scholarly journals Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1110
Author(s):  
G. G. Md. Nawaz Ali ◽  
Md. Mokhlesur Rahman ◽  
Md. Amjad Hossain ◽  
Md. Shahinoor Rahman ◽  
Kamal Chandra Paul ◽  
...  
Keyword(s):  
Public Policy ◽  
Temporal Dynamics ◽  
Herd Immunity ◽  
Public Perceptions ◽  
Policy Implications ◽  
Social Media Data ◽  
Public Sentiment ◽  
The Us ◽  
Negative Sentiment ◽  
Media Data

There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.

scholarly journals Public Perceptions about COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

2021 ◽  
Author(s):  
G. G. Md. Nawaz Ali ◽  
Md. Mokhlesur Rahman ◽  
Md. Amjad Hossain ◽  
Md. Shahinoor Rahman ◽  
Kamal Chandra Paul ◽  
...  
Keyword(s):  
Public Policy ◽  
Temporal Dynamics ◽  
Herd Immunity ◽  
Healthcare Management ◽  
Policy Implications ◽  
Management Communication ◽  
Public Sentiment ◽  
The Us ◽  
Twitter Users ◽  
Important Time

There exists a compelling need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February of 2021 and late March of 2021 shows that in spite of overall strength of positive sentiment, and increasing numbers of Americans being fully vaccinated, negative sentiment about COVID-19 vaccines still persists among sections of people who are hesitant towards the vaccine. In this study, we performed sentiment analytics on vaccine tweets, studied changes in public sentiment over time, conducted vaccination sentiment validation using actual vaccination data from the US CDC and Household Pulse Survey (HPS), explored influence of maturity of Twitter user-accounts and generated geographic mapping of sentiments by location of Twitter users. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework which was developed for COVID-19 sentiment analytics, to systematically analyze directions for public policy processes to potentially improve the administration of vaccines. Application of the PSS framework provides important time sensitive insights for state and federal government agencies and associated organizations to better implement public policy processes for healthcare management, communication, transparency, motivation and societal operational policies such as social distancing. These insights are expected to contribute to processes that can expedite the vaccination program and move closer to the cherished herd immunity goal.

scholarly journals Artificial Intelligence-enabled analysis of social media data to understand public perceptions of COVID-19 contact tracing apps (Preprint)

10.2196/26618 ◽  
2020 ◽  
Author(s):  
Kathrin Cresswell ◽  
Ahsen Tahir ◽  
Zakariya Sheikh ◽  
Zain Hussain ◽  
Andrés Domínguez Hernández ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Public Perceptions ◽  
Contact Tracing ◽  
Social Media Data ◽  
Media Data

Analysing the features of negative sentiment tweets

2018 ◽  
Vol 36 (5) ◽  
pp. 782-799 ◽  
Author(s):  
Ling Zhang ◽  
Wei Dong ◽  
Xiangming Mu
Keyword(s):  
Research Area ◽  
Small Sample ◽  
Negative Word ◽  
Social Media Data ◽  
Content Type ◽  
Word Use ◽  
Part Of Speech ◽  
Negative Sentiment ◽  
Media Data

Purpose This paper aims to address the challenge of analysing the features of negative sentiment tweets. The method adopted in this paper elucidates the classification of social network documents and paves the way for sentiment analysis of tweets in further research. Design/methodology/approach This study classifies negative tweets and analyses their features. Findings Through negative tweet content analysis, tweets are divided into ten topics. Many related words and negative words were found. Some indicators of negative word use could reflect the degree to which users release negative emotions: part of speech, the density and frequency of negative words and negative word distribution. Furthermore, the distribution of negative words obeys Zipf’s law. Research limitations/implications This study manually analysed only a small sample of negative tweets. Practical implications The research explored how many categories of negative sentiment tweets there are on Twitter. Related words are helpful to construct an ontology of tweets, which helps people with information retrieval in a fixed research area. The analysis of extracted negative words determined the features of negative tweets, which is useful to detect the polarity of tweets by machine learning method. Originality/value The research provides an initial exploration of a negative document classification method and classifies the negative tweets into ten topics. By analysing the features of negative tweets, related words, negative words, the density of negative words, etc. are presented. This work is the first step to extend Plutchik’s emotion wheel theory into social media data analysis by constructing filed specific thesauri, referred to as local sentimental thesauri.

scholarly journals Sentiment Analysis Methods for HPV Vaccines Related Tweets Based on Transfer Learning

Healthcare ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 307
Author(s):  
Li Zhang ◽  
Haimeng Fan ◽  
Chengxia Peng ◽  
Guozheng Rao ◽  
Qing Cong
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Hpv Vaccination ◽  
Experimental Results ◽  
Fine Tuning ◽  
Hpv Vaccines ◽  
Social Media Data ◽  
Public Sentiment ◽  
Media Data

The widespread use of social media provides a large amount of data for public sentiment analysis. Based on social media data, researchers can study public opinions on human papillomavirus (HPV) vaccines on social media using machine learning-based approaches that will help us understand the reasons behind the low vaccine coverage. However, social media data is usually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limits the application of deep learning methods in effectively training models. To tackle this problem, we propose three transfer learning approaches to analyze the public sentiment on HPV vaccines on Twitter. One was transferring static embeddings and embeddings from language models (ELMo) and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWE-BiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called fine-tuning generative pre-training (GPT) and fine-tuning bidirectional encoder representations from transformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pre-training (GPT) model. The fine-tuned BERT model was constructed with BERT model. The experimental results on the HPV dataset demonstrated the efficacy of the three methods in the sentiment analysis of the HPV vaccination task. The experimental results on the HPV dataset demonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. The fine-tuned BERT model outperforms all other methods. It can help to find strategies to improve vaccine uptake.

scholarly journals From Claiming Credit to Avoiding Blame: The Evolution of Congressional Strategy for Asbestos Management

1991 ◽  
Vol 11 (2) ◽  
pp. 153-186 ◽  
Author(s):  
Charlotte Twight
Keyword(s):  
Public Policy ◽  
Early Stage ◽  
The United States ◽  
Public Perceptions ◽  
Information Costs ◽  
The Public ◽  
Congressional Behavior ◽  
The Us ◽  
Blame Avoidance ◽  
Credit Claiming

ABSTRACTThis paper develops a theory synthesizing credit-claiming and blameavoidance explanations of congressional behavior and evaluates it against asbestos policy in the United States from the 1920s through the 1980s. Public policy is viewed as shaped by officeholders' ability to achieve political ends through augmenting information costs and other transaction costs facing the public. Public perceptions are seen both as the endogenous product of congressional information-cost manipulation and as an exogenous constraint that changes in identifiable ways over time. Different policy stances - open credit claiming, concealed credit claiming, early-stage blame avoidance, and full-scale blame avoidance – are predicted to emerge in response to specified conditions, yielding implications about the expected timing of public policy changes. Specific types of transaction-cost manipulation are predicted to accompany the identified policy stances. The US asbestos policy experience is shown to be consistent with the predictions of the model.

scholarly journals Visual Sentiment Analysis on Social Media Data

2021 ◽  
pp. 366-372
Author(s):  
Harshala Bhoir ◽  
K. Jayamalini
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Social Media Data ◽  
Media Image ◽  
The Social ◽  
Textual Data ◽  
Art Works ◽  
Negative Sentiment ◽  
Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.

scholarly journals Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing (Preprint)

2020 ◽  
Author(s):  
Oladapo Oyebode ◽  
Chinenye Ndulue ◽  
Ashfaq Adib ◽  
Dinesh Mulchandani ◽  
Banuchitra Suruliraj ◽  
...  
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Language Processing ◽  
Social Issues ◽  
Public Perceptions ◽  
Psychosocial Issues ◽  
Social Media Data ◽  
Health Related ◽  
The Impact ◽  
Media Data

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 behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as 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 globally using social media data. METHODS We apply 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 collect over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we perform data preprocessing which involves applying NLP techniques to clean and prepare the data for automated theme extraction. Third, we apply context-aware NLP approach to extract meaningful keyphrases or themes from over 1 million randomly-selected comments, as well as compute sentiment scores for each theme and assign sentiment polarity (i.e., positive, negative, or neutral) based on the scores using lexicon-based technique. Fourth, we categorize related themes into broader themes. RESULTS A total of 34 negative themes emerged, out of which 15 are health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues are increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues include frustrations due to life disruptions, panic shopping, and expression of fear. Social issues include harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes include public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS We uncover various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommend interventions that can help address the health, psychosocial, and social issues based on the positive themes and other remedial ideas rooted in research. 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, as well as in reacting to any future pandemics.

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