Relationship between university students’ emotional expression on tweets and subjective well-being: Considering the effects of their self-presentation and online communication skills

概要

BMC Public Health

(2023) 23:594
Ye et al. BMC Public Health
https://doi.org/10.1186/s12889-023-15485-2

Open Access

RESEARCH

Relationship between university students’
emotional expression on tweets and subjective
well‑being: Considering the effects of their
self‑presentation and online communication
skills
Shaoyu Ye1*   , Kevin K. W. Ho2   , Kei  Wakabayashi1 and Yuuki Kato3 

Abstract 
This study investigated how personal characteristics such as generalized trust, self-consciousness and friendship, and
desire for self-presentation are related to the subjective well-being of university students who use Twitter in Japan,
including the effects of their online communication skills. We conducted a survey in May 2021 with Twitter users
and analyzed their log data between January 2019 and June 2021. The log data of 501 Twitter users, including the
number of public tweets, retweets, and emotional expressions among different patterns of social media (e.g., Twitter
only, Twitter + Instagram, Twitter + LINE + Instagram, etc.) and academic standings, were analyzed using ANOVA and
stepwise regression analyses. The results showed that the number of tweets and retweets, with and without photos/
videos, increased in 2020 and 2021 compared to 2019, and the ratio of positive sentences remained almost the same
for the two-and-a-half-year period of this study. However, the proportion of negative sentences increased slightly. It is
clear that the factors which affected the university students’ subjective well-being differed depending on the respective patterns of social media use.
Keywords  COVID-19, Emotional expression, Subjective well-being, Twitter
Introduction
Since January 2020 and the beginning of the COVID19 pandemic, many people have experienced changes
in their lives. Since then, many cities and countries
have experienced lockdowns, and people were asked to
*Correspondence:
Shaoyu Ye
shaoyu@slis.tsukuba.ac.jp
1
Faculty of Library, Information and Media Science, University of Tsukuba,
Ibaraki 305‑8850, Japan
2
Graduate School of Business Sciences, University of Tsukuba,
Tokyo 112‑0012, Japan
3
Faculty of Arts and Sciences, Sagami Women’s University,
Kanagawa 252‑0383, Japan

physically distance themselves from each other during
the quarantine and lockdown arrangements. One of the
most common ways to connect with family members and
friends was through the Internet, mainly via social media,
to make phone calls, send text messages, and share posts
with others. However, connecting with friends and relatives through social media could not fully compensate for
the loss of face-to-face interaction. Previous research has
indicated that people’s well-being has been affected significantly, and how they connect has changed [1].
To understand the impact of the pandemic, we examined the changes in people’s emotions through their
behavior on social media. Previous research has investigated how emotional expression and posting motivation

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Ye et al. BMC Public Health

(2023) 23:594

on Twitter relate to a person’s social network structure.
Specifically, Kitamura et  al. [2] examined 1,472 Twitter
users aged 20–39  years, showing a significant negative
relationship between social reward motivation and the
number of negative emotion words, particularly anxiety
emotion words. Additionally, a significant positive relationship between recording motivation and the number
of positive emotion words was observed, and positive
emotion words increased as the clustering coefficient
increased. When the clustering coefficient exceeds the
average value and increases, the coefficient of exchange/
self-sufficiency to predict positive words also increases.
In particular, when the clustering coefficient is low, the
amount of change in exchange/self-sufficiency motivation is small but increases when the clustering coefficient
is high. These findings suggest that emotional expression in Twitter posts is related to the user’s motivation
and might depend on the size of the social network.
The relationship between Twitter usage and personality
traits is also confirmed by Mori and Haruno [3]. They
built machine learning models that predicted the user’s
personality using Twitter usage features and showed that
word statistics information on Twitter is a good estimator of mental health traits. Their report provides strong
evidence for the link between Twitter posts and personal
characteristics. Additionally, Ye et  al. [4] examined the
relationship between university students’ generalized
trust, social skills, number of tweets, types of emotional
expressions and topics, and subjective well-being. The
results indicated that: (i) users with higher levels of generalized trust and social skills had a higher level of subjective well-being and used fewer negative expressions;
(ii) users with a large number of tweets used both positive and negative expressions but they used more negative than positive expression; and (iii) users who used
fewer negative expressions and those who used more
positive expressions had higher levels of subjective wellbeing. However, they only showed correlations among
these factors, whether there are causal relationships or
not still remains unknown. In addition, the implication
from Ye et al. [3] is before COVID-19, it is necessary to
investigate the impact of the pandemic on young generations by examining the influence of the structure of their
social networks on Twitter, such as the number of followers, number of accounts followed, and emotional expression on their posts to explore further these relationships
with subjective well-being and other related factors, since
Twitter is the most popular opened social networking
service (SNS) among young generations in Japan [5]. We
believe investigating these relationships is crucial to help
public health authorities worldwide understand how to
develop policies to improve young generations’ subjective

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well-being during and after the pandemic. In particular,
we focused on the following research question:
RQ: Does a change in emotional expression in tweets
and/or retweets published on Twitter reflect the changes
in users’ subjective well-being before and during the
COVID-19 pandemic?
Literature review and background of research

During the COVID pandemic, social media became the
most convenient tool for communicating with others and
the primary source of information and misinformation
when lockdowns were in place [6]. Gao et  al. [7], using
data collected at the beginning of the pandemic, showed
that social media exposure was positively associated with
anxiety and the combination of depression and anxiety. Other research also reports that people only like to
express their emotions and that there are norms of online
expression of emotion based on the social media selected
[8]. Some researchers have also used text mining to study
how emotional valence is related to COVID-19 misinformation on Twitter and noted that misinformation was
more related to negative valence [9].
Among the most common social media platforms,
Twitter has been extensively investigated as a tool to
probe into people’s emotions [10]. Mori and Haruno
[3] examined the relationship between the information
found on Twitter and the personal characteristics of people who replied to the tweets through machine learning. The results of the study showed that social network
information on Twitter could accurately estimate a user’s
personality. Additionally, linguistic statistical information
and linguistic information about the words used can be
used to estimate a person’s mental health status. In other
words, it is possible to estimate a user’s characteristics
from the number of tweets and the linguistic expression
used for the tweets. In addition, as mentioned earlier, Ye
et  al. [4] observed the relationships between subjective
well-being and emotional expression on Twitter.
Based on the above findings, it could be assumed that
the contents of Twitter posts and emotional expressions
might be related to individual characteristics. However,
most of these findings were obtained before the outbreak of the COVID-19 pandemic. After the start of the
pandemic and the declaration of a state of emergency
in Japan, researchers began to observe the relationship
between the anxiety of young people about COVID19 and their tweets. For example, the Japanese media
reported that negative expressions used on Twitter
increased dramatically [11]. Ye and Ho [1] conducted a
survey right after the lifting of the first state of emergency
in Japan and reported that young people spent more time
on Twitter to gain some emotional support because they

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(2023) 23:594

tried to avoid face-to-face contact during the first state
of emergency. However, compared to 2020, there were
fewer limitations in 2021 and 2022, so they also observed
changes in the relationship between social media use
and subjective well-being from 2021 to 2022. In particular, their subjective well-being was not strongly related
to their social media use [12]. Therefore, in this study,
we analyzed the number of tweets and retweets and the
presence or absence of changes in emotional expression
before and during the COVID-19 pandemic and investigated their relationship.
Compared to face-to-face communication, the
online world is characterized by visual anonymity, a
lack of nonverbal information, and reduced concern
about others’ perceptions of the users themselves,
which encourages self-presentation [13]. Ye [5] conducted a survey with 1,681 university students who
used social media, including LINE, Twitter, Instagram, and Facebook, to explore differences in social
media usage among different platforms. The study
showed that university students who used Twitter
only showed the highest level of self-appeal and topic
avoidance scores and the lowest scores for online
communication skills, received the least social support from others and had the lowest level of subjective
well-being. Because using social media may promote
the diversification of young people’s self-consciousness and friendship [14], the effect is considered more
prominent in the case of social media with high visual
anonymity, such as Twitter.
To investigate the effects of the COVID-19 pandemic
on young people’s online communication behaviors and
subjective well-being, we used Twitter as the major tool
to probe into this issue. Using sentiment analysis, we
examined how different combinations of social media
can affect subjective well-being. Previous studies also
indicate that self-consciousness, and friendship and selfpresentation [15] [16] are significant factors that influence young people’s subjective well-being; therefore, we
also included these two factors. Furthermore, as people
with a higher level of generalized trust tend to use Twitter to connect with strangers [1], we also examined the
effects of generalized trust.
Research method
Research design

From May 10 to 22, 2021, we conducted an online survey with university students in the Kanto region of Japan.
A total of 1,694 students submitted their responses, and
1,681 were analyzed, as 13 were incomplete. However,
only 577 participants had public tweets and/or retweets
between January 2019 and June 2021. Therefore, in this

Page 3 of 12

study, we conducted the analysis of the data obtained
from the 577 participants.
In the survey, their personal characteristics, including generalized trust toward others (α 
= 0.80),
self-consciousness and friendship, desire for selfpresentation and admiration, usage of various social
media, online communication skills (α = 0.80), and
subjective well-being (α = 0.86), were measured. Ye
[5] conducted a factor analysis about self-consciousness and friendship and desire for self-presentation
and admiration and found six subscales for self-consciousness and friendship (i.e., “self-indeterminate factor (α = 0.74)”, “self-establishment factor (α = 0.75)”,
“self-independency factor (α 
= 0.71)”, “self-variable
factor (α = 0.69)”, “dependency factor (α = 0.42)”, and
“self-concealment factor (α = 0.61)”, respectively) and
four subscales for the desire for self-presentation and
admiration (i.e., “rejection avoidance factor (α = 0.84)”,
“praise acquisition factor (α = 0.84)”, “self-appeal factor (α = 0.83)”, and “topic avoidance factor (α = 0.72)”,
respectively). However, because the internal reliabilities for the “dependency factor” and “self-concealment
factor” were lower than 0.65, we conducted the following analysis without these two factors. Additionally, we
obtained participants’ consent to collect their log data
from Twitter for further data analysis.
Emotional expression and topic analysis

We collected and analyzed the log data using the Twitter
API. The number of tweets, number of retweets, number
of tweets with photos/videos, and number of retweets of
tweets with photos/videos were calculated as features.
Terms including “コロナ” ( “corona” in katakana, the Japanese syllabic writing for terms in foreign languages),
“corona,” and “covid” (including capitalized and non-capitalized letters)—hereinafter “COVID-19”—were set as
keywords directly related to COVID-19, and the number
of tweets and retweets including these keywords were
calculated and analyzed.
Sentiment analysis was performed using a neural network model implemented using the flairNLP library
[17]. The neural network uses a word-embedding layer
and a bidirectional long short-term memory (BiLSTM)
layer to convert tweets into feature vectors, and a linear
transformation and Softmax function are used to classify sentences into three categories: positive, negative,
and neutral. For the dataset for learning the parameters
of the neural network, we used 20,000 Japanese tweets
with emotion labels given by crowdsourcing through a
service from Lancers, a Japanese crowdsourcing company. In the holdout verification, which estimated the
prediction accuracy using part of the dataset as test

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Table 1  Demographics (n = 577)
Items

Demographics

Gender

Male 51.6%

Average age

19.6 years (SD 1.31)

Academic standing

Female 47.1%

First-year: 32.2%

Others 1.2%

Second-year: 28.9%

Third-year: 21.7%

Fourth-year or above: 17.1%

Accommodation

Dormitory: 19.4%

Home/Relatives’ home: 20.3%

Apartment 58.9%

Shared: 1.4%

Living style

Alone: 71.8%

With family/relatives: 22.5%

With acquaintances/lovers: 2.4%

Room shared: 3.3%

Internet usage time by computer per day

12 h or more: 3.3%

10–12 h: 2.6%

8–10 h: 5.7%

6–8 h: 13.5%

4–6 h: 25.8%

2–4 h: 27.7%

Internet usage time by smartphones per day

Information about the Twitter account

0–2 h: 18.4%

Not used: 2.9%

12 h or more: 3.1%

10–12 h: 3.5%

8–10 h: 7.8%

6–8 h: 20.6%

4–6 h: 27.4%

2–4 h: 26.3%

0–2 h: 11.1%

Not used: 0.2%

Account Time: 44.8 months (SD 27.23)
Accounts followed: 544.6
Number of accounts: 2.71 (SD 1.76)
Number of followers 481.2

Daily Twitter usage time
Content viewed on Twitter (Note 1)

Content posted on Twitter (Notes 1 and 2)

Twitter post frequency

12 h or more: 0.3%

10–12 h: 0.5%

8–10 h: 1.7%

6–8 h: 3.1%

4–6 h: 6.2%

2–4 h: 23.1%

Killing time: 83.2%

Information about hobbies: 80.6%

News: 49.9%

Conversation with friends: 42.5%

Review confirmation: 22.5%

Dissipating stress: 21.7%

Information about COVID-19: 16.8%

Job hunting: 7.5%

Others: 4.5%

None: 0.5%

Common hobbies: 56.3%

Photos and videos: 42.1%

Reply to friends’ Tweets: 40.9%

Maintaining friendships: 24.6%

Self-deprecating: 22.7%

A fulfilling life: 27.7%

Report-related: 14.7%

Others: 12.0%

Corona-related anxiety: 4.0%

Corona-related information: 3.3%

Job hunting information: 2.9%

Do not post: 24.3%

Almost daily: 42.5%

Several times per week: 15.9%

One time per week: 11.3%

One time per month: 7.8%

0–2 h: 65.0%

Hardly: 22.5%

(1) Items that allow multiple responses
(2) 24.3% of respondents did not have any public posts. Therefore, the maximum percentage of content posted was 75.7%

data, the accuracy rates of the positive, negative, and
neutral classes were 0.70, 0.56, and 0.59, respectively.
The tweets of the analysis targets were divided into
newline characters and regarded as sentences, and
each sentence was input to the neural network to provide an emotion label. However, retweets, tweets without nouns, and tweets containing five or fewer words
were excluded from the analysis. Hashtags, user-mention tags, and URLs were deleted from the text of the
tweets. The percentage of sentences classified as positive or negative was calculated for each analysis target
and used as the analysis target.

Data analysis
Demographics of our participants

Table  1 shows the demographics of the participants
who posted or retweeted. We found that approximately
one-third of the participants were first-year students.
Additionally, more than 70% lived alone, similar to the
findings before the pandemic[5].1 Similar to previous
findings, more time was spent on the Internet through
smartphones than computers, however, this difference
1 

We coded “1” for living alone and “2” for living with others for the living
style.

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was narrowing.2 Additionally, the top three purposes of
using Twitter were related to the collection of information, that is, killing time, sharing hobbies, and browsing news; the top three posted contents were common
hobbies, sharing photos and videos, replying to friends,
etc., which were similar to the content posted before
the pandemic. Meanwhile, 42.5% of the respondents
indicated that they posted on Twitter daily, and 22.5%
reported that they rarely posted. The ratios of both
types of respondents were higher than those before the
pandemic period3 [5].
Emotional expressions in tweets and retweets

The collected data were compared separately in 2019
(before the pandemic), 2020 ­(1st year during the pandemic), and from January to June 2021 (­2nd year during the pandemic). We used data corresponding to the
first half of the year for analysis. We matched this to the
usage pattern of students as the academic year started in
the first half of the year, and university students started
to develop their social networks, especially first-year
students. This arrangement also allowed us to match
our data collection period (i.e., mid to late May) with
the tweets and retweets they posted. At this stage, we
removed data from 8 participants’ data as their tweet/
retweet records were insufficient for us to conduct further analysis. We considered participants to be users of a
particular social media platform if they spent at least 20%
of their social media time on that particular platform.
Of the 15 possible combinations of usage patterns, we
observed nine patterns that met the following requirements: (1) LINE only (n = 5); (2) Twitter only (n = 70); (3)
Instagram only (n = 2); (4) LINE and Twitter (n = 149);
(5) LINE and Instagram (n = 11); (6) Twitter and Instagram (n = 17); (7) LINE, Twitter, and Instagram (n = 282);
(8) LINE, Instagram, and Facebook (n = 1); and (9) all
four social media platforms (n = 32). For further analysis,
we only included Patterns 2 (n = 70), 4 (n = 149), and 7
(n = 282), as they are the only three patterns that account
for more than 10% of the participants. The results are
presented in Table 2. Regardless of the use patterns, the
ratios of positive and negative sentences within these
three periods showed no significant differences in the
number of tweets and retweets, including those with keywords of COVID-19, from 2020 to 2021. The number of

2 

Monthly conversion was performed for the usage time of the Internet by
computers and smartphones as follows: 1–2 h per day is 30, 2–4 h per day is
90, 4–6 h per day is 150, 6–8 h per day is 210, 8–10 h per day is 270, 10–12 h
per day is 330, and 12 h or more per day is 360.

3

 The frequency of post was converted monthly as follows. Rarely is 0,
once a month is 0.5 times, once a week is 10, several times a week is 20, and
almost every day is 30.

Page 5 of 12

tweets, retweets, and tweets and retweets with photos/
videos in the two-and-a-half-year period showed significant differences.
As shown by Ye [5], the posting frequency of Twitter differed depending on the combination of social
media platforms used, including Facebook (required
users to provide a real name), Instagram (linked to Facebook), and LINE (usually used for connecting with close
friends). Therefore, we analyzed the responses based on
the combination of the social media platforms they use.
In this study, we used Twitter, LINE, and Instagram (282
people, Pattern 7, 56.3%) as the most common patterns
of social media usage, followed by Twitter and LINE
(149 people, Pattern 4, 29.7%), and 70 people (Pattern
2, 14.0%) who use Twitter only. We analyzed these three
patterns in detail and summarized the results in Table 2.
From Table  2, it is clear that the number of tweets and
retweets (F = 114.27, p < 0.001 for tweets and F = 36.36,
p < 0.001 for retweets) and the number of tweets and
retweets with photos/videos (F 
= 33.50, p < 0.001 for
tweets and F = 14.02, p < 0.001 for retweets) increased
from 2019 to 2021 in overall results. Therefore, there
was a growing trend in the number of tweets posted and
retweets (including photos/videos) from 2019 to 2021,
reflecting an increase in Twitter usage. With regard to the
number of tweets about COVID-19, there was a slight
increase in the overall result (F = 15.76, p < 0.001). There
were few retweets about COVID-19. Regarding the ratio
of positive and negative sentences on Twitter, we noted
an increase in negative sentences (F = 3.40, p < 0.05) and
a decrease in positive sentences (F = 3.05, p < 0.05) in this
period. Figure 1 shows the ratios of positive and negative
sentences from 2019 to 2021.
We further analyzed the data according to the academic
standing of the participants (based on their academic
standing in 2020–21). As shown in Table 3, we noted an
increasing trend in the number of tweets, retweets, and
tweets and retweets with photos/videos across academic
standings. Except for fourth-year students, the average number of tweets about COVID-19 peaked in 2021.
However, there was no common trend in the ratio of positive and negative sentences (Fig. 2).
Factors affecting subjective well‑being

Ye [5] clarified that the self-consciousness and friendship, self-presentation desire, and online communication skills of university students differed depending
on the usage pattern; therefore, we also analyzed how
they differed depending on the three patterns (Table 4).
There were significant differences in self-establishment
(i.e., Pattern 7 [3.78] was significantly higher than Patterns 2 [3.50] and 4 [3.60], F = 3.49, p < 0.05), rejection avoidance factor (i.e., Patterns 2 [2.94] and 7 [2.99]

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Table 2  Changes in the number of tweets/retweets and emotional expression based on social media usage patterns
Items

January to June
2019

January to June 2020

January to June 2021

ANOVA

Overall

Overall

Overall

Overall

2
Number of tweets

4

7

74.28

2

4

7

170.44

2

4

7

468.90

2019
114.27

75.56 105.42 57.50 177.66 221.94 141.43 1,075.59 510.07 296.55 2.19
Number of retweets

15.74

37.82

20.87 18.81
Number of tweets with photos/videos

11.62

Number of tweets related to COVID-19

9.18

5.34
7.54

4.19

0.00
0.35

Ratio of negative sentences

0.24

0.28
0.23

23.93
10.20

0.00

0.06

20.16

0.00

0.00

9.85

0.41

0.25

0.10

0.24

0.32

35.30

57.27

14.57

0.31

0.11

0.00

0.01

0.03

0.37

0.23

0.00

0.00

0.26

0.32

9.76 ***

2.28

14.78 ***

2.85

9.89 ***

1.08

4.58 **

.78

2.15

***

 − 
 − 
3.05 *

0.26

0.35

8.74 ***

9.68 *** 15.09 ***

3.40 *

0.28
0.26

1.79

1.00

0.31
0.27

34.03 ***

***

1.01
15.76

0.12

2.36

***

1.50
14.02

50.26

2021

***

.82
33.50

77.11

0.01

0.26
0.24

118.07

0.14
0.15

0.33
0.27

36.36
106.44 63.43

31.15
30.91

0.00
0.00

187.54
59.30

40.70

0.11
0.00

Number of retweets related to COVID-19 0.00
Ratio of positive sentences

31.25

16.16
6.47

0.00
0.00

93.56
53.92

26.79

14.70 14.79
Number of retweets with photos/videos

12.85 30.00

2020
***

0.28

0.27

.04

.04

.05

(1) Overall is the average of all participants (n = 501). Pattern 2 refers to exclusively Twitter users (n = 70); Pattern 4 refers to people who use Twitter and LINE (n = 149);
and Pattern 7 refers to people who use Twitter, LINE, and Instagram (n = 282)
(2) ANOVA: Overall is the F-value of the ANOVA for comparison from 2019 to 2021. The individual year value is the ANOVA for comparing the three patterns in the
same year
(3) ***p < .001; **p < .01; *p < .05

were significantly higher than Pattern 4 [2.58], F = 7.59,
p < 0.001), praise acquisition factor (i.e., Pattern 7 [3.27]
was significantly higher than Pattern 4 [3.01], F = 7.32,
p < 0.001), online communication skills (i.e., Patterns 2
[51.37] and 7 [52.83] were significant higher than Pattern
4 [48.19], F = 11.62, p < 0.001), Twitter usage period (i.e.,
Pattern 2 [52.61] was significant higher than Patterns
4 [41.49] and 7 [43.51], F = 4.21, p < 0.05), number of
Twitter accounts (i.e., Pattern 2 [3.64] was significantly
higher than Pattern 7 [2.77], which was also significantly higher than Pattern 4 [2.32], F = 13.95, p < 0.001),
number of Twitter accounts followed (i.e., Pattern
2 [1,003.03] was significantly higher than Patterns 4
[472.11] and 7 [432.98], F = 14.36, p < 0.001), number
of Twitter account followers (i.e., Pattern 2 [911.61]
was significantly higher than Patterns 4 [338.12] and 7
[442.61], F = 13.09, p < 0.001), and subjective well-being
(i.e., Pattern 7 [49.94] was significantly higher than Patterns 2 [43.89] and 4 [45.66], F = 12.13, p < 0.001) based
on ANOVA and post-hoc tests.
We further conducted a stepwise multiple regression
using subjective well-being as the dependent variable
for demographic attributes, factors related to personal
characteristics, and Twitter usage (Tables  2 and 4) as

independent variables. The results are presented inTable  5.4 As all VIF values are less than 3, our regression
models did not have the multicollinearity problem. It was
noted that praise acquisition positively affected the subjective well-being of students for all the three patterns.
For those who used Twitter only (Pattern 2), the generalized trust had positive effects on improving their subjective well-being, whereas their self-appeal and spending
more time on the Internet through smartphones lowered
their subjective well-being. Similar to Pattern 2, for those
who used Twitter and LINE (Pattern 4), the generalized
trust had positive effects on improving subjective wellbeing, as well as their self-establishment. Participants of
Pattern 7, those who used Twitter, Instagram, and LINE,
would have a higher level of subjective well-being and
self-establishment if they had a higher ratio of positive
sentences and a lower level of subjective well-being if

4 

Since gender is a nominal scale, males are set to “0”, females are set to “1”,
and others are set to “2” as dummy variables. Since the number of “others”
was small, only men and women were included in the following multivariate
analysis of this study.

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Page 7 of 12

Fig. 1  Ratios of positive and negative sentences based on social media usage patterns

they had more Twitter accounts or had a higher level of
self-indeterminism and self-independency.
We also compared the standardized coefficients
between the patterns, and the results are presented in
Table 6. As noted, the results of comparisons between the
coefficients were all insignificant, except for the comparison of Pattern 2 with Pattern 7 for praise acquisition.

Discussion
In this study, we collected personal data from university
students enrolled in the Kanto region of Japan through
a survey. In addition, we collected their log data (public posts) on Twitter. Then we analyzed the relationships between their social media use patterns, emotional
expressions on Twitter, and subjective well-being using
these variables.
Theoretical implications

We analyzed whether emotional expression in tweets
and retweets posted by university students on Twitter changed since the beginning of the COVID-19
pandemic. Additionally, we analyzed the results using
social media patterns and found that the number of
tweets and retweets (including tweets and retweets

with photos and videos) increased from 2019 to 2021
for all major patterns. This result is probably related to
the fact that about one-third of the participants were
freshmen who used Twitter to build new interpersonal
relationships in April 2021 when they started their
university lives [5]. In 2019 and 2020, they were only
second- and third-year high school students in the
Japanese education system,5 and refrained from taking
university entrance exams and seldom met their classmates during the pandemic.
In general, the effects of generalized trust, self-consciousness and friendship, and self-presentation on subjective well-being echoed previous findings. These results
included: (i) a high level of generalized trust (except for
Pattern 7), self-establishment (except for Pattern 2), and
praise acquisition from others improved their levels of
subjective well-being, and (ii) a high level of self-appeal
(for Pattern 2), self-indetermination, and self-independency (the latter two for Pattern 7) reduced their levels of
subjective well-being.

5 

Japanese high schools use a 3-year system.

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Page 8 of 12

Table 3  Changes in the number of tweets and retweets and emotional expression based on academic standing
Item
Number of tweets

Number of retweets

Number of tweets with photos/videos

Number of retweets with photos/videos

Number of tweets related to COVID-19

Number of retweets related to COVID-19

Ratio of positive sentences

Ratio of negative sentences

Academic Standing

January to June 2019 January to June 2020 January to
June 2021

First-year

50.56

72.19

422.33

Second-year

27.48

184.89

599.26

Third-year

98.33

238.63

405.64

Fourth-year or higher

175.42

248.58

407.37

F-value

8.89 ***

6.60 ***

2.13

First-year

12.23

23.37

66.31

Second-year

6.65

42.97

133.27

Third-year

19.42

42.36

94.96

Fourth-year or higher

34.53

51.14

73.14

F-value

4.43 **

1.18

2.75 *

First-year

8.90

17.41

46.98

Second-year

4.91

33.14

79.58

Third-year

16.54

27.41

55.87

Fourth-year or higher

22.75

33.06

51.28

F-value

5.51 ***

.85

1.92

First-year

4.43

12.73

21.71

Second-year

2.79

24.09

45.14

Third-year

8.39

10.82

32.50

Fourth-year or higher

7.70

15.60

22.48

F-value

1.92

.59

1.77

First-year

0.00

0.01

0.10

Second-year

0.00

0.13

0.16

Third-year

0.00

0.17

0.17

Fourth-year or higher

0.00

0.17

0.16

F-value

 − 

3.49 *

.55

First-year

0.00

0.00

0.00

Second-year

0.00

0.00

0.01

Third-year

0.00

0.01

0.00

Fourth-year or higher

0.00

0.01

0.01

F-value

 − 

1.13

.71

First-year

0.38

0.40

0.33

Second-year

0.39

0.29

0.29

Third-year

0.32

0.34

0.31

Fourth-year or higher

0.35

0.31

0.30

F-value

.95

3.58 *

.71

First-year

0.22

0.20

0.26

Second-year

0.22

0.29

0.29

Third-year

0.25

0.27

0.28

Fourth-year or higher

0.26

0.28

0.30

F-value

.78

4.36 **

.93

(1) The number of students in Year 1, 2, 3 and 4 are 162 149, 109, and 81, respectively. The F-value is the result of ANOVA for comparison between the mean value of
these 4 years
(2) ***p < .001, **p < .01, *p < .05

Regarding emotional expressions, we first analyzed the
proportion of positive and negative sentences on Twitter. We noted that the proportion of positive sentences

remained almost unchanged for 2.5  years, whereas the
proportion of negative sentences increased slightly
(Tables 2 and 3) during the same period. However, there

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Fig. 2  Ratios of positive and negative sentences based on academic standings

Table 4  Scores for each variable for different usage patterns
Items

Overall

Pattern 2

Pattern 4

Pattern 7

F-statistics

Generalized Trust

20.23

20.26

19.85

20.43

.829

Self-indeterminate factor

3.53

3.53

3.46

3.57

.63

Self-establishment factor

3.69

3.50

3.60

3.78

3.49 *

Self-independent factor

2.99

3.16

2.99

2.95

1.83

Self-variable factor

3.73

3.73

3.58

3.80

1.82

Rejection avoidance factor

2.86

2.94

2.58

2.99

7.59 ***

Praise acquisition factor

3.12

3.01

2.90

3.27

7.32 ***

Self-appeal factor

3.70

3.59

3.67

3.75

1.00

Topic avoidance factor

3.90

3.76

3.88

3.95

1.26

Online communication skills

51.24

51.37

48.19

52.83

11.62 ***

Twitter usage period (months)

44.18

52.61

41.49

43.51

4.21 *

Twitter accounts

2.77

3.64

2.32

2.77

13.95 ***

Twitter accounts followed

544.53

1,003.03

472.11

438.98

14.36 ***

Twitter accounts of followers

477.06

911.61

338.12

442.61

13.09 ***

Subjective well-being

47.82

43.89

45.66

49.94

12.13 ***

(1) Overall is the average of three groups of participants (n = 501). Pattern 2 refers to exclusively Twitter users (n = 70); Pattern 4 refers to people who use Twitter and
LINE (n = 149); and Pattern 7 refers to people who use Twitter, LINE, and Instagram (n = 282)
(2) ***p < .001, **p < .01, *p < .05

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Table 5  Factors affecting their subjective well-being between different use patterns
Factors

Pattern 2

Internet usage time by smartphones
Ratio of positive sentences

Pattern 4

Standardized
Coefficient

VIF

−.231*

1.034

Pattern 7

Standardized
Coefficient

VIF

The number of Twitter accounts
.271 ***

Generalized trust

.213 **

1.192

1.055

Self-indeterminate factor
Self-establishment factor

.195

#

1.439

.405

***

1.035

Self-independency factor
Praise acquisition factor

.385***

1.383

Self-appeal

−.319 **

1.283

R2 (adj.)
F-value

.229 **

1.037

Standardized
Coefficient

VIF

.159 **

1.026

−.113 *

1.018

−.150 **

1.118

.333 ***

1.129

 − .189 ***

1.115

.184 ***

1.105

.502

.313

.308

14.90 ***

23.48 ***

21.07 ***

(1) Pattern 2 refers to exclusively Twitter users (n = 70); Pattern 4 refers to people who use Twitter and LINE (n = 149); and Pattern 7 refers to people who use Twitter,
LINE, and Instagram (n = 282)
(2) ***p < .001, **p < .01, *p < .05

Table 6  Comparison of regression coefficients
Patterns

Unstandardized
Coefficient

Degree of Freedom

SE1

SE2

SE Combined

t-value

#1

#2

b1

b2

df1

df2

Combined

Generalized trust

2

4

.707

.545

69

148

215

.242

.183

.243

.666

Self-establishment

2

4

2.498

5.011

69

148

215

1.305

.892

1.486

4

7

5.011

3.775

148

271

417

.892

.609

.921

−1.691

2

7

2.498

3.775

69

271

338

1.305

.609

1.325

2

4

4.806

2.606

69

148

215

1.249

.778

1.335

4

7

2.606

2.085

148

271

417

.778

.602

.808

.644

2

7

4.806

2.085

69

271

338

1.249

.602

1.268

2.146 *

Praise acquisition

1.343

−.964
1.648

(1) Pattern 2 refers to exclusively Twitter users (n = 70); Pattern 4 refers to people who use Twitter and LINE (n = 149); and Pattern 7 refers to people who use Twitter,
LINE, and Instagram (n = 282)
(2) ***p < .001; **p < .01; *p < .05

were almost no tweets or retweets about COVID-19.
In other words, many of the emotional expressions in
the posts of university students are indeed negative, but
it is difficult to determine whether it is due to COVID19. Japan was one of the few nations worldwide that did
not lock down the country during the pandemic; thus,
its negative impact would probably be less than that of
other countries. However, when further examining the
proportion of positive and negative sentences based on
the usage patterns and academic standings of the participants, it can be noted that there is a general trend of a
decrease in the ratio of positive sentences for the overall
group and based on the patterns. This may be due to the

COVID-19 situation making people have bad emotions
in 2020; thus, they felt unhappy.
Looking at the findings through the lens of academic
standings, we noted that while first-year students had a
higher ratio of positive sentences most of the time, the
percentage of positive sentences saw a decrease in 2021.
All three other groups also either had a decrease or flatted out from 2020 to 2021. The findings of the firstyear students relate to their academic journey, as these
participants were in high school in 2019 and 2020, and
entered university in 2021. The reduction in the ratio of
positive sentences could be due to the change in their
living environment (as they would relocate from their

Ye et al. BMC Public Health

(2023) 23:594

hometown to the university and faced changes in their
lives) and their attitude toward COVID-19. Indeed,
our ANOVA results showed that the differences in the
ratios in 2021 among the four groups were statistically
insignificant.
However, there was an interesting observation regarding the ratio of negative sentences. While there was an
increase in the ratio of negative sentences in 2020 for
second-, third- and fourth-year students, there was a
decreasing trend for first-year students. For first-year students (who were high school students in 2019 and 2020),
their lives were probably not significantly adversely
affected by COVID-19 in 2020. They might even have
fresh experience in participating in the coursework
through various types of online courses. Therefore, these
new exposures might make them feel adventurous and
have fewer complaints. However, second-year students
might have initially experienced a lot of pressure in 2019
(as they were facing university admission examinations
when the news about COVID-19 broke out in December
2019) and had a sharp increase in the ratio of negative
sentences. These students probably had a more difficult
time during their campus life compared to other grades,
as all classes in the Spring semester were conducted
online, and most classes were still conducted online in
the Autumn semester. All extracurricular activities were
suspended during that period, which meant that they had
few opportunities to communicate with their classmates
in person to reduce their anxiety and stress. As a result,
we still observed an increase in the ratio of negative sentences in 2021.
We also noted that for Twitter-only users (Pattern 2),
subjective well-being decreased if they spent too much
time on the Internet via their smartphones. An effect
of the ratio of positive sentences was observed for users
who used Twitter, LINE, and Instagram (Pattern 7). The
ratio of positive sentences on Twitter was positively
related to subjective well-being, which may indicate
that users with higher levels of subjective well-being are
more likely to use more positive sentences. As Ye [5]
clarified that compared to other patterns’ users, Twitteronly users received the least social support and had the
lowest level of subjective well-being, while they had the
highest level of depression tendency. This might be due
to the highest visual anonymity on Twitter, which allows
users to connect with strangers and have posts without
letting other people know who they are. On the other
hand, users of Twitter, LINE, and Instagram might be
able to make posts freely on Twitter and communicate
with strangers while communicating with their family
and intimate friends through LINE, and also communicate with those friends/acquaintances who are not that

Page 11 of 12

intimate on Instagram. This kind of usage allows them to
keep a good balance between intimate people and strangers, which helps them receive various kinds of social
support (instrumental and emotional), therefore, has
effects on improving their subjective well-being.
To conclude, our findings show that emotional expressions that affect subjective well-being differed depending on the pattern of use of other social media, even if all
participants used Twitter.
Practical implications

The finding of this study shows the relationships between
the subjective well-being of university students in Japan
and their social media use patterns. Therefore, we suggest that public health authorities should consider taking
our findings in developing programs help young adults
who face stress and other mental health issues due to the
COVID-19 crisis [18] [19], such as using young adults’
social media use patterns as a proxy to analyze their
behavior and the corresponding mental health concerns
for developing better support for them.

Conclusion
This paper presents valuable information on how university students’ emotional expressions on Twitter were
related to their subjective well-being in Japan. Through
the results of regression analyses, we found that subjective well-being was affected by the time spent on the
Internet through smartphones (for Pattern 2), the percentage of positive sentences (for Pattern 7), and the
number of Twitter accounts (also for Pattern 7).
Limitations and future research directions

This study has some limitations. Even though 1,681 social
media users participated in the survey, only 577 of them
had public tweets and retweets. Additionally, among the
final 501 responses, we could only analyze three of the
15 possible combinations of social media usage patterns.
Furthermore, the number of tweets and retweets we collected from the respondents’ public records was insufficient to analyze their behavior further quarterly. To
better understand how social media usage patterns relate
to users’ subjective well-being, it would be necessary to
recruit more respondents of different ages with more
public tweets/retweets to analyze in more detail.
Acknowledgements
The authors would like to thank all the participants who helped answer the
survey and provide log data, and all the people for their kind cooperation.
Informed consent statement
Informed consent was obtained from all participants.

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(2023) 23:594

Authors’ contributions
Shaoyu Ye wrote the main manuscript and conducted the first-round analysis.
Kevin K.W. Ho wrote part of the Literature and Discussion and prepared
figures. Kei Wakabayshi wrote part of the Research Method and Results and
conducted the log data analysis (natural language processing). Yuuki Kato
conducted emotional expression analysis based on Kei Wakabayashi’s analysis
results. The author(s) read and approved the final manuscript.
Funding
This study was supported by JSPS KAKENHI Grant Number 21H03770 (principal investigator: Dr. Shaoyu Ye).
Availability of data and materials
The datasets used and analyzed during the current study are available from
the corresponding author upon reasonable request.

Declarations
Ethics approval and consent to participate
The study was conducted with the approval of the Research Ethics Review
Board at the Faculty of Library, Information and Media Science, University of
Tsukuba, Japan. All methods were carried out in accordance with relevant
guidelines and regulations.
Consent for publication
It is not applicable for the consent for publication.
Competing interests
The authors declare no competing interests.
Received: 17 October 2022 Accepted: 20 March 2023

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