With the world-wide development of 2019 novel coronavirus, although WHO has officially announced the disease as COVID-19, one controversial term -"Chinese Virus" is still being used by a great number of people. In the meantime, global online media coverage about COVID-19-related racial attacks increases steadily, most of which are anti-Chinese or anti-Asian. As this pandemic becomes increasingly severe, more people start to talk about it on social media platforms such as Twitter. When they refer to COVID-19, there are mainly two ways: using controversial terms like "Chinese Virus" or "Wuhan Virus", or using non-controversial terms like "Coronavirus". In this study, we attempt to characterize the Twitter users who use controversial terms and those who use non-controversial terms. We use the Tweepy API to retrieve 17 million related tweets and the information of their authors. We find significant differences between these two groups of Twitter users across their demographics, user-level features like the number of followers, political following status, as well as their geo-locations. Moreover, we apply classification models to predict Twitter users who are more likely to use controversial terms. To our best knowledge, this is the first large-scale social media-based study to characterize users with respect to their usage of controversial terms during a major crisis.

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BACKGROUND The number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media. OBJECTIVE This study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media. METHODS We applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette–related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette–related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category. RESULTS More than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58% and 15%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories. CONCLUSIONS The most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes.

BACKGROUND In recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users’ perspective. OBJECTIVE This study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data. METHODS A dataset consisting of 2.8 million e-cigarette–related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms. RESULTS Temporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors. CONCLUSIONS This study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors.

BACKGROUND A cross-sectional study conducted by French researchers showed that the rate of current daily smoking was significantly lower in COVID-19 patients than in the French general population. OBJECTIVE We aim to examine the dissemination of this French study among Twitter users and whether a shift in their attitudes towards smoking occurred after its publication on April 21st, 2020. METHODS Twitter posts were crawled between April 14th and May 4th, 2020 by the Tweepy stream API, using a COVID-19 related keyword query. After filtering, the final 1,929 tweets were classified into three groups: 1) tweets not related to French study before it was published; 2) tweets not related to French study after it was published; 3) tweets related to French study after it was published. The tweets’ attitudes towards smoking were compared among the above three groups using multinomial logistic regression models in statistical analysis software R. RESULTS The temporal analysis showed a peak in the number of tweets discussing the results from the French study right after its publication. Multinomial logistic regression models on sentiment scores showed the proportion of negative attitudes toward smoking in tweets related to French study after it was published (17.07%) was significantly lower than tweets not related to the French study either before (34.92%, P < 0.001) or after the French study was published (34.34%, P < 0.001). CONCLUSIONS The public’s attitude toward smoking shifted in a positive direction after the French study found a lower incidence of COVID-19 cases in daily smokers.