Changes in Interests and Emotional Responses toNews Coverage of Coronavirus Disease 2019 Case
Numbers Over Time
Satoshi Fukuda
Faculty of Science and Engineering
Chuo University
Tokyo, Japan
fukuda.satoshi.3238@kc.chuo-u.ac.jp

Hidetsugu Nanba
Faculty of Science and Engineering
Chuo University
Tokyo, Japannanba@kc.chuo-u.ac.jp

Abstract—Understanding how people’s interest andemotional state change in response to news coverage of aparticular topic and elucidating the characteristics of thesechanges can reveal the shifting nature of attention and emotion.
We analyzed people’s interest and emotional responses
expressed via Twitter in response to news coverage ofannouncements of new cases of coronavirus disease 2019
(COVID-19) as a case study. As a measure of interest, we
examined replies to tweets of news items posted by media outlets
on Twitter, and classified the emotional content of each reply
tweet using Plutchik’s wheel of emotion. The analysis suggested
that people were most interested in COVID-19 case numbers in
April 2020, when the first wave of cases occurred and the firstemergency declaration was issued, and in July 2020, when thesecond wave of cases emerged. The results revealed that fear was
the most commonly expressed emotion. The ratio of fear-related
tweets was highest in February and March 2020, a time at which
new COVID-19 cases were confirmed in various locations andthere was substantial public discussion regarding whether
Japan would declare a state of emergency for the first time.
Keywords—Twitter, Sentiment Analysis, COVID-19

I. INTRODUCTION
For unknown events that are of interest to a larger people,
media may report every time there is some progress and event
to be covered. For example, the coronavirus disease 2019
(COVID-19) outbreak that emerged in December 2019 was anovel event with global consequences, and various media
outlets reported a range of related news items on a daily basis.
In addition to television and newspapers, in recent years, news
has been increasingly distributed through news websites andsocial networking services. Unfamiliar negative events oftencause widespread anxiety. Ongoing news coverage can leadpeople to develop a deeper understanding and awareness of anevent. As understanding and awareness of an event increases,
some people may feel relieved. However, awareness of new
problems related to the event or ongoing problems that persist
may cause more interest, and some individuals may continue
to feel anxious. We have an interest for understanding how
people’ interest and emotional responses change over time inresponse to news reports of specific events, and elucidating
the characteristics of these changes. This information might
be useful for supporting people who feel anxious, leadingthem to develop a sense of emotional security.
To investigate this issue, we examined the reporting of
new positive cases of COVID-19 as a case study to analyzechanges in people’s interest and emotional responses to news
coverage. In Japan, since January 2020, the Ministry of Health,Labour and Welfare has announced the number of new
COVID-19 cases in each prefecture daily. Following the

XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE

Hiroko Shoji
Faculty of Science and Engineering
Chuo University
Tokyo, Japan
hiroko@kc.chuo-u.ac.jp

announcement, the Japanese media has reported the numberof new positive cases as a news item, through television,
newspapers, news sites and social networking services. Fig. 1
shows an example of a tweet posted on Twitter by theYahoo!News account (username: @YahooNewsTopics).
Some tweets of news headlines report the number of cases inTokyo, and some provide additional information, such as theincrease or decrease in the number of new cases comparedwith the previous case number. Additionally, users may tweet
comments and opinions about the news in the form of replies
to news tweets. In the current study, we examined news tweets
posted by Yahoo!News on Twitter and users’ reply tweets toanalyze changes in people’s interest and emotional responses
regarding announcements of the number of new cases of
COVID-19.
II. RELATED WORKOver the course of the COVID-19 pandemic, a range of
events have occurred, including the confirmation of COVID19 in various locations, the discovery of COVID-19 variants,
and the implementation of government policies such as
vaccination, promotion of mask use, and lockdowns.
Furthermore, COVID-19 has had a significant impact on
education and the economy, and has been a common topic ofconversation in daily life and in the media. In response, inrecent years, many studies have identified various topics
addressed during the COVID-19 pandemic, and haveanalyzed how people and media feel about each topic, andhow their feelings have changed over time.
De et al. [1] conducted sentiment analysis on news articles
and tweets to identify changes in sentiment and topics
addressed over time in their analysis of the impact of COVID19 in Brazil. Ghasiya et al. [2] analyzed news articles on
COVID-19 in four countries (United Kingdom, India, Japan,
and South Korea) using top2vec [3] and RoBERTa [4] todetermine which topics were covered and how sentiment oneach topic differed across countries. Additionally, a numberof sentiment analysis studies have focused on specific themes
and topics during the COVID-19 pandemic. Bhagat et al. [5]applied sentiment analysis to blog and newspaper articles
using TextBlob [6] with the goal of identifying public opinion
toward online learning during the COVID-19 pandemic.
Wang et al. [7] analyzed controversial topics related to mask
wearing and vaccination using latent Dirichlet allocation [8]in the United States. Specifically, the researchers analyzed
how interest in the topic changed using the sentiment score
calculated by TextBlob and inferred the factors that caused thesentiment score to fluctuate by comparing the changes to theactual events related to COVID-19. Hu et al. [9] analyzed how

Fig. 1. Example of a tweet regarding news of new cases of COVID-19 byYahoo!News on Twitter

We used Yahoo! News (username: @YahooNewsTopics)
as the news site account under examination. The collection
period was from February 1, 2020, when the number of
positive COVID-19 cases began to be announced, to May 31,
2022. From this dataset, news tweets regarding theannouncement of the number of new positive COVID-19
cases and tweets that were replies to those tweets wereextracted, and emotional analysis and word extraction were
performed on each reply tweet. As described in Section I,
news tweets regarding the announcement of the number of
new cases may include information regarding the increase or
decrease in new cases compared with previously reported casenumbers. We therefore compared how people’s interest andemotional responses changed in response to such statements
in news tweets. To achieve this, the following query was used
to identify tweets posted by Yahoo!News. Note that not allnews tweets centered on the announcement of new COVID19 cases were covered, and other news tweets may have been
included.
Query 1: コロナ AND 東京都 AND 感染 AND 新た AND [0-9]+人

sentiments and opinions about vaccines changed in the UnitedStates by splitting the time frame by two events regarding thevaccine that occurred between March 1, 2020 and February
28, 2021: clinical trials of the Moderna vaccine, and theadministration of the first dose of the COVID-19 vaccine in
the United States. Yousefinaghani et al. [10] used VADER[11] to make positive or negative classifications of a set oftweets regarding the COVID-19 vaccine in various countries,
including the United States, United Kingdom, and Canada,
and extracted keywords that appeared in each sentiment. They
then defined the categories “anti-vaccine,” “hesitant,” and“pro-vaccine” and the keywords associated with eachcategory, and analyzed the volume of tweets for each category
in each country. In the current study, we focused on news
coverage of announcements of the number of new cases on
Twitter, which were regularly reported during the COVID-19
pandemic, to determine how interest in and feelings about the
topic changed over time. By examining reply tweets to news
reports, we focused directly on interest in and feelings about
this topic.
III. METHODOLOGYOur analytical approach comprised two main components:
1) collecting tweets posted by news accounts on Twitter andreplies to those tweets; and 2) sentiment analysis and word
extraction for each reply tweet. Each of these steps is
described in detail below.
A. Tweet Data Collection
The corpus of the tweet set was constructed as follows.
First, tweets posted by a particular news account werecollected via the Twitter application programming interface.
At the same time, tweets that were replies to the tweets posted
by the account were collected. Specifically, we used theaccount name of a news site beginning with @ as a query andcollected tweets that contained it. We named the former set oftweets the news tweet set, and the latter the reply set. Then,
using the tweet ID information that shows the reply-to tweet
included in each tweet in the reply set, each tweet in the reply
set was linked to the corresponding tweet in the news tweet
set.

1

https://github.com/ids-cv/wrime(In English: corona AND Tokyo AND infected AND new AND[0-9]+ person)

Query 2: コロナ AND 東京都 AND 感染 AND 新た AND [0-9]+人AND (下回 OR 減っ OR 減少)(In English: corona AND Tokyo AND infected AND new AND[0-9]+persons AND (down OR decrease OR decline))

Query 3: コロナ AND 東京都 AND 感染 AND 新た AND [0-9]+人AND (上回 OR 増え OR 増加)(In English: corona AND Tokyo AND infected AND new AND[0-9]+person AND (up OR increase OR larger))

B. Sentiment Analysis and Lexical Extraction
As a dataset for classification, we used the Japaneseemotion analysis dataset1 compiled by Kajiwara et al. [12].
This dataset contains 43,200 texts posted on social networking
services labeled with Plutchik’s eight emotions [13] on fourlevels of emotional intensity (none, weak, medium, andstrong). Additionally, the labels included five types: one
applied by the writer of the text, three applied by three readersof the text, and one that was constructed by averaging theemotional intensities assigned by the three readers. We used
40,000 data for training, 1,200 data for validation, and 2,000
data for evaluation. Labels representing the average emotional
intensity identified by the three readers were used, with weak,medium, and strong emotional intensity being positive, andnone being negative.
As a classification model, we used BERT [14]. Parameters
were set to 32 for batch size, 5 for epochs, 1e-5 for learning
rate, and 128 for the number of tokens. Adam [15] was used
for optimization. As a learning model, we used bert-basedjapanese-whole-word-masking2.
Table I shows the classification performance for eachemotion. These classifiers were used to classify the reply
tweets. Note that each reply tweet could be classified into
multiple emotions because of the binary classification of eachemotion.
Next, each news tweet was divided by month and thenumber of replies was counted by month. Then, the numberof tweets from which the word was extracted and the numberof users who posted those tweets for each emotion-category2

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

TABLE I.
EMOTIONJOY
SADNESS

CLASSIFICATION PERFORMANCEPRECISION

RECALLANTICIPATIONSURPRISEANGERFEARDISGUST
TRUST

0.799
0.409
0.745
0.519
0.229
0.203
0.042
0.000

0.710
0.702
0.803
0.577
0.423
0.742
0.923
0.000
Fig. 2. Number of tweets collected by Queries 1–3 in the news site set

were counted by month. By comparing the number of tweets
from which a word was extracted with the number of users
who posted the corresponding tweets, it was possible to verify
whether the word had generality. For example, if the numberof tweets and the number of users were almost equal, theextracted word was considered to have generality, and if thenumber of users was very small, the word was assumed tohave been generated by a specific person or group.
IV. RESULTS AND DISCUSSION
A. Distribution of News Tweets
Fig. 2 shows the transitions of the number of tweets
collected by Queries 1, 2, and 3 in the news site set and thenumber of new COVID-19 cases in Tokyo in the open data set
available on the Ministry of Health, Labour and Welfare
website3 . From Query 1 (as shown in Fig. 2), news tweets
about the announcement of the number of new cases peaked
in July 2020, with 49 tweets posted. In that month, the numberof new COVID-19 cases increased for the second time, andthis period was known as the second wave in Japan. The news
tweets included news items that only announced the numberof new cases, as well as news items that included the numberof new cases accompanied by a call to action by thegovernment and Tokyo Metropolitan Government for the
public to limit their behavior and their views regarding
COVID-19. In May 2020, September 2020, and December
2021, the number of news tweets identified using Query 1
showed a decrease. These periods roughly coincided withperiods in which there were decreases in the number of newcases.
A comparison of Query 1 and Query 3 revealed that thenews tweet trends were similar until February 2022, whereas
the news tweet trends identified using Query 2 and Query 3
appeared to conflict. A comparison of the trends in Query 2
and 3 and the number of new cases revealed that the numberof news tweets matching Query 2 increased during the period
in which the number of new cases was decreasing, and thenumber of news tweets matching Query 3 increased during theperiod in which the number of new cases was increasing.
However, in the period after February 2022, the results
indicated that the transition between Query 1 and Query 2 wassimilar than the transition between Query 1 and Query 3.
These results suggest that until February 2022, there weremore news reports about the increase or the highest number of
new cases of COVID-19 compared with the past, and after
February 2022, reports about the decrease in the number of
new cases became dominant.

3

https://www.mhlw.go.jp/stf/covid-19/open-data.html

B. Distribution of Reply Tweets
Fig. 3 shows the number of tweets replying to news tweets
in each month collected by each query, and the number of new
COVID-19 cases in Tokyo. The transition of Query 1 in Fig.
3 shows that the number of replies peaked in April 2020 andJuly 2020. Japan had its first and second waves of COVID-19
infections in April and July 2020, respectively. Table II showssome of the nouns (phrases) extracted from reply tweets in
April and July 2020 collected by Query 1. The numbers to theleft and right in parentheses indicate the number of tweets andusers from which the word or phrase was extracted,
respectively. In Table II, the term "emergency declaration"
appeared in April 2020. In Japan, the government issued
emergency declarations in the period from April to May 2020.
This suggests that the increase in reply tweets in April 2020
was caused by a lack of information about COVID-19 and thefact that the public had not experienced a declaration of a stateof emergency before, and, therefore, were highly interested in
the event. Additionally, the term “emergency declaration”
also appeared in July 2020. In that month, no emergency
declarations were issued in Japan, but the number of new cases
had begun to increase. These results suggest that the increase
in reply tweets in July 2020 may have been primarily causedby discussions among the public about declaring a state of
emergency in response to the increase in the number of newcases. Incidentally, the word "Phone" in April 2020 in Table
II appeared in tweets that replied to a tweet containing thenews item entitled "Of the 118 people newly confirmed
infected by the Tokyo Metropolitan Government on April 4,nearly 70% have no known route of infection. It is particularly
difficult to identify the route of infection in the younger age
groups, and the public health center said that they do not
answer our phone calls for interviews." ("東京都が 4 日新たに感染確認した 118 人のうち、7 割近くが感染経路不明。特に感染経路の特定が難しいのが若年層で、保健所からは「聞き取り調査のための電話に出てくれない」との声。 ) Many of

the reply tweets expressed opinions and impressions of theinterview methods for younger people. Although this news
title contained information about new cases, the main topic
was the identification of the route of infection among young
people. In future, additional approaches should be considered
for selecting only news tweets that primarily announced thenumber of new cases.

TABLE II.

EXAMPLE OF WORDS EXTRACTED FROM REPLY TWEETS
COLLECTED USING QUERY 1

2020-04
Num ofTweetsinfect
341
phone
299
emergency
245
declaration
self197
restraint
Word

Num of
Users
312
297
223
188

2020-07
Num ofTweetsinfect280
goverment
209
emergency
202
declaration
counter174
measureWord

Num of
Users
251
179
170
158

Fig. 4. Ratios of tweets with each emotion label replying to the tweets in
Query 1

Fig. 3. Number of tweets replying to the tweets in the reply set

Subsequently, increases in the number of replies were
found in December 2020, July 2021, and January 2022. These
periods correspond to the months before peak in third, fifth,
and sixth wave for number of new positive cases, respectively(i.e., the timing of the increase in the number of new cases).
Additionally, as in Fig. 2, the transitions for Query 1 andQuery 3 were similar, while those for Query 1 and Query 3
appeared to conflict. These results indicate that the media andpublic interest in the announcement of the number of new
COVID-19 cases was related to the increase in the number of
new positive cases.
C. Sentiment Analysis Results for Reply Tweets
Fig. 4, Fig. 5, and Fig. 6 show the ratio of tweets with eachemotional label relative to the number of tweets in each monthfor each reply tweet in Queries 1, 2, and 3. In Fig. 4 throughto Fig. 6, "Fear," "Surprise," and "Anticipation" were the main
emotions expressed. In particular, the comparison ofclassification results shown in Table I revealed that "Fear" wasthe main emotion expressed, despite its lower classification
accuracy compared with "Anticipation" and "Surprise". These
results indicate that "Fear" was a commonly expressed
emotional response to reports of the number of new positives.
Specifically examining the transition of "Fear," Query 1
indicated that the ratio of "Fear" tweets was very high in
February and March 2020. In addition, in November 2020 andJanuary 2022, the ratio of "Fear" tweets increased comparedwith the previous 2 months, indicating a peak in the ratio of
tweets. The same tendency was observed for "Fear" in Figure
6 (Query 3). Table III shows some of the words extracted fromthe "Fear" reply tweets in February 2020, March 2020,
November 2020, and January 2022 in Query 1, and Table IVshows some of the words extracted from the "Fear" reply
tweets in March 2020, November 2020, and January 2022 inQuery 3. Table III shows that in February 2020, the words"facility," "elderly," and "nursing home" appeared. In March
2020, the terms "hospital," "hospital infection," andFig. 5. Ratios of tweets with each emotion label replying to the tweets in
Query 2

Fig. 6. Ratios of tweets with each emotion label replying to the tweets in
Query 3

"medical collapse" were identified, as shown in Tables III and
IV. In February and March, COVID-19 cases were identified
in nursing homes and hospitals, respectively, and these cases
made headlines in Japan. Furthermore, in March 2020, as
shown in Table 4, the term "emergency declaration" appeared.
In Japan, the first emergency declaration was issued by thegovernment in April 2020. However, the results indicate thatthe topic had been talked about since the previous month,when the number of new cases increased. These findings
indicate that the reason for the appearance of expressions of"Fear" among the people was related to the increase in thenumber of new COVID-19 cases and their discovery invarious locations, as well as the emergency declaration, which
was an event that had not previously been experienced inJapan. The results shown in Tables III and IV also indicatedthat the term "emergency declaration" appeared in both

TABLE III.

Word
infectfacilityelderly
nursing
home
Wordinfectseriously
illness
emergency
declaration
government

TABLE IV.

EXAMPLES OF WORDS EXTRACTED FROM REPLY TWEETS
EXPRESSING "FEAR" COLLECTED USING QUERY 1
2020-02
Num ofTweets25
13
12

Num of
Users
24
13
12

8

8

2020-11
Num ofTweets40
18

Num of
Users
35
16

15

15

11

11

infecthospital
medicalcollapse
hospital
infection
Word
infectcold(illness)
emergency
declaration
omicron2020-03
Num ofTweets55
20
16

Num of
Users
54
20
14

15

15

2022-01
Num ofTweets103
79

Num of
Users
96
73

56

49

45

38

2020-03
Num of Tweets

Num of
Users
26

11

11

9

9

8

6

Word

Num of Users
30
29
12
12
11
11
10
10
2022-01
Num of Num ofTweets
Users
66
62

cold
(illness)
seriously
illness
emergency
declaration
omicron

54

46

40

36

33

31

November 2020 and January 2022. In these months, thenumber of new positive cases began to increase, but noemergency declarations were issued in Japan at the time. Thisfinding suggests that each time the number of new cases
increased, emergency declarations became a topic ofconversation, possibly leading to expressions of "Fear".
The ratio of tweets for each emotion in October andNovember 2021 in Query 1 (as shown in Fig. 4) revealed that
"Anticipation" exhibited the highest value. The results
revealed similar patterns for Query 2 (as shown in Fig. 5) andQuery 3 (as shown in Fig. 6). Examples of words extracted
from "Anticipation" reply tweets collected by Queries 1, 2,
and 3 in October and November 2021 are shown in Tables V–
VII, respectively. The words “vaccine” and “vaccination”
appear in each of these tables. The Japanese government haspromoted vaccination of its citizens since April 2021, andimplemented policies such as distribution of vaccination
coupons and development of a COVID-19 vaccination
appointment system. Vaccination is one of the policies ofgreat interest to Japanese people in the fight against COVID-

4https://www.kantei.go.jp/jp/content/vaccination_data5.pdf

EXAMPLES OF WORDS EXTRACTED FROM REPLY TWEETS
EXPRESSING "ANTICIPATION" COLLECTED USING QUERY 1

2021-10
Num ofTweetsvaccine15
vaccination
15
Word

infection
6 wave13
12

Num of
Users
15
8
11
9

2021-11
Num ofTweetsvaccine9
spread of5
infection
6 wave
5
mask
5
Word

Num of
Users
7
5
5
5

TABLE VI.

EXAMPLES OF WORDS EXTRACTED FROM REPLY TWEETS
EXPRESSING "FEAR" COLLECTED USING QUERY 3

Word
infectself-restraint
emergency declaration
hospital
2020-11
Word
Num ofTweetsinfect30
emergency
declaration
countermeasure
mask

Word

TABLE V.

EXAMPLES OF WORDS EXTRACTED FROM REPLY TWEETS
EXPRESSING "ANTICIPATION" COLLECTED USING QUERY 2

2021-10
Num ofTweetsvaccination
11
election9
Word

Infectvaccine98

Num of
Users
6
9
8
8

2021-11
Num ofTweetsvaccine6
spread of5
infectionmask
3
6 wave2
Word

Num of
Users
5
5
3
2

TABLE VII.

EXAMPLES OF WORDS EXTRACTED FROM REPLY TWEETS
EXPRESSING "ANTICIPATION" COLLECTED USING QUERY 3

2021-10
Num ofTweetsemergency
2
decralation
death
2
treat
2
infect2
Word

Num of
Users
2
2
2
2

Word
6 wavevaccinestay homeincrease

2021-11
Num ofTweets4

Num of
Users
4

3
2
2

3
2
2

19, and the total number of vaccinations exceeded 100 million
by August 20214. In addition, in October and November 2021,
the number of new cases decreased. The results describedabove suggest that the increase in expressions of anticipation
during this period could potentially be attributed to the greaternumber of people becoming hopeful about the effectivenessof the vaccine for decreasing the number of new cases.
V. CONCLUSION
We analyzed changes in interest in and feelings aboutnews items on Twitter using the topic of new cases of COVID19 as a case study. Replies to news tweets were used toinvestigate this topic. The results revealed that people in Japan
exhibited the highest level of interest in April 2020, when thefirst wave of new cases occurred in the country and the firstemergency declaration was issued, and in July 2020, when thesecond wave of new cases emerged. We also conducted
emotion classification of the tweets on the basis of Plutchik’s
wheel of emotion and found that "Fear" was the mostcommonly expressed emotion. In particular, the ratio of tweets
expressing fear was very high in February and March 2020,
possibly because of the occurrence of the first increase in thenumber of COVID-19 cases in various locations, and interestin the first emergency declaration. Additionally, the highest
ratios of tweets expressing a feeling of anticipation occurredin October and November 2021, likely because of interest in
the effectiveness of vaccination and the decrease in thenumber of new positive cases.

In future, we plan to analyze changes in interest in andfeelings about news topics other than COVID-19, such as
climate change and automated driving technology. News
regarding the number of new COVID-19 cases is often
described in factual terms, and the sentiment of the news
media is less likely to be expressed. In contrast, for topics suchas emergency declarations and vaccinations, the media often
express their opinions and arguments through editorials. Thus,
we also plan to analyze and compare changes in interest andsentiment between the media and the public.
ACKNOWLEDGMENTS
This work was supported by the Chuo University Research
Cluster Formation Support Program in FY2021 and JSPS
KAKENHI Grant Number JP22K18152.
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