marketplace has shown great development prospects all over the world. Reddit, one of the most popular forums in the world, has a very large user group and thus great influence. This study aims to gain a systematic understanding of e-cigarette flavors based on data collected from Reddit. Flavor popularity, mixing, characteristics, trends, and brands are analyzed. Fruit flavors were mentioned the most (n = 15,720) among all the posts and were among the most popular flavors (n = 2902) used in mixed blends. Strawberry and vanilla flavors were the most popular for e-juice mixing. The number of posts discussing e-cigarette flavors has increased sharply since 2014. Mt. Baker Vapor and Hangen were the most popular brands discussed among users. Information posted on Reddit about e-cigarette flavors reflected consumers’ interest in a variety of flavors. Our findings suggest that Reddit could be used for data mining and analysis of e-cigarette-related content. Understanding how e-cigarette consumers’ view and utilize flavors within their vaping experience and how producers and marketers use social media to promote flavors and sell products could provide valuable information for regulatory decision-makers.

The electronic cigarette (e-cigarette) market has grown rapidly in recent years. However, causes of e-cigarette related symptoms among users and their impact on health remain uncertain. This research aims to mine the potential relationships between symptoms and e-liquid components, such as propylene glycol (PG), vegetable glycerine (VG), flavor extracts, and nicotine, using user-generated data collected from Reddit. A total of 3605 e-liquid related posts from January 1st, 2011 to June 30th, 2015 were collected from Reddit. Then the patterns of VG/PG distribution among different flavors were analyzed. Next, the relationship between throat hit, which was a typical symptom of e-cigarette use, and e-liquid components was studied. Finally, other symptoms were examined based on e-liquid components and user sentiment. We discovered 3 main sets of findings: 1) We identified three groups of flavors in terms of VG/PG ratios. Fruits, cream, and nuts flavors were similar. Sweet, menthol, and seasonings flavors were classified into one group. Tobacco and beverages flavors were the third group. 2) Throat hit was analyzed and we found that menthol and tobacco flavors, as well as high ratios of PG and nicotine level, could produce more throat hit. 3) A total of 9 systems of 25 symptoms were identified and analyzed. Components including VG/PG ratio, flavor, and nicotine could be possible reasons for these symptoms. E-liquid components shown to be associated with e-cigarette use symptomology were VG/PG ratios, flavors, and nicotine levels. Future analysis could be conducted based on the structure of e-liquid components categories built in this study. Information revealed in this study could be utilized by e-cigarette users to understand the relationship between e-liquid type and symptoms experienced, by vendors to choose appropriate recipes of e-liquid, and by policy makers to develop new regulations.

Estimating the key epidemiological features of the novel coronavirus (2019-nCoV) epidemic proves to be challenging, given incompleteness and delays in early data reporting, in particular, the severe under-reporting bias in the epicenter, Wuhan, Hubei Province, China. As a result, the current literature reports widely varying estimates. We developed an alternative geo-stratified debiasing estimation framework by incorporating human mobility with case reporting data in three stratified zones, i.e., Wuhan, Hubei Province excluding Wuhan, and mainland China excluding Hubei. We estimated the latent infection ratio to be around 0.12% (18,556 people) and the basic reproduction number to be 3.24 in Wuhan before the city’s lockdown on January 23, 2020. The findings based on this debiasing framework have important implications to prioritization of control and prevention efforts. One Sentence Summary: A geo-stratified debiasing approach incorporating human movement data was developed to improve modeling of the 2019-nCoV epidemic.

Purpose: Many users build personal projects in co-innovation community to accomplish their innovations. However, very few projects from such communities are successful and understanding of this phenomenon is limited. The purpose of this paper is to identify the factors facilitating user projects success in online co-innovation communities. Design/methodology/approach: Based on the theories of persuasion and diffusion of innovation (DOI), a conceptual model is proposed to explain how project success likelihood is affected by the creator, project and user participation characteristics. Then, the model and hypotheses are tested through binary logistic regression on a secondary data set of 572 projects collected from a typical user co-innovation community, Local Motors. Findings: The results show that creator characteristics (prior success rate), project characteristics (project popularity, length and duration) and user participation characteristics (participation users and degree) have significant and positive impacts on project success likelihood. The number of prior projects, which can hardly represent the creator’s credibility in open and unrestricted situations, has no significant influence on the project success likelihood. Practical implications: This study offers project creators the keys to increase their projects successful possibility. Besides, this study recommends a new way to attract users and helps to identify creative and effective users for community practitioners. Originality/value: This study expands the research scope in online co-innovation community by focusing on user personal projects. In addition, it combines persuasion theory and DOI theory to add the holistic understanding of user project success likelihood.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet