Information Engineering Express
International Institute of Applied Informatics
2024, Vol. 10, No. 1, IEE804

Analysis of International Interests and Emotional
Responses to the Coronavirus Pandemic
Satoshi Fukuda *, Hidetsugu Nanba *, Hiroko Shoji *Abstract
The outbreak of coronavirus disease 2019 (COVID-19) in December 2019 is still exerting a
global impact in 2022, with various media outlets reporting related news items on a daily basis.
We analyzed the interest in, and emotional reactions to, COVID-19 of people around the world,
as expressed on Twitter. As a measure of interest, we examined replies to news tweets posted byfour news outlets (Yahoo! News, The Wall Street Journal, The Guardian, and The Times of India),
and classified the emotional content of each reply tweet using Plutchik’s wheel of emotion. The
analysis suggested that negative sentiment prevailed worldwide between January 2020 and May
2022; fear-related tweets were significantly more common from January to February 2020 thanin the other months in all news reports. However, anticipation-related tweets were more common
than those in all other emotion categories in October–November 2021 in Japan. We also analyzed
the factors that contributed to the rise of a particular emotion by tracing the news to which tweets
with the emotion replied. Our approach that used the news and reply tweets was useful in approximating the factors of the emotional reactions of people to COVID-19.
Keywords: Twitter, Sentiment Analysis, COVID-19

1 Introduction
The coronavirus disease 2019 (COVID-19) outbreak that began in December 2019 was a novel
event with global consequences. Subsequently, variants including the Delta and Omicron strains
were identified, and policies were implemented by governments to control the spread of COVID19 including mask-wearing recommendations, lockdowns and vaccination programs. Many people learned about these developments by watching the news. For events of interest to the general
population, the media may provide new reports whenever there are developments; various media
outlets reported a range of COVID-19 related news on a daily basis. The total numbers of tweets
in reply to COVID-19 related news posted on Twitter between January 1, 2020 (when COVID19 emerged as a newsworthy topic worldwide) and May 31, 2022 by several news outlets (Yahoo! News, The Wall Street Journal, The Guardian, and The Times of India) are shown in Figure
1. In this figure, the tweets matching the query (corona (コロナ) OR covid) posted by each news
outlets are counted by month. It can be seen that the number of COVID-19 related news items*

Chuo University, Tokyo, Japan2

S. Fukuda, H. Nanba, H. Shojipeaked between March and April 2020. Since that time, the news tweets have gradually declined,
but even in May 2022, over 100 of news has been posted. From this, COVID-19 has been one of
the most important news topics in the past 2 years for people around the world. When covid-19
was discovered, many people would have been concerned about it as an unknown virus, but thecontinued reporting of analysis and government countermeasure information on this virus would
have helped people gain a better understanding and awareness. However, awareness of new problems related to the event or ongoing problems that persist may cause more interest, and someindividuals may continue to feel anxious. We are interested in understanding how people’s levelof interest and emotional responses change over time in response to news reports related to the
COVID-19, and the related factors. Research on this topic would help to investigate what triggers
people’s feelings of anxiety and security, and clarify the differences and common elements in theemotions expressed by people from around the world.
To investigate changes in people’s interests and emotions in relation to COVID-19, we analyzed reply tweets to tweets posted by the news outlets shown in Figure 1. Reply tweets are theindicator of people’s level of interest in the news dealing with specific events. Another advantage
of using reply is that can collect tweets that mention or comment on news that are not matchedby query-based searches. In our analysis of the emotional responses, we used binary classifierstrained on a labeled dataset based on Plutchik’s wheel of emotions [1]. Plutchik distinguishedeight primary emotions: joy, sadness, anticipation, surprise, anger, fear, disgust, and trust. By
analyzing the emotional content of the reply tweets, the emotional responses of the general population could be tracked over the course of the COVID-19 pandemic. Furthermore, we traced thetweets back to the original news items when a particular emotion is aroused. We demonstrate that
our approach using news items, their reply tweets, and emotional classification is useful inproviding an overview of the factors that have changed people’s emotions.
The rest of this paper is organized as follows: the second section reviews the relevant literature;
the third section describes the approach used to collect reply tweets to COVID-19-related news
tweeted by the various media outlets, the emotion classification system, and the text analysis
approach; the fourth section discusses the results of the tweet analysis; and the last section summarizes the findings.

Figure 1: Numbers of coronavirus-related tweets by four major news outlets.

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Analysis of International Interests and Emotional Responses to the Coronavirus Pandemic

2 Related Work
Many studies have identified various topics emerging during the COVID-19 pandemic, and haveanalyzed people’s feelings about these topics, and changes therein over time. In a Brazilian study,
De and Figueiredo [2] applied sentiment analysis to news articles and tweets, to identify changes
in sentiment over time to COVID-19-related topics. Ghasiya and Okamura [3] analyzed news
articles on COVID-19 in four countries, while other studies used Top2Vec [4] and RoBERTa [5]to analyze differences in sentiment among countries. Other studies performing sentiment analysis
focused on specific themes and topics emerging during the COVID-19 pandemic. Bhagat et al.
[6] applied sentiment analysis to blog and newspaper articles using TextBlob [7], with the goalof understanding public opinion of online learning during the COVID-19 pandemic. Wang et al.
[8] analyzed controversial topics related to mask-wearing and vaccination in the United Statesusing latent Dirichlet allocation [9]. Specifically, they analyzed how interest in these topics
changed over time using sentiment scores calculated by TextBlob, and identified factors associated with fluctuations in those scores. Hu et al. [10] analyzed how sentiments and opinions about
vaccines evolved in the United States according to two critical events: clinical trials of theModerna vaccine in March 1, 2020 and the administration of the first dose of a COVID-19 vaccine in February 28, 2021. Yousefinaghani et al. [11] used VADER [12] to classify tweets pertaining to the COVID-19 vaccine as negative or positive, in various countries including theUnited States, United Kingdom, and Canada, and extracted keywords for each class. They thendefined “anti-vaccine,” “hesitant,” and “pro-vaccine” categories, and again identified keywords
associated with each category. Finally, they analyzed the volume of tweets in each category bycountry. In the current study, we determined how people’s interest in and feelings about COVID19 around the world changed over time. We approximated this analysis by examining tweets inreply to news items.

3 Analysis Approach
3.1 Overview
We analyzed COVID-19-related tweets posted by major news outlets (Yahoo! News, The WallStreet Journal, The Guardian, and The Times of India), and tweets made in reply to those posts.
We calculated the total and average number of reply tweets by month, and examined changes
therein. Through this approach, we tracked public interest in COVID-19. We also classified the
reply tweets on the basis of the eight emotions defined in Plutchik’s wheel of emotion, and examined changes in their relative proportions. Through this analysis, we identified common anddistinct factors associated with changes in the emotional responses of people worldwide to the
COVID-19 pandemic. In addition, we traced the reply tweets back to the original news tweets to
identify the events that evoked emotional responses. Section 3.2 describes the tweet collection
process, and Section 3.3 describes the construction of the emotional classification model.

3.2 Collection of Tweet Data
Reply tweets to the news tweets described in Section 1 were collected. Specifically, the accountname of the news outlet beginning with @ as a query was entered. Then, using tweet ID thatshows the reply-to tweet included in each tweet, each reply tweet was linked to the corresponding
news tweet. The Yahoo! News reply tweets were all in Japanese, while those for the other outlets
were all in English. The data collection period was January 1, 2020 to May 31, 2022. Table 1

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S. Fukuda, H. Nanba, H. Shojishows the total number of reply tweets for each news outlet.

3.3 Classification Model
We used machine learning to construct a binary tweet classification model. We describe the dataset and parameter settings used to construct the binary classifiers, as well as the classification
performance for each emotion, below.

3.3.1 Dataset
For the classification of Japanese tweets, we used the Japanese emotion analysis dataset compiled
by Kajiwara et al. [13]. This dataset contains 43,200 texts posted on social networking services
labeled with Plutchik’s eight emotions on four levels of emotional intensity (none, weak, medium,and strong). In addition, the labels included five types: one applied by the writer of the text, three
applied by three readers of the text, and one by averaging the emotional intensities assigned by
the three readers. We used 40,000 data for training, 1,200 data for validation, and 2,000 data forevaluation. Labels representing the average emotional intensity by the three readers were used,with weak, medium, and strong emotional intensity being positive, and none being negative.
For the classification of English tweets, we used the English emotion analysis dataset from theEmotion Classification (E-c) task, which is included in the dataset distributed in SemEval 2018
Task 1: Affect in Tweets [14]. This dataset contains 6,838, 886, and 3,259 data subsets for training,validation, and evaluation, respectively, and each text posted on a social networking service islabeled with 11 different emotions (Anger, Anticipation, Disgust, Fear, Joy, Love, Optimism,
Pessimism, Sadness, Surprise, Trust). We used eight of the eleven emotions (Anger, Anticipation,
Disgust, Fear, Joy, Sadness, Surprise, and Trust) to unify our classification with the emotion labels used in the dataset of Kajiwara et al.

3.3.2 Parameter Settings
As a classification model, we used BERT [15]. Parameters were set to 32 for batch size, 10 for
epochs, 1e-6 for learning rate, and 128 for the number of tokens. Adam [16] was used for optimization. As a learning model for Japanese and English, we used “bert-based-japanese-wholeword-masking”† and “bert-base-uncased”‡, respectively.

3.3.3 Classification Performance
Table 2 shows the classification performance for each emotion, separately for Japanese and English data. These classifiers were used to classify the reply tweets. Each tweet could be classified
into multiple emotion categories because of the binary classification approach.

†‡https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking
https://huggingface.co/bert-base-uncased

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Analysis of International Interests and Emotional Responses to the Coronavirus Pandemic

Table 1: Total number of reply tweets for each news outlet
News Outlet
Yahoo! News
The Wall Street Journal
The Guardian
The Times of India

Account name@YahooNewsTopics@WSJ@guardian@timesofindia

Total number of reply tweets
898,222
181,911
186,465
105,223

Table 2: Classification performance for each emotional category

Joy
Sadness
Anticipation
Surprise
Anger
Fear
DisgustTrust

4

Japanese
RecallPrecision
0.693
0.784
0.567
0.635
0.779
0.758
0.421
0.621
0.063
0.500
0.333
0.619
0.138
0.678
0.093
0.438

EnglishRecallPrecision
0.767
0.765
0.118
0.510
0.738
0.725
0.732
0.722
0.861
0.835
0.545
0.646
0.758
0.700
0.131
0.434

Results and Discussion

4.1 Distribution of Reply Tweets
Numbers of reply tweets to news tweets per month and monthly average number of reply tweets
by news outlet are shown in Figure 2(a)–(d). In March and April 2020, the number of reply tweets
by all outlets was considerably higher than the overall monthly average, after which there was a
gradual decrease (in line with a decline in the number of news tweets). This may indicate thatinterest in covid-19 related topics may be narrowed down to a specific people. Interestingly, thepeak for monthly average of the reply tweets was not seen in March or April 2020 for all news
outlets; it occurred in April 2020 and August 2021 for Yahoo! News, November 2021 for The
Wall Street Journal, April 2020 and May 2022 for The Guardian, and April and May 2021 for
The Times of India. While COVID-19 was an unknown virus in early 2020, since 2021 awareness and understanding have increased, and countermeasures have been introduced in many
countries. Overall, the results suggest that public interest in the pandemic changed between theearly part of 2020 and the post-2021 period.

4.2 Classification Results and Discussion
Figures 3(a)–(d) show the monthly average ratio of emotional reply tweets for the four news
outlets. As shown in Figure 3(a), gear and anticipation were commonly expressed in reply tweets
to posts by Yahoo! News. Here, the precision exceeded 60% for both fear and anticipation, while
the recall was 33.3% and 77.9%, respectively, as shown in Table 2. On the basis of these data,
there may have been more fear tweets than suggested in Figure 3(a), particularly in response toposts by Yahoo! News. As shown in Figures 3(b)–(d), anger and disgust were commonly expressed in reply tweets to posts by The Wall Street Journal, The Guardian, and The Times of India.
The proportion of joy reply tweets was higher for posts by The Times of India compared withthose by the other media outlets. Here, comparing the performance of joy, anger, and disgust in
Table 2, each showed the recall and precision of over 70%, suggesting that there is a certain
degree of reliability in the transition of these three emotions in Figures 3 (b) to (d).

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S. Fukuda, H. Nanba, H. Shoji(a) Yahoo! News.

(b) The Wall Street Journal.

(c) The Guardian.

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Analysis of International Interests and Emotional Responses to the Coronavirus Pandemic(d) The Times of India.
Figure 2: Numbers of reply tweets to news tweets per month and monthly average number of
reply tweets by news outlet.

(a) Yahoo!News.

(b) The Wall Street Journal.

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S. Fukuda, H. Nanba, H. Shoji(c) The Guardian.

(d) The Times of India.
Figure 3: Relative proportions of reply tweets in the various emotional categories.

Interestingly, across all media, the proportion of fear tweets was significantly higher in January
and February 2020 than in all other months. We investigated the associated events by examining
the news tweets in those months. Table 3 shows the top news tweets with the highest number offear reply tweets for each news outlet. From this table, it was common to see the emergence ofnews stories that were closely watching moves in China against COVID-19 or were concerned
about the impact of COVID-19 on their own country. Also, the COVID-19 outbreak onboard the
Diamond Prince cruise ship, which was covered by Yahoo! News and The Wall Street Journal,
was of interest to many people, as it could shed light on how the novel virus affects in an enclosed
environment. Of course, although they do not appear in Table 3, the Guardian and the Times ofIndia have also published news on this topic. However, the number of replies in January andFebruary 2020 is significantly lower than in the following months as shown in Figure 2, suggesting that some people who were interested in events before COVID-19 became a common topic
around the world had a heightened sense of fear and concern about events related to COVID-19.
We here focus on the news that "toilet paper will be in short supply" appeared in news tweets
in Yahoo! News. In Japan, a nationwide buying up of toilet paper and other paper products occurred from the end of February to April 2020, became a major social problem. The origin of thisis believed to be a post on a social networking site that can be translated as follows: "Most toilet

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Analysis of International Interests and Emotional Responses to the Coronavirus Pandemic

paper is manufactured and exported in China, and will be in short supply due to COVID-19".
There was a surge of posts claiming that this was a hoax; the news media then picked up the story
and people became aware that the rumor was false. However, many people considered it possiblethat others would be fooled by the hoax and thus purchase more toilet paper, leading to a shortage;
as a result, they themselves bought more of the product, leading to a genuine shortage. From this,such hoax-based anxiety and fear may have also led to fear in covid-19 pandemic in Japan.
Figure 3 shows that negative emotions as fear, anger, and disgust reply tweets were dominant
in response to posts by all of the news outlets. However, tweets expressing anticipation, i.e., a
positive emotion, were dominant over fear tweets in October and November 2011 as shown in
Figure 3(a). Table 4 shows the five news tweets with the most anticipation reply tweets; news
appeared regarding economic support from the government, including direct cash transfers toindividuals and the Go To Travel campaign to support Japan’s tourism sector. A news also appeared that dealt with a different topic than the above: "Japanese Society of Infectious Diseases
is calling for active vaccination with the influenza vaccine". An examination of reply tweets tothis news revealed that some users posted tweets about continuing to wear masks and gargle andwash their hands. These can be seen as positive statement that we will continue to use establishedpractices and knowledge under the covid-19 pandemic against this news. On the other hand,sever-al tweets also found that said the news was announced by an organization making moneyfrom vaccines. Such opinions could be viewed as a (non-essential) anticipation of the push for
vaccination with a cynical response to this news. Thus, our approach can be useful in capturing
roughly the news that results from a particular emotional upsurge and people’s reactions to it.

5Conclusion

We analyzed tweets made in reply to news tweets posted by major news outlets pertaining to
COVID-19 to investigate the changes in interest in, and emotional responses to, COVID-19 of
people around the world were investigated. Our analysis results suggested that fear was commonly expressed in reply tweets to Yahoo! News posts, while anger and disgust were commonly
expressed in replies to tweets by The Wall Street Journal, The Guardian, and The Times of India(from January 2020, when the pandemic began, to May 2022). In addition, the numbers of fear
reply tweets were significantly higher in January and February 2020 than in all other months, for
all news outlets, while Anticipation tweets were dominant in October and November 2021 inJapan. We also analyzed the factors that contributed to the rise of a particular emotion by tracing
the news to which tweets with the emotion replied. Our approach was useful for tracking theemotional reactions of people over the course of the COVID-19 pandemic. In future, we will aim
to devise additional emotional categories for tweets using analysis techniques such as cluster
analysis.

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S. Fukuda, H. Nanba, H. Shoji

Table 3: Example news tweets provoking fear reply tweets in the period January–February
2020
Account

Date

Yahoo!
News

2020/
02/16

The
Wall
StreetJournalThe
GuardianThe
Times ofIndia

Number of
Replies
229

News Tweet

18

A male paramedic in his 30s from Yokohama Fire Department was infectedwith a new type of coronavirus, a Kanagawa Prefecture official said. The
crew member was wearing a high-grade mask, goggles, and other personal
protection equipment.
Phrases such as "toilet paper scarcity" have become prominent on social
networking sites, but the notion that toilet paper is in short supply due tothe novel coronavirus is incorrect. Only about 2.3% of Japan’s toilet paper
is imported from China.
Many Ministry of Health, Labour and Welfare of Japan (MHLW) employees working on the cruise ship where an outbreak of the novel coronavirus
occurred have returned to work without being tested for the virus. The
MHLW has decided not to allow these employees to undergo testing at thistime.
With the spread of the novel coronavirus, Chinese social networking sites
drew attention to marathons held over the weekend in various parts of Japan. On the basis of the experience of Wuhan, some people believe thatJapan may encounter difficulties if this situation continues, or be worse off
than Wuhan.
A person who recovered from infection with the novel coronavirus in
China has been reinfected. The patient, who had been hospitalized in Sichuan Province with a novel coronavirus infection, was discharged fromthe hospital and, after 10 days of home isolation, was confirmed to havebeen reinfected.
The State Department will evacuate US citizens and their families onboard
the Diamond Princess cruise ship quarantined for coronavirus, a Centers
for Disease Control and Prevention official has said.
Public ire over Hong Kong’s relative inaction in response to the coronavirus outbreak has grown as neighboring Macau, another self-governed
Chinese territory, stepped up preventive efforts.
A doctor in Wuhan reprimanded by the police for his early warnings about
the dangers of the coronavirus has died, triggering an extraordinary outpouring of emotion in China. #WSJWhatsNo
A man and a woman, both in their 80s, onboard the Diamond Princess
cruise ship have died.
Heard on the Street: What will happen to the Sino-American trade deal as
the coronavirus outbreak spreads worldwide?
Coronavirus: Hong Kong health workers to strike in China border row as
deaths pass 300
Is the coronavirus panic turning the UK into a hostile environment for East
Asians? #coronavirusuk
Coronavirus shakes citizens’ faith in Chinese government.

16

Chinese state media downplays coronavirus as Xi strikes positive tone.

16

Tell us how coronavirus is affecting your day to day life.

5
3

Air India Boeing 747 on standby to evacuate Indians from Wuhan amid
coronavirus outbreak.
Coronavirus: Seven people who travelled to China under observation.

3

Iran confirms 13 more coronavirus cases, two deaths - Health ministry.

3

Economy affected with coronavirus, but govt giving medication for common cold: Manish Tewari.
#CoronavirusOutbreak: 324 Indians evacuated from China’s Wuhan in
special Air India plane.

2020/
02/27

211

2020/
02/21

199

2020/
02/19

197

2020/
02/21

192

2020/
02/15

38

2020/
01/31

32

2020/
02/07

31

2020/
02/20
2020/
02/07
2020/
02/02
2020/
01/31
2020/
01/24
2020/
01/24
2020/
02/27
2020/
01/27
2020/
01/25
2020/
02/21
2020/
02/06
2020/
02/01

22
21
59
22

3

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Analysis of International Interests and Emotional Responses to the Coronavirus Pandemic

Table 4: Example news tweets by Yahoo! News provoking anticipation reply tweets in the
period October–November 2021
Date
2021/
11/04

Numberof Replies
684

2020/
10/11

436

2021/
10/04

388

2021/
10/07

185

2020/
10/05

137

News Tweet
The government and ruling party have decided to provide 100,000 yen in cash to children and young people (aged < 18 years) as a one-time support measure in response to
the spread of the novel coronavirus. No income limit will be set. The plan will be included in a major economic stimulus package to be finalized by the government on
March 19.
Prime Minister Kishida stated on a TV Tokyo program (November 11) that he would
like to implement economic measures in response to the novel coronavirus, and that hewas "not opposed" to Komeito’s proposal to provide a fixed amount of 100,000 yen tochildren (aged < 18 years), adding that he would discuss the details with the ruling party
and decide on the specifics.
Prime Minister Kishida was asked if he would provide cash transfers to individuals as ameasure against the new coronavirus strain, and he stated that he would consider cashtransfers for women, non-regular workers, and students on a case-by-case basis. He indicated that he would decide on the amount after consulting with the ruling party.
The Japanese Society of Infectious Diseases is calling for active vaccination against influenza. Last season, the number of influenza cases plummeted as a result of countermeasures taken in the wake of the coronavirus pandemic. If restrictions on overseas
travel are eased and the virus is brought into Japan, there is a risk of a pandemic thisseason.
At a press conference held on May 5, the Minister of Land, Infrastructure, Transport,
and Tourism, Tetsuo Saito, stated that he will consider the status of the novel coronavirus
before resuming the Go To Travel tourism support project. He stated that the project is"essential for the recovery of tourist areas and local economies”.

Acknowledgments
This work was supported by the Chuo University Research Cluster Formation Support Programin FY2021 and JSPS KAKENHI Grant Number JP22K18152.

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