scholarly journals Potential impact of a COVID-19 and smoking paper on Twitter users’ attitudes toward smoking: Observational Study (Preprint)

10.2196/25010 ◽  
2020 ◽  
Author(s):  
Chunliang Tao ◽  
Destiny Diaz ◽  
Zidian Xie ◽  
Long Chen ◽  
Dongmei Li ◽  
...  
2018 ◽  
Vol 36 (15_suppl) ◽  
pp. 5541-5541
Author(s):  
Jessica Michalak ◽  
Mike Hernandez ◽  
Alexandria Laws ◽  
Lovell Jones ◽  
Diane C. Bodurka ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Claudie Lamoureux ◽  
Charles-Antoine Guilloux ◽  
Clémence Beauruelle ◽  
Stéphanie Gouriou ◽  
Sophie Ramel ◽  
...  

AbstractStrict anaerobes are undeniably important residents of the cystic fibrosis (CF) lung but are still unknowns. The main objectives of this study were to describe anaerobic bacteria diversity in CF airway microbiota and to evaluate the association with lung function. An observational study was conducted during eight months. A hundred and one patients were enrolled in the study, and 150 sputum samples were collected using a sterile sample kit designed to preserve anaerobic conditions. An extended-culture approach on 112 sputa and a molecular approach (quantitative PCR targeting three of the main anaerobic genera in CF lung: Prevotella, Veillonella, and Fusobacterium) on 141 sputa were developed. On culture, 91.1% of sputa were positive for at least one anaerobic bacterial species, with an average of six anaerobic species detected per sputum. Thirty-one anaerobic genera and 69 species were found, which is the largest anaerobe diversity ever reported in CF lungs. Better lung function (defined as Forced Expiratory Volume in one second > 70%) was significantly associated with higher quantification of Veillonella. These results raise the question of the potential impact of anaerobes on lung function.


2020 ◽  
Author(s):  
Chunliang Tao ◽  
Destiny Diaz ◽  
Zidian Xie ◽  
Long Chen ◽  
Dongmei Li ◽  
...  

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.


2020 ◽  
Author(s):  
Marichi Gupta ◽  
Adity Bansal ◽  
Bhav Jain ◽  
Jillian Rochelle ◽  
Atharv Oak ◽  
...  

Objective: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals' perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users' perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time. Materials and Methods: We collected 166,005 tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion. Results: We identified 28,555 relevant tweets and estimate that 40.4% indicate uncertainty about weather's impact, 33.5% indicate no effect, and 26.1% indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion. Discussion: There is no consensus among the public for weather's potential impact. Earlier months were characterized by tweets that were uncertain of weather's effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza's seasonality, President Trump's comments on weather's effect, and social distancing. Conclusion: There is a major gap between scientific evidence and public opinion of weather's impacts on COVID-19. We provide evidence of public's misconceptions and topics of discussion, which can inform public health communications.


2000 ◽  
Vol 64 (9) ◽  
pp. 641-650 ◽  
Author(s):  
JK Yip ◽  
JL Hay ◽  
JS Ostroff ◽  
RK Stewart ◽  
GD Cruz

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