Hydrometeorological disasters during COVID-19: Insights from topic modeling of global aid reports.
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ABSTRACT:
SUBMITTER: Malakar K
PROVIDER: S-EPMC9109990 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCOVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public's conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public's vaccine awareness through sentiment-based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines.ObjectiveIn this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved.MethodsWe collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter's application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment-based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns.ResultsOverall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign.ConclusionsTo the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment-based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign.
| S-EPMC8827037 | biostudies-literature
Project description:In an effort to gauge the global pandemic's impact on social thoughts and behavior, it is important to answer the following questions: (1) What kinds of topics are individuals and groups vocalizing in relation to the pandemic? (2) Are there any noticeable topic trends and if so how do these topics change over time and in response to major events? In this paper, through the advanced Sequential Latent Dirichlet Allocation model, we identified twelve of the most popular topics present in a Twitter dataset collected over the period spanning April 3rd to April 13th, 2020 in the United States and discussed their growth and changes over time. These topics were both robust, in that they covered specific domains, not simply events, and dynamic, in that they were able to change over time in response to rising trends in our dataset. They spanned politics, healthcare, community, and the economy, and experienced macro-level growth over time, while also exhibiting micro-level changes in topic composition. Our approach differentiated itself in both scale and scope to study the emerging topics concerning COVID-19 at a scale that few works have been able to achieve. We contributed to the cross-sectional field of urban studies and big data. Whereas we are optimistic towards the future, we also understand that this is an unprecedented time that will have lasting impacts on individuals and society at large, impacting not only the economy or geo-politics, but human behavior and psychology. Therefore, in more ways than one, this research is just beginning to scratch the surface of what will be a concerted research effort into studying the history and repercussions of COVID-19.
| S-EPMC9140268 | biostudies-literature
Project description:BackgroundVaccination is a cornerstone of the prevention of communicable infectious diseases; however, vaccines have traditionally met with public fear and hesitancy, and COVID-19 vaccines are no exception. Social media use has been demonstrated to play a role in the low acceptance of vaccines.ObjectiveThe aim of this study is to identify the topics and sentiments in the public COVID-19 vaccine-related discussion on social media and discern the salient changes in topics and sentiments over time to better understand the public perceptions, concerns, and emotions that may influence the achievement of herd immunity goals.MethodsTweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, the day the World Health Organization declared COVID-19 a pandemic, to January 31, 2021. We used R software to clean the tweets and retain tweets that contained the keywords vaccination, vaccinations, vaccine, vaccines, immunization, vaccinate, and vaccinated. The final data set included in the analysis consisted of 1,499,421 unique tweets from 583,499 different users. We used R to perform latent Dirichlet allocation for topic modeling as well as sentiment and emotion analysis using the National Research Council of Canada Emotion Lexicon.ResultsTopic modeling of tweets related to COVID-19 vaccines yielded 16 topics, which were grouped into 5 overarching themes. Opinions about vaccination (227,840/1,499,421 tweets, 15.2%) was the most tweeted topic and remained a highly discussed topic during the majority of the period of our examination. Vaccine progress around the world became the most discussed topic around August 11, 2020, when Russia approved the world's first COVID-19 vaccine. With the advancement of vaccine administration, the topic of instruction on getting vaccines gradually became more salient and became the most discussed topic after the first week of January 2021. Weekly mean sentiment scores showed that despite fluctuations, the sentiment was increasingly positive in general. Emotion analysis further showed that trust was the most predominant emotion, followed by anticipation, fear, sadness, etc. The trust emotion reached its peak on November 9, 2020, when Pfizer announced that its vaccine is 90% effective.ConclusionsPublic COVID-19 vaccine-related discussion on Twitter was largely driven by major events about COVID-19 vaccines and mirrored the active news topics in mainstream media. The discussion also demonstrated a global perspective. The increasingly positive sentiment around COVID-19 vaccines and the dominant emotion of trust shown in the social media discussion may imply higher acceptance of COVID-19 vaccines compared with previous vaccines.
| S-EPMC8244724 | biostudies-literature
Project description:Probabilistic topic models provide an unsupervised method for analyzing unstructured text, which have the potential to be integrated into clinical automatic summarization systems. Clinical documents are accompanied by metadata in a patient's medical history and frequently contains multiword concepts that can be valuable for accurately interpreting the included text. While existing methods have attempted to address these problems individually, we present a unified model for free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts. In the proposed model, phrases are represented by chained n-grams and a Dirichlet hyper-parameter is weighted by both document-level and patient-level context. This method and three other Latent Dirichlet allocation models were fit to a large collection of clinical reports. Examples of resulting topics demonstrate the results of the new model and the quality of the representations are evaluated using empirical log likelihood. The proposed model was able to create informative prior probabilities based on patient and document information, and captured phrases that represented various clinical concepts. The representation using the proposed model had a significantly higher empirical log likelihood than the compared methods. Integrating document metadata and capturing phrases in clinical text greatly improves the topic representation of clinical documents. The resulting clinically informative topics may effectively serve as the basis for an automatic summarization system for clinical reports.
| S-EPMC4902330 | biostudies-literature
Project description:BackgroundThe COVID-19 outbreak highlighted the importance of rapid access to research.ObjectiveThe aim of this study was to investigate research communication related to COVID-19, the level of openness of papers, and the main topics of research into this disease.MethodsOpen access (OA) uptake (typologies, license use) and the topic evolution of publications were analyzed from the start of the pandemic (January 1, 2020) until the end of a year of widespread lockdown (March 1, 2021).ResultsThe sample included 95,605 publications; 94.1% were published in an OA form, 44% of which were published as Bronze OA. Among these OA publications, 42% do not have a license, which can limit the number of citations and thus the impact. Using a topic modeling approach, we found that articles in Hybrid and Green OA publications are more focused on patients and their effects, whereas the strategy to combat the pandemic adopted by different countries was the main topic of articles selecting publication via the Gold OA route.ConclusionsAlthough OA scientific production has increased, some weaknesses in OA practice, such as lack of licensing or under-researched topics, still hold back its effective use for further research.
| S-EPMC9531723 | biostudies-literature
Project description:BackgroundThe COVID-19 outbreak was designated a global pandemic on March 11, 2020. The relationship between vaping and contracting COVID-19 is unclear, and information on the internet is conflicting. There is some scientific evidence that vaping cannabidiol (CBD), an active ingredient in cannabis that is obtained from the hemp plant, or other substances is associated with more severe manifestations of COVID-19. However, there is also inaccurate information that vaping can aid COVID-19 treatment, as well as expert opinion that CBD, possibly administered through vaping, can mitigate COVID-19 symptoms. Thus, it is necessary to study the spread of inaccurate information to better understand how to promote scientific knowledge and curb inaccurate information, which is critical to the health of vapers. Inaccurate information about vaping and COVID-19 may affect COVID-19 treatment outcomes.ObjectiveUsing structural topic modeling, we aimed to map temporal trends in the web-based vaping narrative (a large data set comprising web-based vaping chatter from several sources) to indicate how the narrative changed from before to during the COVID-19 pandemic.MethodsWe obtained data using a textual query that scanned a data pool of approximately 200,000 different domains (4,027,172 documents and 361,100,284 words) such as public internet forums, blogs, and social media, from August 1, 2019, to April 21, 2020. We then used structural topic modeling to understand changes in word prevalence and semantic structures within topics around vaping before and after December 31, 2019, when COVID-19 was reported to the World Health Organization.ResultsBroadly, the web-based vaping narrative can be organized into the following groups or archetypes: harms from vaping; Vaping Regulation; Vaping as Harm Reduction or Treatment; and Vaping Lifestyle. Three archetypes were observed prior to the emergence of COVID-19; however, four archetypes were identified post-COVID-19 (Vaping as Harm Reduction or Treatment was the additional archetype). A topic related to CBD product preference emerged after COVID-19 was first reported, which may be related to the use of CBD by vapers as a COVID-19 treatment.ConclusionsOur main finding is the emergence of a vape-administered CBD treatment narrative around COVID-19 when comparing the web-based vaping narratives before and during the COVID-19 pandemic. These results are key to understanding how vapers respond to inaccurate information about COVID-19, optimizing treatment of vapers who contract COVID-19, and possibly minimizing instances of inaccurate information. The findings have implications for the management of COVID-19 among vapers and the monitoring of web-based content pertinent to tobacco to develop targeted interventions to manage COVID-19 among vapers.
| S-EPMC7641646 | biostudies-literature
Project description:This investigation employed the Asymmetric Power GARCH model and found that COVID-19 substantially harms the US and Japan's market returns. Moreover, COVID-19 has influenced the variance of the US, Germany, and Italy's stock markets more than the Global Financial Crises (GFC). However, GFC indicated a more significant impact on the financial volatility of the Nikkei 225 index and SSEC than COVID-19. The study confirmed the leverage effect for the S&P 500, Nasdaq Composite Index, DAX 30, Nikkei 225, FTSE MIB, and SSEC. The analysis authenticated that the health crisis that befell due to COVID-19 have imperatively originated the financial crisis globally; however, the Asian markets still make available better prospects for portfolio optimization.
| S-EPMC7318935 | biostudies-literature
Project description:Newspapers are very important for a society as they inform citizens about the events around them and how they can impact their life. Their importance becomes more crucial and indispensable in the times of health crisis such as the current COVID-19 pandemic. Since the starting of this pandemic newspapers are providing rich information to the public about various issues such as the discovery of a new strain of coronavirus, lockdown and other restrictions, government policies, and information related to the vaccine development for the same. In this scenario, analysis of emergent and widely reported topics/themes/issues and associated sentiments from various countries can help us better understand the COVID-19 pandemic. In our research, the database of more than 100,000 COVID-19 news headlines and articles were analyzed using top2vec (for topic modeling) and RoBERTa (for sentiment classification and analysis). Our topic modeling results highlighted that education, economy, US, and sports are some of the most common and widely reported themes across UK, India, Japan, South Korea. Further, our sentiment classification model achieved 90% validation accuracy and the analysis showed that the worst affected country, i.e. the UK (in our dataset) also has the highest percentage of negative sentiment.
| S-EPMC8545217 | biostudies-literature
Project description:The COVID-19 infections have profoundly and negatively impacted the whole world. Hence, we have modeled the dynamic spread of global COVID-19 infections with the connectedness approach based on the TVP-VAR model, using the data of confirmed COVID-19 cases during the period of March 23rd, 2020 to September 10th, 2021 in 18 countries. The results imply that, (i) the United States, the United Kingdom and Indonesia are global epidemic centers, among which the United States has the highest degree of the contagion of the COVID-19 infections, which is stable. South Korea, France and Italy are the main receiver of the contagion of the COVID-19 infections, and South Korea has been the most severely affected by the overseas epidemic; (ii) there is a negative correlation between the timeliness, effectiveness and mandatory nature of government policies and the risk of the associated countries COVID-19 epidemic affecting, as well as the magnitude of the net contagion of domestic COVID-19; (iii) the severity of domestic COVID-19 epidemics in the United States and Canada, Canada and Mexico, Indonesia and Canada is almost equivalent, especially for the United States, Canada and Mexico, whose domestic epidemics are with the same tendency; (iv) the COVID-19 epidemic has spread though not only the central divergence manner and chain mode of transmission, but also the way of feedback loop. Thus, more efforts should be made by the governments to enhance the pertinence and compulsion of their epidemic prevention policies and establish a systematic and efficient risk assessment mechanism for public health emergencies.
| S-EPMC8795671 | biostudies-literature