Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data (Preprint)

BACKGROUND Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic. OBJECTIVE In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data. METHODS The study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups. RESULTS The US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic. CONCLUSIONS Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

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Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data

JMIR Public Health and Surveillance ◽

10.2196/24859 ◽

2021 ◽

Vol 7 (1) ◽

pp. e24859

Author(s):

Yankun Gao ◽

Zidian Xie ◽

Dongmei Li

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Topic Modeling ◽

Viral Infections ◽

Application Programming Interface ◽

Electronic Cigarette ◽

Twitter Data ◽

Social Media Platforms ◽

Twitter Users ◽

Public Health Practitioners

Background Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic. Objective In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data. Methods The study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups. Results The US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic. Conclusions Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

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A Perspective of Electronic Cigarette Users on the COVID-19 Pandemic in Twitter: Observational Study (Preprint)

10.2196/preprints.20867 ◽

2020 ◽

Author(s):

Yankun Gao ◽

Zidian Xie ◽

Dongmei Li

Keyword(s):

Social Media ◽

Immune Responses ◽

Sentiment Analysis ◽

Topic Modeling ◽

Electronic Cigarette ◽

Virus Infections ◽

Twitter Data ◽

The Us ◽

Twitter Users ◽

Negative Sentiment

BACKGROUND Previous studies indicated electronic cigarette users might be more vulnerable to COVID-19 infections and could develop more severe symptoms once contracted COVID-19 due to their impaired immune responses to virus infections. Social media has been widely used to express users’ responses to the COVID-19 pandemic. OBJECTIVE We aimed to examine the responses of electronic cigarette Twitter users to the COVID-19 pandemic using Twitter data. METHODS The COVID-19 dataset contained COVID-19-related Twitter posts (tweets) between March 5th, 2020 and April 3rd, 2020. Ecig group included Twitter users who didn’t have commercial accounts but ever retweeted e-cigarette promotion posts between May 2019 and August 2019. Twitter users who didn’t post or retweet any e-cigarette-related tweets were defined as Non-Ecig group. Sentiment analysis was conducted to compare sentiment scores towards the COVID-19 pandemic between both groups. Topic modeling was used to compare the main topics discussed between the two groups. RESULTS The US COVID-19 dataset consisted of 1,112,558 COVID-19-related tweets from 15,657 unique Twitter users in the Ecig group and 9,789,584 COVID-19-related tweets from 2,128,942 unique Twitter users in the Non-Ecig group. Sentiment analysis showed that the Ecig group have more negative sentiment scores than the Non-Ecig group. Results from topic modeling indicated the Ecig group had more concern about COVID-19 related death, while the Non-Ecig group cared more about the government’s responses to the COVID-19 pandemic. CONCLUSIONS Electronic cigarette Twitter users has more concern towards the COVID-19 pandemic. Twitter is a useful tool to timely monitor public responses to the COVID-19 pandemic.

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User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study (Preprint)

10.2196/preprints.17280 ◽

2019 ◽

Author(s):

Xinyi Lu ◽

Long Chen ◽

Jianbo Yuan ◽

Joyce Luo ◽

Jiebo Luo ◽

...

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Electronic Cigarette ◽

Web Based ◽

User Perceptions ◽

Twitter Data ◽

Youth And Young Adults ◽

Data Driven Approach ◽

Social Media Platforms ◽

Fruit Flavor

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.

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User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study

Journal of Medical Internet Research ◽

10.2196/17280 ◽

2020 ◽

Vol 22 (6) ◽

pp. e17280 ◽

Cited By ~ 4

Author(s):

Xinyi Lu ◽

Long Chen ◽

Jianbo Yuan ◽

Joyce Luo ◽

Jiebo Luo ◽

...

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Electronic Cigarette ◽

Web Based ◽

User Perceptions ◽

Twitter Data ◽

Youth And Young Adults ◽

Data Driven Approach ◽

Social Media Platforms ◽

Fruit Flavor

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.

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Hybrid Classification Technique for Sentiment Analysis of the Twitter Data

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9116.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1750-1753

Keyword(s):

Social Media ◽

Random Forest ◽

Sentiment Analysis ◽

Research Work ◽

Related Data ◽

Twitter Data ◽

Opinion Extraction ◽

Social Media Platforms ◽

Hybrid Classification ◽

Media Platform

Sentiment can be described in the form of any type of approach, thought or verdict which results because of the occurrence of certain emotions. This approach is also known as opinion extraction. In this approach, emotions of different peoples with respect to meticulous rudiments are investigated. For the attainment of opinion related data, social media platforms are the best origins. Twitter may be recognized as a social media platform which is socially accessible to numerous followers. When these followers post some message on twitter, then this is recognized as tweet. The sentiment of twitter data can be analyzed with the feature extraction and classification approach. The hybrid classification is designed in this work which is the combination of KNN and random forest. The KNN classifier extract features of the dataset and random forest will classify data. The approach of hybrid classification is applied in this research work for the sentiment analysis. The performance of the proposed model is tested in terms of accuracy and execution time.

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Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set (Preprint)

10.2196/preprints.19273 ◽

2020 ◽

Author(s):

Emily Chen ◽

Kristina Lerman ◽

Emilio Ferrara

Keyword(s):

Social Media ◽

Data Collection ◽

Application Programming Interface ◽

Time Data ◽

Data Set ◽

Twitter Data ◽

Social Media Platforms ◽

Application Programming ◽

Twitter Activity ◽

Online Conversation

BACKGROUND At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter. OBJECTIVE In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. METHODS We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. RESULTS Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. CONCLUSIONS It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.

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Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter

Data and Information Management ◽

10.2478/dim-2020-0032 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Yi Zhao ◽

Haixu Xi ◽

Chengzhi Zhang

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Topic Modeling ◽

Latent Dirichlet Allocation ◽

Experimental Results ◽

Social Implications ◽

Income Levels ◽

Related Information ◽

The Social ◽

Twitter Users

AbstractCoronavirus disease 2019 (COVID-19) pandemic-related information are flooded on social media, and analyzing this information from an occupational perspective can help us to understand the social implications of this unprecedented disruption. In this study, using a COVID-19-related dataset collected with the Twitter IDs, we conduct topic and sentiment analysis from the perspective of occupation, by leveraging Latent Dirichlet Allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model, respectively. The experimental results indicate that there are significant topic preference differences between Twitter users with different occupations. However, occupation-linked affective differences are only partly demonstrated in our study; Twitter users with different income levels have nothing to do with sentiment expression on covid-19-related topics.

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COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

Frontiers in Public Health ◽

10.3389/fpubh.2021.812735 ◽

2022 ◽

Vol 9 ◽

Author(s):

Zunera Jalil ◽

Ahmed Abbasi ◽

Abdul Rehman Javed ◽

Muhammad Badruddin Khan ◽

Mozaherul Hoque Abul Hasanat ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Deep Learning ◽

Sentiment Analysis ◽

Social Networking Sites ◽

Daily Lives ◽

The Public ◽

Learning Techniques ◽

Social Media Platforms ◽

Twitter Users

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

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Researching with Twitter timeline data: A demonstration via “everyday” socio-political talk around welfare provision

Big Data & Society ◽

10.1177/2053951718766624 ◽

2018 ◽

Vol 5 (1) ◽

pp. 205395171876662 ◽

Cited By ~ 1

Author(s):

Phillip Brooker ◽

Julie Barnett ◽

John Vines ◽

Shaun Lawson ◽

Tom Feltwell ◽

...

Keyword(s):

Social Media ◽

Narrative Analysis ◽

Keyword Search ◽

User Engagement ◽

Political Issue ◽

Welfare Provision ◽

Twitter Data ◽

Political Talk ◽

Social Media Platforms ◽

Twitter Users

Increasingly, social media platforms are understood by researchers to be valuable sites of politically-relevant discussions. However, analyses of social media data are typically undertaken by focusing on ‘snapshots’ of issues using query-keyword search strategies. This paper develops an alternative, less issue-based, mode of analysing Twitter data. It provides a framework for working qualitatively with longitudinally-oriented Twitter data (user-timelines), and uses an empirical case to consider the value and the challenges of doing so. Exploring how Twitter users place “everyday” talk around the socio-political issue of UK welfare provision, we draw on digital ethnography and narrative analysis techniques to analyse 25 user-timelines and identify three distinctions in users’ practices: users’ engagements with welfare as TV entertainment or as a socio-political concern; the degree of sustained engagement with said issues, and; the degree to which users’ tweeting practices around welfare were congruent with or in contrast to their other tweets. With this analytic orientation, we demonstrate how a longitudinal analysis of user-timelines provides rich resources that facilitate a more nuanced understanding of user engagement in everyday socio-political discussions online.

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#AllforJan: How Twitter Users in Europe Reacted to the Murder of Ján Kuciak—Revealing Spatiotemporal Patterns through Sentiment Analysis and Topic Modeling

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10090585 ◽

2021 ◽

Vol 10 (9) ◽

pp. 585

Author(s):

Tamás Kovács ◽

Anna Kovács-Győri ◽

Bernd Resch

Keyword(s):

Topic Modeling ◽

Prime Minister ◽

Spatial Information ◽

European Countries ◽

Twitter Data ◽

Political Narrative ◽

Social Media Platforms ◽

Twitter Users ◽

Source Of Information

Social media platforms such as Twitter are considered a new mediator of collective action, in which various forms of civil movements unite around public posts, often using a common hashtag, thereby strengthening the movements. After 26 February 2018, the #AllforJan hashtag spread across the web when Ján Kuciak, a young journalist investigating corruption in Slovakia, and his fiancée were killed. The murder caused moral shock and mass protests in Slovakia and in several other European countries, as well. This paper investigates how this murder, and its follow-up events, were discussed on Twitter, in Europe, from 26 February to 15 March 2018. Our investigations, including spatiotemporal and sentiment analyses, combined with topic modeling, were conducted to comprehensively understand the trends and identify potential underlying factors in the escalation of the events. After a thorough data pre-processing including the extraction of spatial information from the users’ profile and the translation of non-English tweets, we clustered European countries based on the temporal patterns of tweeting activity in the analysis period and investigated how the sentiments of the tweets and the discussed topics varied over time in these clusters. Using this approach, we found that tweeting activity resonates not only with specific follow-up events, such as the funeral or the resignation of the Prime Minister, but in some cases, also with the political narrative of a given country affecting the course of discussions. Therefore, we argue that Twitter data serves as a unique and useful source of information for the analysis of such civil movements, as the analysis can reveal important patterns in terms of spatiotemporal and sentimental aspects, which may also help to understand protest escalation over space and time.

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