Twitter Sentiment Research Articles

Understanding investors behaviors during the COVID-19 outbreak using Twitter sentiment analysis

The COVID-19 pandemic caused stock price volatility and a market crash in 2020. Such events were an opportunity to gain insights into investor psychology. We sought to examine investors behaviors during the COVID-19 outbreak by analyzing the correlation between COVID-19-related tweet sentiment and stock price movements. The analysis was done at three levels: the stock market, the industry sector, and individual company stocks. We hypothesized that the investors would initially underestimate the health risk associated with the pandemic, but as time progressed, acknowledge the health risk and start panic selling. By analyzing the correlation between tweet sentiment and stock prices, we discovered that after the World Health Organization (WHO) declared the outbreak a Public Health Emergency of International Concern (PHEIC), investors displayed a tendency to disregard the health risk, coinciding with an increase in stock prices. However, the U.S. Centers for Disease Control and Prevention (CDC) warning and the pandemic declaration coincided with a period during which investors may have displayed a sense of apprehension, with the stock prices declining at the same time. To validate the sentiment analysis results, we also identified the most-used words in tweets and analyzed the correlation between tweet features and stock prices. Our work showed that, along with economic variables, behavioral factors like investor sentiment help explain the market’s behavior. Our work also highlighted the value of using a mixed methods approach to study complex processes in the real world.

CBOE Volatility Index Forecasting under COVID-19: An Integrated BiLSTM-ARIMA-GARCH Model

After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The results revealed that during COVID-19, the proposed integrated model, which trained both the Twitter sentiment values and historical VIX values, presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models.

Analysis of Twitter Sentiments About the Russian-Ukraine War Using Naive Bayes Based on Particle Swarm Optimization

The Russia-Ukraine crisis has not found a solution until now, at least until November 2022. Many Indonesians have expressed their opinions on this matter via social media Twitter. This study uses the Naïve Bayes (NB) Algorithm based on Particle Swarm Optimization (PSO) to analyze opinions regarding the RussiaUkraine war. Data was taken from Twitter using the keywords "Russia Ukraine", "Russia vs Ukraine", "Russia-Ukraine", and "Perang Russia". The number of data sets taken is 5000 data. The results showed that the accuracy of the Naïve Bayes (NB) algorithm without Particle Swarm Optimization (PSO) was 67.72%, the precision value was 58.33%, the recall value was 79.75%, and the error rate of 57.14% while Naïve Bayes (NB) with Particle Swarm Optimization (PSO) the accuracy obtained is 73.48%, the precision value is 65.62%, the recall value is 76.36% and error rate of 50.36%. Thus it can be said that the Particle Swarm Optimization (PSO) applied to the Naïve Bayes algorithm for objects in research can increase the accuracy of results.

Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

Handling sentiment drifts in real time twitter data streams are a challenging task while performing sentiment classifications, because of the changes that occur in the sentiments of twitter users, with respect to time. The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time. This work proposes an adaptive learning algorithm-based framework, Twitter Sentiment Drift Analysis-Bidirectional Encoder Representations from Transformers (TSDA-BERT), which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time. The framework also works on static data by converting them to data streams using the Kafka tool. The experiments conducted on real time and simulated tweets of sports, health care and financial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model, with accuracies of 91%, 87% and 90%, respectively. Though the results have been provided only for a few topics, as a proof of concept, this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

A Review on Twitter Sentiment Analysis Using ML

Abstract: Social Media sites like twitter have billions of people share their opinions day by day as tweets. As tweet is characteristic short and basic way of human emotions. So, in this paper we focused on sentiment analysis of Twitter data. Most of Twitter's existing sentiment analysis solutions basically consider only the textual information of Twitter messages and strives to work well in the face of short and ambiguous Twitter messages. Recent studies show that patterns of spreading feelings on Twitter have close relationships with the polarities of Twitter messages. In this paper focus on how to combine the textual information of Twitter messages and sentiment dissemination models to get a better performance of sentiment analysis in Twitter data. To this end, proposed system first analyses the diffusion of feelings by studying a phenomenon called inversion of feelings and find some interesting properties of the reversal of feelings. Therefore, we consider the interrelations between the textual information of Twitter messages and the patterns of diffusion of feelings, and propose random forest machine learning to predict the polarities of the feelings expressed in Twitter messages. As far as we know, this work is the first to use sentiment dissemination models to improve Twitter's sentiment analysis. Numerous experiments in the real-world dataset show that, compared to state-of-the-art text-based analysis algorithms.

Twitter Sentiment Analysis for Covid-19 Vaccines in Turkey based on BiGRU-CNN Deep Learning Model

Günümüzde, sosyal medya platformları duyguları ifade etmenin en iyi yoludur. Yaklaşık iki yıldır, Covid-19 yeni koronavirüs salgının ortaya çıkması tüm dünyada olduğu gibi ülkemizde de insanların üzerinde benzeri görülmemiş karmaşık duygular yarattı. Covid-19’a karşı aşı çalışmalarının başlamasından sonra insanların duyguları daha karmaşık hale geldi. Daha yakın zamanda, Covid-19’un Delta, Omicron vb. varyantlarının çıkması da toplumda yeniden büyük bir korku yarattı. İnsanlar, bu süreçte duygu ve düşüncelerini paylaşmak üzere Twitter gibi sosyal medya araçlarına yöneldi. Twitter’da duygu analizi yapmak çok önemli ve zorlu bir görevdir. Bu çalışmada amacımız, derin öğrenme mimarilerinin gücünden faydalanarak Türk halkının aşılama süreciyle ilgili farklı duygularını araştırmak ve halkın mevcut aşılama girişimlerine yönelik tepkilerine genel bir bakış sunmaktır. Çalışmada, Twitter’da 16 Haziran 2021 ve 18 Eylül 2021 arasında paylaşılan Türkçe tweetler toplanmıştır. İnsanların her türden aşılarla ilgili duyguları, doğal dil işleme (NLP) aracı olan TextBlob kullanılarak değerlendirildi. Daha sonra, duygu sınıflandırması için yeni bir model önerildi. Önerilen model, Glove kelime gömme vektörüyle tek katmanlı Çift-yönlü Geçitli Tekrarlayan Birim (Bi-GRU) ve Evrişimli Sinir Ağı (CNN) modelini kullanan BiGRU-CNN modelidir. Önerilen yöntemin deneysel sonuçları en son modellerle kıyaslandığında umut vericidir. Bu çalışma, halkın COVID-19 aşıları hakkındaki görüşlerinin anlaşılmasını geliştirmekte ve koronavirüsü dünyadan yok etme hedefini desteklemektedir.

Short Text Clustering Algorithms, Application and Challenges: A Survey

The number of online documents has rapidly grown, and with the expansion of the Web, document analysis, or text analysis, has become an essential task for preparing, storing, visualizing and mining documents. The texts generated daily on social media platforms such as Twitter, Instagram and Facebook are vast and unstructured. Most of these generated texts come in the form of short text and need special analysis because short text suffers from lack of information and sparsity. Thus, this topic has attracted growing attention from researchers in the data storing and processing community for knowledge discovery. Short text clustering (STC) has become a critical task for automatically grouping various unlabelled texts into meaningful clusters. STC is a necessary step in many applications, including Twitter personalization, sentiment analysis, spam filtering, customer reviews and many other social network-related applications. In the last few years, the natural-language-processing research community has concentrated on STC and attempted to overcome the problems of sparseness, dimensionality, and lack of information. We comprehensively review various STC approaches proposed in the literature. Providing insights into the technological component should assist researchers in identifying the possibilities and challenges facing STC. To gain such insights, we review various literature, journals, and academic papers focusing on STC techniques. The contents of this study are prepared by reviewing, analysing and summarizing diverse types of journals and scholarly articles with a focus on the STC techniques from five authoritative databases: IEEE Xplore, Web of Science, Science Direct, Scopus and Google Scholar. This study focuses on STC techniques: text clustering, challenges to short texts, pre-processing, document representation, dimensionality reduction, similarity measurement of short text and evaluation.

Real-Time Twitter Data Analysis: A Survey

Internet users are used to a steady stream of facts in the contemporary world. Numerous social media platforms, including Twitter, Facebook, and Quora, are plagued with spam accounts, posing a significant problem. These accounts are created to trick unwary real users into clicking on dangerous links or to continue publishing repetitious messages using automated software. This may significantly affect the user experiences on these websites. Effective methods for detecting certain types of spam have been intensively researched and developed. Effectively resolving this issue might be aided by doing sentiment analysis on these postings. Hence, this research provides a background study on Twitter data analysis, and surveys existing papers on Twitter sentiment analysis and fake account detection and classification. The investigation is restricted to the identification of social bots on the Twitter social media network. It examines the methodologies, classifiers, and detection accuracies of the several detection strategies now in use.

COVID vaccine stigma: detecting stigma across social media platforms with computational model based on deep learning

The study presents the first computational model of COVID vaccine stigma that can identify stigmatised sentiment with a high level of accuracy and generalises well across a number of social media platforms. The aim of the study is to understand the lexical features that are prevalent in COVID vaccine discourse and disputes between anti-vaccine and pro-vaccine groups. This should provide better insight for healthcare authorities, enabling them to better navigate those discussions. The study collected posts and their comments related to COVID vaccine sentiment in English, from Reddit, Twitter, and YouTube, for the period from April 2020 to March 2021. The labels used in the model, “stigma”, “not stigma”, and “undefined”, were collected from a smaller Facebook (Meta) dataset and successfully propagated into a larger dataset from Reddit, Twitter, and YouTube. The success of the propagation task and consequent classification is a result of state-of-the-art annotation scheme and annotated dataset. Deep learning and pre-trained word vector embedding significantly outperformed traditional algorithms, according to two-tailed P(T≤t) test and achieved F1 score of 0.794 on the classification task with three classes. Stigmatised text in COVID anti-vaccine discourse is characterised by high levels of subjectivity, negative sentiment, anxiety, anger, risk, and healthcare references. After the first half of 2020, anti-vaccination stigma sentiment appears often in comments to posts attempting to disprove COVID vaccine conspiracy theories. This is inconsonant with previous research findings, where anti-vaccine people stayed primarily within their own in-group discussions. This shift in the behaviour of the anti-vaccine movement from affirming climates to ones with opposing opinions will be discussed and elaborated further in the study.

Analysis of Twitter Sentiment Towards Madrasahs Using Classification Methods

In today's digital era, the influence and use of the internet has become a necessity, especially in Indonesia itself, internet users in early 2021 reached 202.6 million people. The most widely used internet use by Indonesians is social media. Several incidents of sexual violence that occurred in the madrasa environment as reported in the media, the emergence of radical Islamic issues which he said were the fruit of thoughts from the madrasa environment, terrorism which was also said to come from misinterpreting knowledge from madrasahs, intolerance to different religions, changes in the character of madrasah students and so on will cause negative thoughts towards madrasah. To find out how the sentiment of social media users towards madrasahs, a study was conducted on analisis twitter sentiment towards madrasah using the classification method. The methods used are Naïve Bayes (NB), Decision Tree (DT) and K – Nearest Neighbor (K-NN). Toimprove the performance of the classification method is carried out using the Particle Swarm Optimization (PSO) selection feature. On the other hand, tools gataframework, execute Python script dan rapidminer diguna kan jug a dalam penelitian this to membantu preprocessi ng dan cleansing pa da datasethingga membantu menciptaka n corpus dan sentiment ana lysis. Acuration obtained from the Naïve Bayes algorithm accuracy: 76.86% +/- 5.24% (micro average: 76.86%), Decision Tree accuracy: 61.38% +/- 5.46% (micro average: 61.35%), K-NN accuracy: 74.70% +/- 4.83% (micro average: 74.67%), Naïve Bayes PSO accuracy: 80.80% +/- 4.86% (micro average: 80.79%, Decision Tree PSO accuracy: 65.27% +/- 5.26% (micro average: 65.28%), and K-NN PSO accuracy: 67.24% +/- 7.92% (micro average: 67.25%). The results showed that the Naïve Bayes PSO algorithm got the best and accurate results. This study succeeded in obtaining an effective and best algorithm in classifying positive comments and negative comments related to sentiment analysis towards madrasahs by classification method.

Are Twitter sentiments during COVID-19 pandemic a critical determinant to predict stock market movements? A machine learning approach

The problem of stock market prediction is a challenging task owing to its complex nature and the numerous indirect factors at play. The sentiments regarding socio-political issues such as wars and pandemics can affect stock prices. The spread of the COVID-19 pandemic continues to take a toll on the economy and fluctuations in sentiment of the concerns about the health impacts of the disease can be captured from the microblogging platform, Twitter. We examined how these sentiments during the Covid-19 pandemic and the health impacts arising from the disease along with other macroeconomic indicators provide useful information to predict the stock indices in a more accurate manner. We developed a machine learning model namely, long-short term memory (LSTM) networks to predict the impact of the Covid-19 induced sentiments on the stock values of different sectors in the United States and India. We did the same predictions using the timeseries statistical models such as autoregressive moving average model and the linear regression model. We then compared the performance of the LSTM and the timeseries statistical models to find that the machine learning model has produced more accurate predictions of the stock indices. The performance of the models across the sectors and between the United States and India are compared to draw economic inferences.

جمال_خاشقجي# #JamalKhashoggi: Unraveling multilingual Twitter sentiment dynamics in a longitudinal comparative analysis of tweets in Arabic and English

In 2018, Jamal Khashoggi, a Saudi journalist who wrote for the Washington Post, was last seen alive entering the Saudi Consulate in Istanbul. Confirmed news of his murder ignited a heated and polarized public debate on Twitter. We use agenda melding as a theoretical lemma and argue that Twitter sentiment flourishes within multilingual, ad hoc public spheres contributing to an emotional agenda. We examined the Twitter sentiment from 2018 to 2021 by looking at the most popular hashtags used in both the Arabic-and-English language spheres. The daily sentiment analysis of 3,278,464 tweets revealed that both languages had a predominantly negative sentiment; however, the English sphere was more extreme in their emotional expression. An additional analysis of external media URLs found in a subsample of tweets highlighted distinct references to media discourse, emphasizing an East–West divide. Implications for global communication are discussed.

Sentiment Analysis of Twitter Data

Twitter has become a major social media platform and has attracted considerable interest among researchers in sentiment analysis. Research into Twitter Sentiment Analysis (TSA) is an active subfield of text mining. TSA refers to the use of computers to process the subjective nature of Twitter data, including its opinions and sentiments. In this research, a thorough review of the most recent developments in this area, and a wide range of newly proposed algorithms and applications are explored. Each publication is arranged into a category based on its significance to a particular type of TSA method. The purpose of this survey is to provide a concise, nearly comprehensive overview of TSA techniques and related fields. The primary contributions of the survey are the detailed classifications of numerous recent articles and the depiction of the current direction of research in the field of TSA.

Tweeting about alcohol: Exploring differences in Twitter sentiment during the onset of the COVID-19 pandemic.

This study explores pandemic-related changes in Twitter communication by examining differences in emotional, psychological and social sentiment between alcohol-related tweets and a random sample of non-alcohol tweets during the onset of the COVID-19 pandemic. Two equivalent size sets of English-language, COVID-specific tweets posted between February 1st and April 20th, 2020 are examined. The first set includes 1.5 million tweets containing alcohol-related keywords, while the second set does not contain such references. LIWC software analyzed the tweets for sentiment factors. ANCOVAs were used to determine whether language use significantly differed between the sets, considering differences in the pandemic period (before or after the pandemic declaration) while controlling for the number of tweets. The study found that tweets in the 40 days after March 11, 2020 contained more authentic language, more affiliation-oriented language, and exhibited more positive emotion than tweets in the 40 days pre-declaration. Alcohol-related status was a significant factor only when tweets contained personal concerns, regardless of pandemic period. Authenticity levels increased significantly in alcohol-related tweets post-declaration. The findings suggest alcohol may play a lesser role in the expression of psychological, social, and emotional sentiment than the pandemic period, but interaction between authentic language and alcohol references may reflect an increased use of alcohol for coping.

Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience.

Chronic dermatologic disorders can cause significant emotional distress. Sentiment analysis of disease-related tweets helps identify patients' experiences of skin disease. To analyze the expressed sentiments in tweets related to alopecia areata (AA), hidradenitis suppurativa (HS), and psoriasis (PsO) in comparison to fibromyalgia (FM). This is a cross-sectional analysis of Twitter users' expressed sentiment on AA, HS, PsO, and FM. Tweets related to the diseases of interest were identified with keywords and hashtags for one month (April, 2022) using the Twitter standard application programming interface (API). Text, account types, and numbers of retweets and likes were collected. The sentiment analysis was performed by the R "tidytext" package using the AFINN lexicon. A total of 1,505 tweets were randomly extracted, of which 243 (16.15%) referred to AA, 186 (12.36%) to HS, 510 (33.89%) to PsO, and 566 (37.61%) to FM. The mean sentiment score was -0.239 ± 2.90. AA, HS, and PsO had similar sentiment scores (p = 0.482). Although all skin conditions were associated with a negative polarity, their average was significantly less negative than FM (p < 0.0001). Tweets from private accounts were more negative, especially for AA (p = 0.0082). Words reflecting patients' psychological states varied in different diseases. "Anxiety" was observed in posts on AA and FM but not posts on HS and PsO, while "crying" was frequently used in posts on HS. There was no definite correlation between the sentiment score and the number of retweets or likes, although negative AA tweets from public accounts received more retweets (p = 0.03511) and likes (p = 0.0228). The use of Twitter sentiment analysis is a promising method to document patients' experience of skin diseases, which may improve patient care through bridging misconceptions and knowledge gaps between patients and healthcare professionals.

ANALISIS SENTIMEN TWITTER TERHADAP WACANA PENUNDAAN PEMILU DENGAN METODE SUPPORT VECTOR MACHINE

Machine learning plays an important role in managing important issues to classify and predict information that develops ahead of the General Election in Indonesia. Especially in knowing public sentiment on the discourse on the postponement of the 2024 election through Twitter social media. So it is necessary to analyze the discourse by categorizing it as positive or negative. The Support Vector Machine (SVM) model is used for analysis and classification. The sample data used were 100 tweets data which were then scraped in the period January 2022 – May 2022. The processing was done using Python programming and Jupyter Lab tools. Before doing the analysis, do preprocess to eliminate unnecessary words and information so that the level of accuracy of the results of this Twitter sentiment classification can provide a closer picture of reality. As for the results of the grouping carried out positive sentiment in as many as 40 data tweets and negative sentiment in as many as 60 data tweets. The classification test results on tweets data with a good level of accuracy of 92%. These results are expected to be a reference for future researchers who want to improve accuracy or analysis results.

Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python

People frequently use the social media site Twitter to share their thoughts and show their emotions on various occasions. Data analysis using sentiment analysis can retrieve the sentiment that the data contains. Twitter sentiment analysis applies sentiment analysis to data from tweets on the social media platform in order to identify user sentiments. The amount of study in this area has steadily increased over the last few decades. The complex format of tweets, which makes processing harder, is the cause of this. The very small size of the tweet format creates a whole new set of issues, such as the use of slang and acronyms. In this essay, we evaluate a few publications on sentiment analysis research on Twitter, detailing the methodology used and.

Comparative Analysis of Deep Learning Approaches for Twitter Text Classification

Abstract—Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly. Sentiment analysis aims to extract opinion automatically from data and classify them as positive and negative. Twitter widely used social media tools, been seen as an important source of information for acquiring people’s attitudes, emotions, views, and feedbacks. Within this context, Twitter sentiment analysis techniques were developed to decide whether textual tweets express a positive or negative opinion. In contrast to lower classification performance of traditional algorithms, deep learning models, including Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), have achieved a significant result in sentiment analysis. Keras is a Deep Learning (DL) framework that provides an embedding layer to produce the vector representation of words present in the document. The objective of this work is to analyze the performance of deep learning models namely Convolutional Neural Network (CNN), Simple Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), bidirectional Long Short-Term Memory (Bi-LSTM), BERT and RoBERTa for classifying the twitter reviews. From the experiments conducted, it is found that RoBERTa model performs better than CNN and simple RNN for sentiment classification. Keywords—Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Deep Learning, Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach (RoBERTa).

Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis.

In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.

Twitter sentiment analysis: An Arabic text mining approach based on COVID-19.

The 21st century has seen a lot of innovations, among which included the advancement of social media platforms. These platforms brought about interactions between people and changed how news is transmitted, with people now able to voice their opinion as opposed to before where only the reporters were speaking. Social media has become the most influential source of speech freedom and emotions on their platforms. Anyone can express emotions using social media platforms like Facebook, Twitter, Instagram, and YouTube. The raw data is increasing daily for every culture and field of life, so there is a need to process this raw data to get meaningful information. If any nation or country wants to know their people's needs, there should be mined data showing the actual meaning of the people's emotions. The COVID-19 pandemic came with many problems going beyond the virus itself, as there was mass hysteria and the spread of wrong information on social media. This problem put the whole world into turmoil and research was done to find a way to mitigate the spread of incorrect news. In this research study, we have proposed a model of detecting genuine news related to the COVID-19 pandemic in Arabic Text using sentiment-based data from Twitter for Gulf countries. The proposed sentiment analysis model uses Machine Learning and SMOTE for imbalanced dataset handling. The result showed the people in Gulf countries had a negative sentiment during COVID-19 pandemic. This work was done so government authorities can easily learn directly from people all across the world about the spread of COVID-19 and take appropriate actions in efforts to control it.