ABSTRACT
This study draws on corpus methodology to investigate people's reactions to COVID-19 vaccination using the data of Macau netizens' comments on a YouTube channel. Four main topics under discussion were identified based on the word lists. Meanwhile, people were concerned about the activity of vaccines and were also engaged in heated debates on both domestic and foreign vaccines according to the collocation of "疫苗” yìmiáo (vaccine). The discussion topics and concerns varied along with time, evidenced by the results of word lists and collocates of each month. It is also noticeable that some misinformation on vaccines burgeoned and faded before and after the mass vaccination of Macau residents. The supportive voices for the (Chinese) vaccines were building up their momentum over time. This phenomenon lends support to the effective persuasion of gain-framed messages in advocating safe behaviour based on Prospect Theory. Our research has revealed that the corpus-based study of online comments can be leveraged to uncover people's social behaviour in the pandemic context. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories: positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%. © 2022 IEEE.
ABSTRACT
Online public opinion is a collection of netizens' emotions, attitudes, opinions, opinions and so on. With the development of the Internet, the influence of online public opinion on social stability is increasing day by day. This paper takes the 'COVID-19' event as an example, crawls the relevant news and comment data released by People's Daily, and firstly divides public opinion events into four stages according to the news popularity and life cycle theory: Tf-idf algorithm is used to strengthen the selection of key feature words in the corpus. Finally, LDA theme model is used to identify the topic of public opinion and mine the evolution law of network public opinion, which is helpful to effectively guide and control network public opinion and plays an important role in social stability. © 2022 IEEE.
ABSTRACT
With social media pervading all aspects of our life, the opinions expressed by netizens are a gold mine ready to be exploited in a meaningful way to influence all major public do-mains. Sentiment analysis is a way to interpret this unstructured data using AI tools. It is a well-known fact that there has been a 'Zoom Boom' in the field of aesthetic plastic surgery due to the COVID-19 pandemic and the same has put the focus of attention sharply on our appearance. Polarity detection of tweets published on popular aesthetic plastic surgery procedures before and after the onset of COVID can provide great insights for aesthetic plastic surgeons and the health industry at large. In this work, we develop an end-to-end system for the sentiment analysis of such tweets incorporating a state-of-the-art fine-tuned deep learning model, an ingenious 'keyword search and filter approach' and SenticNet. Our system was tested on a large database of 196,900 tweets and the results were visualized using affectively correct word clouds and also subjected to rigorous statistical hypothesis testing to draw meaningful inferences. The results showed a high level of statistical significance. © 2022 IEEE.
ABSTRACT
In the context of the COVID-19 epidemic, the development and popularization of vaccines have effectively alleviated people's panic. Twitter, as one of the world's largest social platforms, promptly reflects the trend of emotional changes in screen names. Currently, vaccines such as Pfizer, Sputnik, and Moderna have successfully made a large number of people gain high immunity against the COVID-19 virus. However, a few cases of death due to vaccines have caused some people to question and worry about the safety of vaccines. A comprehensive understanding of progress of vaccine popularization is conducive making wiser decisions and calming people's panic. Since the large number of Tweets updated daily on Twitter can represent attitudes of netizens on the progress of vaccination, we used Bert model to predict and classify emotion categories to which different Tweets belong, with an accuracy rate of 80%. It is found that with the promotion of vaccination, fluctuation of netizen sentiment for vaccine progress has gradually decreased. Tweets with neutral sentiment still account for a majority of proportion, and the proportion of tweets with positive sentiment has gradually increased. In addition, we used LSTM model to predict the growth of cases with MSE less than 0.001. The growth of new cases in most countries gradually decreased to less than 10, 000 people per day after June. Therefore, most vaccines have made significant progress in both winning public support and preventing COVID-19 infection. © VDE VERLAG GMBH · Berlin · Offenbach.
ABSTRACT
Since the beginning of 2020, China has banned the consumption of wild animals to combat the spread of zoonoses. Most existing studies focus on the intention and behavior of wildlife consumption and their causes;however, few have looked at public willingness to resist wildlife consumption, as well as the cause and effects of such actions. In this study, a framework for an extended theory of planned behavior was constructed. Based on a 7-point Likert scale, a sample of 1194 respondents from eight provinces across China was obtained through an online survey. Structural equation modeling was used to analyze netizen behavioral intention to resist consuming wild animals and their causes to provide a reference for the implementation and optimization of relevant policies. The study model passed the goodness-of-fit test, confirming the robustness of the results. The results showed that Chinese netizens’ intention to resist consuming wild animals was moderate, with 55.19% willing to participate in activities against it, i.e., it is important to resist eating wild animals as a standard. Attitude, subjective norm, perceived behavioral control, and past experience of the Chinese netizen had significant positive effects on resistance intention, i.e., (1) netizens’ current living area with severe outbreaks were more likely to resist wildlife consumption, (2) highly knowledge level netizens were more likely to resist wildlife consumption than less knowledgeable ones, and (3) lower income level had higher behavioral intentions of netizens. The findings suggest that the government must take a lead role in wildlife protection and strengthen its restrictions, laws, and regulations. The media should also be used to promote conservation and popularize a protective message in favor of wild animals. Public quality and assurance of wildlife protection should be culturally reinforced to effectively ban the illegal trade of wild animals and their products.
ABSTRACT
Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data. © 2022 IEEE.
ABSTRACT
The study presents an attempt to analyse how social media netizens in Malaysia responded to the calls for “Social Distancing” and “Physical Distancing” as the newly recommended social norm was introduced to the world as a response to the COVID-19 global pandemic. The pandemic drove a sharp increase in social media platforms’ use as a public health communication platform since the first wave of the COVID-19 outbreak in Malaysia in April 2020. We analysed thousands of tweets posted by Malaysians daily between January 2020 and August 2021 to determine public perceptions and interactions patterns. The analysis focused on positive and negative reactions and the interchanges of uses of the recommended terminologies “social distancing” and “physical distancing”. Using linguistic analysis and natural language processing, findings dominantly indicate influences from the multilingual and multicultural values held by Malaysian netizens, as they embrace the concept of distancing as a measure of global public health safety. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.