Forecasting COVID-19 Vaccination Rates using Social Media Data

The COVID-19 pandemic has had a profound impact on the global community, and vaccination has been recognized as a crucial intervention. To gain insight into public perceptions of COVID-19 vaccines, survey studies and the analysis of social media platforms have been conducted. However, existing methods lack consideration of individual vaccination intentions or status and the relationship between public perceptions and actual vaccine uptake. To address these limitations, this study proposes a text classification approach to identify tweets indicating a user's intent or status on vaccination. A comparative analysis between the proportions of tweets from different categories and real-world vaccination data reveals notable alignment, suggesting that tweets may serve as a precursor to actual vaccination status. Further, regression analysis and time series forecasting were performed to explore the potential of tweet data, demonstrating the significance of incorporating tweet data in predicting future vaccination status. Finally, clustering was applied to the tweet sets with positive and negative labels to gain insights into underlying focuses of each stance. © 2023 ACM.

Analyzing Public Concerns Over COVID-19 Variants Using Social Media

SARS-CoV-2, or more popularly known COVID-19 has claimed more than 5.5 million lives since it has been declared as a global pandemic. Similar to other viruses, COVID-19 is also undergoing several mutations and has many variants like Alpha, Beta, Gamma, Delta, Omicron and others. With so many variants, social media users are confused and posting their frustrations and angers with Tweets or Posts in public social media platforms. These publicly accessible social media posts provide a wealth of information for a social scientist or political leader or a strategic decision maker. This study demonstrates a feasible approach to extract meaningful critical information from social media posts. By programmatically accessing Twitter database from 11th January 2022 till 20th January 2022, we retrieved almost 9 K Tweet messages on 6 different keywords like “COVID Variants”, “Omicron”, “Alpha Variant”, “Beta Variant”, “Gamma Variant” and “Delta Variant”. Results were compared against metrics like users, posts, engagement, and influence. Omicron was found to be the most popular topic compared to other variants with an influence score of 70.2 million and 2.1 K posts during the monitored period. The most popular sources for influences on COVID-19 Variant related posts were found to be @reuters with 24.2M, @forbes with 17.4M, @timesofindia with 14.2M and @inquirerdotnet with 3.4 followers. This study also found out that the most popular Tweet languages were English followed by French and Dutch. Lastly, this study ranked user mentions, word frequency (with word cloud) and hashtags for COVID-19 Variant related twitter posts during the monitored timeframe.

Current Trend of Virology in the Post COVID-19 Era with Related Topic-Based Tweet Analysis

In light of current trends in virology, we performed social media analysis of 13 main topics in the area of virology and ranked these topics with metrics such as users, posts, engagement, and influence. These metrics were monitored against the 13 keywords on Twitter for the same period (i.e., from 27 November to 6 December 2021) for benchmarking purposes. The 13 main topics were "virological Science", " preventive vaccines", "therapeutic vaccines", "viral pathogenesis", "viral immunology", "antiviral strategies", "virus structure", "virus expression", "viral resistance", "emerging viruses", "interspecies transmission", "viruses and cancer" and " viral diseases". "viral diseases" recorded the highest number of users (i.e., 905 users) and the highest number of post (i.e., about 1K posts). The second-highest number of posts were monitored to be on "therapeutic vaccines" with 729 posts from 691 users. In terms of engagement, "viral diseases" (3.4 K) were found to be on the top followed by "viruses and cancer" (3.1K). Lastly, in terms of influence, "viral diseases" recorded 9.0 million influences followed by 6.6 million influences on "emerging viruses". In summary, "viral diseases" was found to be the most engaging and influential topic highest with the highest number of posts from most of the tweet users. In relation to trending hashtags in virology, #COVID19 recorded the highest number of hashtags, followed by # omicron, #sarscov2, #publichealth, #omicronvarient, #wuhan, #originofcovid, #fauci and #epidemiology. Word clouds showing the main area of discussion were also generated for these 13 main topics.

Sentiment Classification and Comparison of Covid-19 Tweets During the First Wave and the Second Wave Using NLP Techniques and Libraries

This research focuses on analysing the sentiments of people pertaining to severe periodic outbreaks of COVID-19 on two junctures – First Wave (Mar’20 & Apr’20) and Second Wave (Jun’21 & Jul’21)-since the first lockdown was undertaken with a view to curb the vicious spread of the lethal SARS-Cov-2 strain. Primarily, the objective is to analyse the public sentiment – as evident in the posted tweets - relating to the different phases of the pandemic, and to illuminate how keeping an eye on change in the tenor and tone of discussions can help government authorities and healthcare industry in raising awareness, reducing panic amongst citizens, and planning strategies to tackle the monumental crisis. Considering the daily volume of social media activity, in our project, we scoped to analyse the Tweets related to the two different pandemic stages – The First wave and the Second wave – by implementing Text Mining and Sentiment Analysis, subfields of Natural Language Processing. To manually extract tweets from the platform, we used Twitter API coupled with Python’s open-source package using a set of COVID-19-related keywords. Crucially, before finalising the project pipeline, we conducted a thorough secondary research to find the solutions and methodologies implemented in our area of interest. We listed the current works and attempted to plug the gaps in those via our experiment. We used several classification and boosting algorithms to create a framework to distinguish the textual data of the tweets. Relevant scope, limitations, and room for improvements have been discussed comprehensively in the upcoming sections. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Tweet Analysis using Hybrid Keyword Extraction Approach

Aim of this paper is to identify the key issues discussed among the people on Twitter using machine learning and NLP techniques regarding COVID-19. One of the most important way to produce these insights is Automatic Keyword Extraction. It is a method to obtain the most important words from the text by summarizing thus providing an insight into the whole context. Text Summarization is the process to condense the content without the loss of significant data. This paper applies a hybrid model of graph-based and topic modeling approaches to extract keywords from a large dataset of approximately 1 million tweets. © 2021 IEEE.

A Machine Learning Approach for Sentiment Analysis to Nurture Mental Health Amidst COVID-19

During the pandemic, when fresh news content is generated every minute about the widespread of the virus, many conversations revolve around the spread and cure of the contagion. At the hands of a commoner who posts news about COVID-19 on social media, the news may manifest itself to accommodate the said person's fear or negative propaganda which can potentially trigger a mass panic outbreak or can disrupt the mental health of a reader. This research discusses the application of Machine Learning in Sentiment Analysis to classify Tweets about Coronavirus as fear sentiment or panic sentiment. It proposes the idea of a web-based application that caters to filter out the fear-inducing sentiment from a user's daily Twitter feed, thus giving the user accurate and well-spirited information. Textual analysis is performed along with necessary textual data visualization. A substantial accuracy of 91% is achieved in the classification of brief Tweets using the Naïve Bayes method. An accuracy of 74% is achieved using the Logistic Regression classification method for brief tweets. This depicts the advancements in the field of sentimental analysis and sheds light on how it can be employed amidst a challenging situation like the pandemic to preserve mental health. © 2021 ACM.

A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets.

Undoubtedly, coronavirus (COVID-19) has caused one of the biggest challenges of all times. The ongoing COVID-19 pandemic has caused more than 150 million infected cases and one million deaths globally as of May 5, 2021. Understanding the sentiment of people expressed in their social media comments can help in monitoring, controlling, and ultimately eradicating the disease. This is a sensitive matter as the threat of infectious disease significantly affects the way people think and behave in various ways. In this study, we proposed a novel method based on the fusion of four deep learning and one classical supervised machine learning model for sentiment analysis of coronavirus-related tweets from eight countries. Also, we analyzed coronavirus-related searches using Google Trends to better understand the change in the sentiment pattern at different times and places. Our findings reveal that the coronavirus attracted the attention of people from different countries at different times in varying intensities. Also, the sentiment in their tweets is correlated to the news and events that occurred in their countries including the number of newly infected cases, number of recoveries and deaths. Moreover, common sentiment patterns can be observed in various countries during the spread of the virus. We believe that different social media platforms have great impact on raising people's awareness about the importance of this disease as well as promoting preventive measures among people in the community.

Unlink the Link Between COVID-19 and 5G Networks: An NLP and SNA Based Approach.

Social media facilitates rapid dissemination of information for both factual and fictional information. The spread of non-scientific information through social media platforms such as Twitter has potential to cause damaging consequences. Situations such as the COVID-19 pandemic provides a favourable environment for misinformation to thrive. The upcoming 5G technology is one of the recent victims of misinformation and fake news and has been plagued with misinformation about the effects of its radiation. During the COVID-19 pandemic, conspiracy theories linking the cause of the pandemic to 5G technology have resonated with a section of people leading to outcomes such as destructive attacks on 5G towers. The analysis of the social network data can help to understand the nature of the information being spread and identify the commonly occurring themes in the information. The natural language processing (NLP) and the statistical analysis of the social network data can empower policymakers to understand the misinformation being spread and develop targeted strategies to counter the misinformation. In this paper, NLP based analysis of tweets linking COVID-19 to 5G is presented. NLP models including Latent Dirichlet allocation (LDA), sentiment analysis (SA) and social network analysis (SNA) were applied for the analysis of the tweets and identification of topics. An understanding of the topic frequencies, the inter-relationships between topics and geographical occurrence of the tweets allows identifying agencies and patterns in the spread of misinformation and equips policymakers with knowledge to devise counter-strategies.