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

Twitter users perceptions of AI-based e-learning technologies

Today, teaching and learning paths increasingly intersect with technologies powered by emerging artificial intelligence (AI).This work analyses public opinions and sentiments about AI applications that affect e-learning, such as ChatGPT, virtual and augmented reality, microlearning, mobile learning, adaptive learning, and gamification. The way people perceive technologies fuelled by artificial intelligence can be tracked in real time in microblog messages promptly shared by Twitter users, who currently constitute a large and ever-increasing number of individuals. The observation period was from November 30, 2022, the date on which ChatGPT was launched, to March 31, 2023. A two-step sentiment analysis was performed on the collected English-language tweets to determine the overall sentiments and emotions. A latent Dirichlet allocation model was built to identify commonly discussed topics in tweets. The results show that the majority of opinions are positive. Among the eight emotions of the Syuzhet package, 'trust' and 'joy' are the most common positive emotions observed in the tweets, while 'fear' is the most common negative emotion. Among the most discussed topics with a negative outlook, two particular aspects of fear are identified: an 'apocalyptic-fear' that artificial intelligence could lead the end of humankind, and a fear for the 'future of artistic and intellectual jobs' as AI could not only destroy human art and creativity but also make the individual contributions of students and researchers not assessable. On the other hand, among the topics with a positive outlook, trust and hope in AI tools for improving efficiency in jobs and the educational world are identified. Overall, the results suggest that AI will play a significant role in the future of the world and education, but it is important to consider the potential ethical and social implications of this technology. By leveraging the positive aspects of AI while addressing these concerns, the education system can unlock the full potential of this emerging technology and provide a better learning experience for students.

Development and validation of a method for preparing heated tobacco product aerosol condensate (HTPAC) for large-scale toxicity data acquisition

A method of preparing heated tobacco product aerosol condensate (HTPAC) was developed to expedite HTP toxicity evaluation, and the effectiveness was assessed. To prepare HTPAC, HTP aerosol was generated and collected using a Cambridge filter (particulate phase) and Dulbecco's phosphate buffered saline (DPBS; gaseous phase). The aerosol collected on the Cambridge filter was extracted using methanol, which was thereafter removed by nitrogen purging. The HTP aerosol residue was mixed with DPBS loaded with the collected HTP vapor, ultimately yielding HTPAC. Nicotine and formaldehyde, key harmful compounds in HTP aerosol, were detected in HTPAC (901 ± 224 and 22.2 ± 3.90 µg stick-1, respectively, comparable to those in HTP aerosol (990-1350 (nicotine) and 2.33-21.9 µg stick-1 (formaldehyde)). Propylene glycol and vegetable glycerin, which influence the amount of HTP aerosol, were detected at similar levels in HTPAC and HTP aerosol (propylene glycol = 616 ± 57.1 (HTPAC) and 320-630 µg stick-1 (aerosol) and vegetable glycerin = 2418 ± 224 (HTPAC) and 1667-4000 µg stick-1 (aerosol)). Known components of HTP aerosol (hydroxyacetone, acetic acid, triacetin, and 2-furanmethanol) were also detected in HTPAC. Consequently, HTPAC offers an effective method for concentrating harmful compounds found in HTP aerosols. This, in turn, facilitates comprehensive toxicity assessments, paving the way for guidelines ensuring the safe utilization of HTP.

Covid-19 vaccines in Italian public opinion: Identifying key issues using Twitter and Natural Language Processing

The COVID-19 pandemic has changed society and people's lives. The vaccination campaign started December 27th 2020 in Italy, together with most countries in the European Union. Social media platforms can offer relevant information about how citizens have experienced and perceived the availability of vaccines and the start of the vaccination campaign. This study aims to use machine learning methods to extract sentiments and topics relating to COVID-19 vaccination from Twitter. Between February and May 2021, we collected over 71,000 tweets containing vaccines-related keywords from Italian Twitter users. To get the dominant sentiment throughout the Italian population, spatial and temporal sentiment analysis was performed using VADER, highlighting sentiment fluctuations strongly influenced by news of vaccines' side effects. Additionally, we investigated the opinions of Italians with respect to different vaccine brands. As a result, 'Oxford-AstraZeneca' vaccine was the least appreciated among people. The application of the Dynamic Latent Dirichlet Allocation (DLDA) model revealed three fundamental topics, which remained stable over time: vaccination plan info, usefulness of vaccinating and concerns about vaccines (risks, side effects and safety). To the best of our current knowledge, this one the first study on Twitter to identify opinions about COVID-19 vaccination in Italy and their progression over the first months of the vaccination campaign. Our results can help policymakers and research communities track public attitudes towards COVID-19 vaccines and help them make decisions to promote the vaccination campaign.

Tweet topics and sentiments relating to distance learning among Italian Twitter users

The outbreak of COVID-19 forced a dramatic shift in education, from in-person learning to an increased use of distance learning over the past 2 years. Opinions and sentiments regarding this switch from traditional to remote classes can be tracked in real time in microblog messages promptly shared by Twitter users, who constitute a large and ever-increasing number of individuals today. Given this framework, the present study aims to investigate sentiments and topics related to distance learning in Italy from March 2020 to November 2021. A two-step sentiment analysis was performed using the VADER model and the syuzhet package to understand the overall sentiments and emotions. A dynamic latent Dirichlet allocation model (DLDA) was built to identify commonly discussed topics in tweets and their evolution over time. The results show a modest majority of negative opinions, which shifted over time until the trend reversed. Among the eight emotions of the syuzhet package, ‘trust’ was the most positive emotion observed in the tweets, while ‘fear’ and ‘sadness’ were the top negative emotions. Our analysis also identified three topics: (1) requests for support measures for distance learning, (2) concerns about distance learning and its application, and (3) anxiety about the government decrees introducing the red zones and the corresponding restrictions. People’s attitudes changed over time. The concerns about distance learning and its future applications (topic 2) gained importance in the latter stages of 2021, while the first and third topics, which were ranked highly at first, started a steep descent in the last part of the period. The results indicate that even if current distance learning ends, the Italian people are concerned that any new emergency will bring distance learning back into use again.

COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing

The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. Twenty (20) positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.

Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach

Background

The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally.

Objective

This paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data.

Methods

We applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19–related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes.

Results

A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research.

Conclusions

We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.

Estimating E-Cigarette Use Prevalence among US Adolescents UsingVaping-Related Online Search Trends

Introduction: Adolescent e-cigarette use is a developing phenomenon. Greater surveillance of underage use is necessary to inform e-cigarette policy and mitigate adolescent e-cigarette use. Accurate prevalence estimates for adolescent e-cigarette use are provided by large national surveys. However, these surveys are costly and provide only annual estimates. To obtain more affordable estimates faster and more frequently, novel methods are required. Methods: Online search term popularity data were taken from Google Trends. Interest in vaping-related search terms were followed monthly from January 2011 to November 2020. Time-lagged zero-normalized cross-correlations were performed between the Google data and current (past 30 day) high-school e-cigarette use prevalence estimates from the National Youth Tobacco Survey (NYTS). The search interest data were then calibrated to the NYTS data to estimate adolescent e-cigarette use prevalence using online searches. Results: Maximum correlation coefficients of 0.979 for "vapes" and 0.938 for "vape" were obtained when search interest lagged use prevalence by one month, and 0.970 for "vape pen" when the lag was two months (p < 0.001 for all). Calibrating the search term data to NYTS provided a high-school current e-cigarette use prevalence estimate of 12.1-18.4% for November 2020, suggesting adolescent use of e-cigarettes has continued to decline since the NYTS estimate of 19.6% for January-March 2020. Conclusions: Online search trend data may provide reasonably reliable and more frequent estimates of adolescent e-cigarette use prevalence at substantially lower costs than traditional surveys. Such additional data may help to assess immediate impacts of policies and events.