What Can Online Doctor Reviews Tell Us? A Deep Learning Assisted Study of Telehealth Service

The present study develops a novel deep learning method which assists text mining of online doctor reviews to extract underlying sentiment scores. Those scores can be used to estimate a healthcare service quality model to investigate how the online doctor reviews impact the online doctor consultation demand. Based on the data from the largest online health platforms in China, our model results show that the underlying sentiment scores have statistically significant impacts on the demand of online doctor consultation. Theoretically, the present study constructs an innovative deep learning algorithm with a better performance than four widely used text mining methods, which can be applied to text mining of many online forums or social media texts. Empirically, our model results show what factors impact the health service quality and online doctor consultation demand, and following those factors, healthcare professionals can improve their service

Social Media Driven Public Health Informatics: Applications in Regulatory Science

Health information technology (health IT) is an emerging interdisciplinary research field that brings innovative and unique opportunities as well as challenges for information systems (IS) researchers. Public Health Informatics (PHI) is an important subdomain of health IT but yet extensively studied by IS researchers. The limited IS literature on PHI surveillance, especially on regulatory science opens new gates for IS researchers to test existing and develop new theories, to design and configure innovative methods and models, and to provide practical health managerial insights. The emerging information technology innovations provide novel insights and opportunities to improve public health surveillance. Specifically, social media analytics and intelligence, broadly accepted and applied in the current IS domains, has motivated a new branch of research in regulatory science that may bear prolific fruits both in theoretical and pragmatic perspectives. This dissertation seeks to address the problem and fills the research gaps by proposing a systematic research framework for regulatory science surveillance using social media-driven approaches. Five essays in the design science paradigm are developed. Essay I aims to provide a basic understanding of social media user-generated content on regulated products by text mining and social network analysis techniques. User-generated content can further motivate research on the user level. Essay II takes this perspective and models user features based on text. Essay III focuses on another important feature of social media – network structures, and develops a computational algorithm for large-scale social network analysis on modeling social influence. After proposing innovative IS solutions to real regulatory public health problems, essay IV endeavors to validate the use of social media datasets by combining survey and social media data for data triangulation. Finally, essay V, motivated by the keyword-based social media data collection processes, strives to automatically and accurately detect sensitive regulated product street names, utilizing rich text information in the ever-changing linguistic environment on social media. Beyond the practical insights of health decision support and solutions provided in each essay, this dissertation offers a systematic social media-driven regulatory science informatics research paradigm to guide future PHI and other analytical IS research

Distributed Representations of Users and Locations for Friendship Recommendation on Location-Based Social Network

Location-based social networks (LBSNs) have gained significant popularity nowadays and their location-sharing features promote social interactions and foster community formation. However, friend recommendation on LBSNs remains a challenging research problem. As check-in trajectories indicate user proximity, we propose a deep learning method to represent users and locations by mining user trajectories and generate top-k friend recommendation

Electronic cigarette usage patterns: a case study combining survey and social media data

Objective: To identify who were social media active e-cigarette users, to compare the use patterns from both survey and social media data for data triangulation, and to jointly use both datasets to conduct a comprehensive analysis on e-cigarette future use intentions. Materials and Methods: We jointly used an e-cigarette use online survey (n = 5132) and a social media dataset. We conducted analysis from 3 different perspectives. We analyzed online forum participation patterns using survey data. We compared e-cigarette use patterns, including brand and flavor types, ratings, and purchase approaches, between the 2 datasets. We used logistic regression to study intentions to use e-cigarettes using both datasets. Results: Male and younger e-cigarette users were the most likely to participate in e-cigarette-related discussion forums. Forum active survey participants were hardcore vapers. The e-cigarette use patterns were similar in the online survey data and the social media data. Intention to use e-cigarettes was positively related to e-liquid ratings and flavor ratings. Social media provided a valuable source of information on users' ratings of e-cigarette refill liquids. Discussion: For hardcore vapers, social media data were consistent with online survey data, which suggests that social media may be useful to study e-cigarette use behaviors and can serve as a useful complement to online survey research. We proposed an innovative framework for social media data triangulation in public health studies. Conclusion: We illustrated how social media data, combined with online survey data, can serve as a new and rich information source for public health research

Analysis of symptoms and their potential associations with e-liquids’ components: a social media study

Abstract Background 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. Methods 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. Results 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. Conclusions 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

Underage JUUL Use Patterns: Content Analysis of Reddit Messages

Background: The popularity of JUUL (an e-cigarette brand) among youth has recently been reported in news media and academic papers, which has raised great public health concerns. Little research has been conducted on the age distribution, geographic distribution, approaches to buying JUUL, and flavor preferences pertaining to underage JUUL users. Objective: The aim of this study was to analyze social media data related to demographics, methods of access, product characteristics, and use patterns of underage JUUL use. Methods: We collected publicly available JUUL-related data from Reddit. We extracted and summarized the age, location, and flavor preference of subreddit UnderageJuul users. We also compared common and unique users between subreddit UnderageJuul and subreddit JUUL. The methods of purchasing JUULs were analyzed by manually examining the content of the Reddit threads. Results: A total of 716 threads and 2935 comments were collected from the subreddit UnderageJuul before it was shut down. Most threads did not mention a specific age, but ages ranged from 13 years to greater than 21 years in those that did. Mango, mint, and cucumber were the most popular among the 7 flavors listed on JUUL's official website, and 336 subreddit UnderageJuul threads mentioned 7 discreet approaches to circumvent relevant legal regulations to get JUUL products, the most common of which was purchasing JUUL from other Reddit users (n=181). Almost half of the UnderageJuul users (389/844, 46.1%) also participated in discussions on the main JUUL subreddit and sought information across multiple Reddit forums. Most (64/74, 86%) posters were from large metropolitan areas. Conclusions: The subreddit UnderageJuul functioned as a forum to explore methods of obtaining JUUL and to discuss and recommend specific flavors before it was shut down. About half of those using UnderageJuul also used the more general JUUL subreddit, so a forum still exists where youths can attempt to share information on how to obtain JUUL and other products. Exploration of such social media data in real time for rapid public health surveillance could provide early warning for significant health risks before they become major public health threats.NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [5R01DA037378-05]; NNSFCNational Natural Science Foundation of China [71602184, 71472175, 71621002]; CASDefence Research & Development Organisation (DRDO)Chinese Academy of Sciences [ZDRW-XH-2017-3]; Ministry of Health of the People's Republic of China [2017ZX10303401-002]; Research Foundation of SKL-MCCS [20190212, 20190204]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

An Examination of Electronic Cigarette Content on Social Media: Analysis of E-Cigarette Flavor Content on Reddit

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