Investigation of Public Acceptance of Misinformation Correction in Social Media Based on Sentiment Attributions: Infodemiology Study Using Aspect-Based Sentiment Analysis.

BACKGROUND: The proliferation of misinformation on social media is a significant concern due to its frequent occurrence and subsequent adverse social consequences. Effective interventions for and corrections of misinformation have become a focal point of scholarly inquiry. However, exploration of the underlying causes that affect the public acceptance of misinformation correction is still important and not yet sufficient. OBJECTIVE: This study aims to identify the critical attributions that influence public acceptance of misinformation correction by using attribution analysis of aspects of public sentiment, as well as investigate the differences and similarities in public sentiment attributions in different types of misinformation correction. METHODS: A theoretical framework was developed for analysis based on attribution theory, and public sentiment attributions were divided into 6 aspects and 11 dimensions. The correction posts for the 31 screened misinformation events comprised 33,422 Weibo posts, and the corresponding Weibo comments amounted to 370,218. A pretraining model was used to assess public acceptance of misinformation correction from these comments, and the aspect-based sentiment analysis method was used to identify the attributions of public sentiment response. Ultimately, this study revealed the causality between public sentiment attributions and public acceptance of misinformation correction through logistic regression analysis. RESULTS: The findings were as follows: First, public sentiments attributed to external attribution had a greater impact on public acceptance than those attributed to internal attribution. The public associated different aspects with correction depending on the type of misinformation. The accuracy of the correction and the entity responsible for carrying it out had a significant impact on public acceptance of misinformation correction. Second, negative sentiments toward the media significantly increased, and public trust in the media significantly decreased. The collapse of media credibility had a detrimental effect on the actual effectiveness of misinformation correction. Third, there was a significant difference in public attitudes toward the official government and local governments. Public negative sentiments toward local governments were more pronounced. CONCLUSIONS: Our findings imply that public acceptance of misinformation correction requires flexible communication tailored to public sentiment attribution. The media need to rebuild their image and regain public trust. Moreover, the government plays a central role in public acceptance of misinformation correction. Some local governments need to repair trust with the public. Overall, this study offered insights into practical experience and a theoretical foundation for controlling various types of misinformation based on attribution analysis of public sentiment.

Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis.

BACKGROUND: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. OBJECTIVE: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. METHODS: We collected 31,100 English tweets containing COVID-19 vaccine-related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. RESULTS: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. CONCLUSIONS: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.

Quantified multidimensional public sentiment characteristics on social media for public opinion management: Evidence from the COVID-19 pandemic.

Background: Public sentiments arising from public opinion communication pose a serious psychological risk to public and interfere the communication of nonpharmacological intervention information during the COVID-19 pandemic. Problems caused by public sentiments need to be timely addressed and resolved to support public opinion management. Objective: This study aims to investigate the quantified multidimensional public sentiments characteristics for helping solve the public sentiments issues and strengthen public opinion management. Methods: This study collected the user interaction data from the Weibo platform, including 73,604 Weibo posts and 1,811,703 Weibo comments. Deep learning based on pretraining model, topics clustering and correlation analysis were used to conduct quantitative analysis on time series characteristics, content-based characteristics and audience response characteristics of public sentiments in public opinion during the pandemic. Results: The research findings were as follows: first, public sentiments erupted after priming, and the time series of public sentiments had window periods. Second, public sentiments were related to public discussion topics. The more negative the audience sentiments were, the more deeply the public participated in public discussions. Third, audience sentiments were independent of Weibo posts and user attributes, the steering role of opinion leaders was invalid in changing audience sentiments. Discussion: Since the COVID-19 pandemic, there has been an increasing demand for public opinion management on social media. Our study on the quantified multidimensional public sentiments characteristics is one of the methodological contributions to reinforce public opinion management from a practical perspective.

Public sentiments and the influence of information-seeking preferences on knowledge, attitudes, death conversation, and receptiveness toward palliative care: results from a nationwide survey in Singapore.

Background: Low awareness about palliative care among the global public and healthcare communities has been frequently cited as a persistent barrier to palliative care acceptance. Given that knowledge shapes attitudes and encourages receptiveness, it is critical to examine factors that influence the motivation to increase knowledge. Health information-seeking from individuals and media has been identified as a key factor, as the process of accessing and interpreting information to enhance knowledge has been shown to positively impact health behaviours. Objective: Our study aimed to uncover public sentiments toward palliative care in Singapore. A conceptual framework was additionally developed to investigate the relationship between information-seeking preferences and knowledge, attitudes, receptiveness of palliative care, and comfort in death discussion. Design and Methods: A nationwide survey was conducted in Singapore with 1226 respondents aged 21 years and above. The data were analysed through a series of hierarchical multiple regression to examine the hypothesised role of information-seeking sources as predictors. Results: Our findings revealed that 53% of our participants were aware of palliative care and about 48% were receptive to receiving the care for themselves. It further showed that while information-seeking from individuals and media increases knowledge, attitudes and receptiveness to palliative care, the comfort level in death conversations was found to be positively associated only with individuals, especially healthcare professionals. Conclusion: Our findings highlight the need for public health authorities to recognize people's deep-seated beliefs and superstitions surrounding the concept of mortality. As Asians view death as a taboo topic that is to be avoided at all costs, it is necessary to adopt multipronged communication programs to address those fears. It is only when the larger communicative environment is driven by the media to encourage public discourse, and concurrently supported by timely interventions to trigger crucial conversations on end-of-life issues between individuals, their loved ones, and the healthcare team, can we advance awareness and benefits of palliative care among the public in Singapore.

A nationwide survey to understand public sentiments and the extent that information-seeking preferences can increase knowledge, attitudes, receptiveness of palliative care, and comfort level in death discussion in Singapore Low awareness of palliative care is a barrier that persistently hinders palliative care acceptance among populations in developing and developed countries. As knowledge shapes attitudes and encourages receptiveness, it is vital that researchers uncover factors that influence the motivation to increase knowledge. Health information-seeking is a factor that deserves greater attention in palliative care research because the process of seeking out information on health concerns from other people or the media can greatly increase individuals' knowledge. As such, this nationwide survey involving 1226 participants was carried out in Singapore to understand the public sentiments toward palliative care. It further statistically analyzed if information-seeking (from individuals and the media) will increase knowledge, attitudes, receptiveness toward palliative care, and comfort level in death discussion. Our findings indicated that 53% of our participants were aware of palliative care and about 48% were receptive to receiving the care for themselves. Furthermore, while information-seeking from individuals and media increases knowledge, attitudes, and receptiveness to palliative care, people are only comfortable to engage in death discussion with individuals, especially healthcare professionals. Exposure to media alone is not enough to encourage individuals to want to talk about end-of-life issues including palliative care. As Asians view death as a taboo topic, it is important for public health authorities to recognize people's deep-seated beliefs and superstitions surrounding the concept of mortality. A multipronged communication program is therefore needed to address these fears. It is only when the larger communicative environment driven by the media to encourage public discourse, and concurrently supported by timely interventions to trigger crucial conversations on end-of-life issues between individuals, their loved ones, and the healthcare team, can we advance awareness and benefits of palliative care among the public in Singapore.

COVID-19 vaccine sentiment analysis using public opinions on Twitter.

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs.