# Political Honeymoon Effect on Social Media: Characterizing Social Media
Reaction to the Changes of Prime Minister in Japan

Kunihiro Miyazaki  Indiana University BloomingtonIndianaUSA<EMAIL_ADDRESS>,  Taichi Murayama  SANKEN, Osaka UniversityOsakaJapan
<EMAIL_ADDRESS>,  Akira Matsui  Yokohama National
UniversityKanagawaJapan<EMAIL_ADDRESS>,  Masaru Nishikawa  Tsuda
UniversityTokyoJapan<EMAIL_ADDRESS>,  Takayuki Uchiba
SugakubunkaTokyoJapan<EMAIL_ADDRESS>,  Haewoon Kwak  Indiana
University BloomingtonIndianaUSA<EMAIL_ADDRESS>and  Jisun An  Indiana
University BloomingtonIndianaUSA<EMAIL_ADDRESS>
(2023)

###### Abstract.

New leaders in democratic countries typically enjoy high approval ratings
immediately after taking office. This phenomenon is called the honeymoon
effect and is regarded as a significant political phenomenon; however, its
mechanism remains underexplored. Therefore, this study examines how social
media users respond to changes in political leadership in order to better
understand the honeymoon effect in politics. In particular, we constructed a
15-year Twitter dataset on eight change timings of Japanese prime ministers
consisting of 6.6M tweets and analyzed them in terms of sentiments, topics,
and users. We found that, while not always, social media tend to show a
honeymoon effect at the change timings of prime minister. The study also
revealed that sentiment about prime ministers differed by topic, indicating
that public expectations vary from one prime minister to another. Furthermore,
the user base was largely replaced before and after the change in the prime
minister, and their sentiment was also significantly different. The
implications of this study would be beneficial for administrative management.

Twitter, honeymoon effect, prime minister, sentiment analysis, regression
discontinuity design

††copyright: acmcopyright††journalyear: 2023††copyright:
acmlicensed††conference: 15th ACM Web Science Conference 2023; April 30-May 1,
2023; Austin, TX, USA††booktitle: 15th ACM Web Science Conference 2023 (WebSci
’23), April 30-May 1, 2023, Evanston, TX, USA††price: 15.00††doi:
10.1145/3578503.3583594††isbn: 979-8-4007-0089-7/23/04††ccs: Applied computing
Sociology††ccs: Social and professional topics††ccs: Information systems World
Wide Web Figure 1. Monthly approval ratings for the Japanese prime ministers
since 2006. The timing of the change of prime minister is annotated with a red
dotted line. Data from (NHK, 2022a).

##  1\. Introduction

Newly elected national leaders, such as presidents and parliamentary prime
ministers (PMs), customarily enjoy high approval ratings immediately after
their inaugurations. This phenomenon, known as the _honeymoon effect_ , has
been observed in democratic countries around the world (Segatti et al., 2015;
Qadan and Idilbi, 2022; Masuyama, 2007; Begley and Sheard, 2021). Figure 1 is
a visualization of monthly approval ratings of the Japanese cabinet from 2006
with the timings of the PMs’ change annotated, which indicates a significant
jump in approval ratings at each change timing.

Since the honeymoon effect is an important phenomenon from the perspective of
government management, politicians and the news media pay great attention to
it. For example, new leaders tend to take advantage of this period of high
approval ratings (i.e., the honeymoon period) as it is the best time to
implement the most audacious (but potentially unpopular) policies that can
leave their mark on the future economy and society (Dominguez, 2005; Nyblade,
2011; Qadan and Idilbi, 2022). Additionally, when the approval rating of the
current leader declines, the administrative party attempts to preserve the
people’s support for the party by putting pressure on the current leader to
step down, hoping for the next honeymoon effect (Nyblade, 2011; Uchiyama,
2022).

Despite its importance, the mechanism of the honeymoon effect is
underexplored. There are many narratives for why the honeymoon effect occurs,
most of which come from the news media and previous studies. For example, it
is driven by the public expectation of a new leader (Segatti et al., 2015),
which makes the mainstream media more gentle to him/her (Hughes, 1995). On the
other hand, such positivity could appear as the public appreciate the previous
leader’s decision to resign (news, 2020), or treat the resignation as the
virtue of integrity (Kambe, 2017). In a nutshell, these narratives see the
honeymoon effect as a change in sentiment toward two persons—a new leader and
a previous leader. However, to what extent these narratives reflect reality
has not been sufficiently verified.

In this work, we aim to deepen our understanding of the honeymoon effect by
analyzing social media reactions to changes in national leadership. To this
aim, we first collect the tweets around the changes in the PMs of Japan and
construct the dataset that records the reactions to the eight changes of PMs
on social media over fifteen years. We primarily use Japanese tweets for the
analysis. Here, we would like to stress that Japan can provide a desirable
case study for analyzing honeymoon effects on social media because 1) Japan
has highly frequent changes of PMs compared to other countries since 2006
(Ryoichi, 2021) when Twitter started its service, 2) Japan maintains a high
degree of national and linguistic congruence (distribution, 2022), which is
easy to detect the originating country of tweets, and moreover 3) Twitter is
quite popular in Japan with approximately 60M users (almost half of its
population) and roughly the same number of daily active users as U.S. (NHK,
2022b), from which we can expect a high correlation between the opinions on
Twitter and the real world.

We tackle the following research questions by leveraging the dataset:

RQ1: How does the honeymoon effect appear on social media? We examine whether
there is a change in sentiment at an aggregated level on Twitter before and
after PMs’ change. Social media allows us to extract people’s raw voices, and
if we can observe the honeymoon effect on social media as well, we can get
insights for government management from it.

RQ2: What aspects contribute to the change of sentiments for a new PM? We
analyze the sentiments by topics to see what aspects people do expect from the
new PMs. If the honeymoon effect arises from the expectations of the new PM,
understanding which topics he/she is expected to address may help him/her
avoid overpaying the cost of approval ratings when implementing bold policies
(Nyblade, 2011).

RQ3: Who are changing the sentiments toward the prime ministers? We analyze
the change in sentiment toward PMs at the user level. Knowing who changes the
sentiments toward PM would be helpful for the communication strategies of new
PMs. Also, to know whether the change of PM has successfully erased the
negative image associated with the previous PM may have an impact on the
actions that the governing party should take when the PM’s approval rating
declines again (Masuyama, 2007).

As a result, we found social media tend to have a significant jump in
sentiment in PM change timings (4 out of 8 times), with one timing when
sentiment significantly declined and no significant results in the remaining
three times. The sentiment was often significantly lower for PM-specific
topics than for topics common to all PMs. Also, comparisons among PMs using
common topics showed that topics with positive sentiment differed by PMs.
Finally, we revealed that the majority of users were largely replaced between
those who tweeted about the previous PM before the resignation and those who
tweeted about the new PM after the inauguration. Furthermore, the sentiments
of these different user groups were significantly different in many cases (7
out of 8 times). In addition, the sentiment of users who tweeted about the
same PM after the inauguration and before the resignation was significantly
different (5 out of 8 times). In many cases, the sentiment was lower before
the resignation (4 out of 5 times), confirming the honeymoon effect in a
different way.

Our contribution is as follows:

1. To the best of our knowledge, this is the first study to examine the political honeymoon effect using social media. By looking into the past cases of the honeymoon effect, we can gain insights into how changes in administration can improve or exacerbate the quality of governance. Moreover, the implications from this study are expected to be helpful not only in future political communication but also in other contexts such as corporate management (i.e., CEO resignation).

2. We construct a longitudinal dataset on the change of PM in Japan. We take full advantage of Twitter data to create a 15-year tweet dataset on eight Japanese PMs consisting of 6.6M tweets. This dataset is publicly available 111https://github.com/Mmichio/honeymoon_effect_public/.

3. We demonstrate various data-analysis methodologies in this study. We use a combination of sentiment analysis and regression discontinuity design to capture the honeymoon effect. In addition, we propose an analytical flow to distinguish between unique and common topics for each PM in the topic analysis for multiple PMs, which allows for a cross-topic analysis of historical PMs. We believe these methods will be helpful for future research.

##  2\. Background and Related Works

###  2.1. Political honeymoon effect

The honeymoon effect, in political terms, is the high approval ratings enjoyed
by a new leader immediately after taking office (Azari, 2017; Beckmann and
Godfrey, 2007). Previous research on this political honeymoon effect has been
concerned with examining the existence of the honeymoon effect itself, as well
as the derived effects. Dominguez (2005) found that U.S. presidents are more
successful in passing legislation during their first 100 days in office than
thereafter. However, it is said that the 45th president of the US, Donald
Trump—and maybe the 46th president, Joe Biden as well— could not get the
honeymoon period, and it has been debated whether a lack of honeymoon is a new
normal or not (FiveThirtyEight, 2021). Segatti et al. (2015) quantitatively
analyzed the honeymoon effect in Italy that led to Renzi’s victory in the
European elections immediately after his administration came to power.
Masuyama (2007), noting that a PM’s approval rating gradually declines after
the honeymoon effect, attempted to model the relationship between the
probability of a PM’s resignation and his approval rating by using survival
time analysis. Dewan and Dowding (2005) analyzed that when a PM has a minister
resign in the cabinet in the UK, the PM’s major decision is praised, which
contributes to a recovery in approval ratings, although this is not the
resignation of the PM. Qadan and Idilbi (2022) analyzed the behavior of
commodity markets during the U.S. honeymoon period and showed that price
volatility was lower during this period. Additionally, studies have shown that
the honeymoon effect is associated with the consolidation of democracy: the
honeymoon effect is an important research topic in the field of comparative
politics as well (Maeda, 2015; Bernhard et al., 2003).

In this study, for the first time to our knowledge, we analyze the factors
that cause the honeymoon effect to occur from a social media perspective. In
particular, we examine the change in sentiment toward a new leader and a
previous leader, which has been at the heart of previous narratives on the
political honeymoon effect. Also note that the focus of this study is on the
honeymoon effect, i.e., the jump in approval ratings right after the change of
PMs, and not on the honeymoon period, during which high approval ratings
persist.

###  2.2. Resignations of Japanese Prime Ministers

In a parliamentary cabinet system, which the Japanese government adopts, PMs
are often replaced when they lose public support and their approval ratings
become quite low (Ryoichi, 2021). In particular, Japan’s PMs around 2010 were
replaced at a higher frequency than the heads of government in other
countries, e.g., Germany and the UK, and most of them were in office for
shorter terms such as about one year (Ryoichi, 2021). This is said to be
because, since the late 1990s, changes in the electoral system have made the
reputation of the PM as the face of the party more important (Nyblade, 2011).
That is, even from the perspective of politicians belonging to the
administrative party, the PM’s approval rating has a significant impact on his
or her own election results; thus, when the PM’s approval rate is low, there
is pressure from the party members to resign, and unpopular PMs tend to have
shorter terms in office (Nyblade, 2011; Uchiyama, 2022). We regarded this
frequent turnover in Japan as valuable for an object of analysis because a
change of national leaders is generally a rare event that is usually difficult
to analyze quantitatively.

##  3\. Data Collection

Change |  | Resignation |  |  | Party of | Tweet volume
---|---|---|---|---|---|---
timing | Prev PM | declaration | New PM | Inauguration | New PM | Prev | New | Total
1 | Shinzo Abe | 2007-09-12 | Yasuo Fukuda | 2007-09-26 | LDP | 391 | 183 | 574
2 | Yasuo Fukuda | 2008-09-01 | Taro Aso | 2008-09-24 | LDP | 1,538 | 2,201 | 3,739
3 | Taro Aso | 2009-08-30 | Yukio Hatoyama | 2009-09-16 | DPJ | 7,523 | 27,457 | 34,980
4 | Yukio Hatoyama | 2010-06-02 | Naoto Kan | 2010-06-08 | DPJ | 187,156 | 135,290 | 322,446
5 | Naoto Kan | 2011-06-02 | Yoshihiko Noda | 2011-09-02 | DPJ | 762,486 | 206,372 | 968,858
6 | Yoshihiko Noda | 2012-12-16 | Shinzo Abe | 2012-12-26 | LDP | 203,418 | 442,322 | 645,740
7 | Shinzo Abe | 2020-08-28 | Yoshihide Suga | 2020-09-16 | LDP | 1,490,975 | 662,597 | 2,153,572
8 | Yoshihide Suga | 2021-09-03 | Fumio Kishida | 2021-10-04 | LDP | 956,954 | 503,165 | 1,460,119

Table 1. The change timings of PMs, the names of the prev/new PMs, the dates
of resignation declaration and inauguration, the party of new PMs (i.e.,
Liberal Democratic Party (LDP) and the Democratic Party of Japan (DPJ)), and
the tweet volume of each change timing regarding prev/new PMs.

There have been eight PM changes in Japan since 2006, and we collected user
reactions for each of those eight changes. There are two key events regarding
the change of PM to evoke user reactions: the previous PM’s resignation
declaration and the new PM’s inauguration. In this study, the period for
obtaining tweets was set from 60 days prior to the previous PM’s resignation
declaration to 60 days after the new PM’s inauguration. It should be noted
that there is a lag between these two events, but in the analysis, we do not
include the data from this lag, although we obtained the data in this lag. The
tweets were acquired for the period for each change timing by using Twitter
Academic API (Pfeffer et al., 2023).

To retrieve tweets about all PMs involved in those timings, we set the search
keywords to be formal as follows (English translation is associated with
Japanese words):

  * •

Surname + given name (i.e., full name, e.g., “岸田文雄” (Fumio Kishida)),

  * •

Surname + “首相” (Prime Minister),

  * •

Surname + “総理” (Prime Minister),

  * •

Surname + “政権” (Administration),

  * •

Surname + “内閣” (Cabinet).

In order to increase the precision of the detection of tweets about the PMs
and cabinets, we did not include keywords of only surnames, surnames with
casual titles (e.g., Kishida-san), and other nicknames.

We did not include retweets in the collection since our purpose was to capture
user sentiment. Also, since Japanese is mostly spoken in Japan (distribution,
2022), we did not conduct additional filtering based on the country. As a
result, we collected around 6,557,452 tweets written by 855,694 unique users.

As a post-processing of tweet acquisition, we conducted user-based removal of
inappropriate tweets. First, since we were interested in the sentiment of
general users, we excluded accounts marked as verified (Edgerly and Vraga,
2019)222Note that we finished our data collection before the Twitter
acquisition by Elon Musk, which affects the quality of verified mark (Duffy,
2022).. Next, to preserve ordinary users’ accounts, we removed the following
accounts:

News media: accounts that have “ニュース” (news), “news,” “新聞” (newspaper), and
“テレビ” (TV) in the account names, and

Organizations: accounts that have “公式アカウント” (official account) in the
description.

We also used Botometer (Davis et al., 2016) to remove bot-like accounts.
Botometer provides scores from 0 to 1 to measure how the accounts are likely
to be bots. There are two types of scores: one for English and the other for
non-English (the language-independent score), among which we used the latter
one. Since the prior studies suggested the threshold of Botometer score to
classify bots and humans mainly targeted English texts and even the threshold
of the score for English accounts varied to some extent (Rauchfleisch and
Kaiser, 2020), we decided to verify the accuracy with a bootstrap method to
decide a threshold. Specifically, for four groups with scores of (0.8-0.85],
(0.85-0.9], (0.9-0.95], and (0.95-1.00], we randomly sampled 30 accounts for
each group, and three Japanese authors manually annotated whether the account
was considered to be a bot or not As a result of majority voting, 13.3%
(Fleiss’ Kappa score: 0.24), 26.7% (0.19), 43.3% (0.49), and 63.6% (0.45) were
determined to be bots, respectively. Therefore, in order to remove obvious
bots, 0.95 was set as the threshold for this study, and accounts with scores
higher than that were removed. We note we could not remove all bots in our
data, which is a limitation of this study. Nonetheless, the ratio of
(0.95-1.00] users is 0.5%, and one of even (0.9-0.95] users is 2.2%, and we
believe there is no significant impact on the results. In addition, to remove
low active accounts, we removed accounts with a total tweet volume of less
than ten (according to their profile information) at the time of data
acquisition (i.e., Nov. 2022). At this point, the number of tweets was 6.1M,
and the number of users was 838k.

Finally, to focus on the sentiment toward a single PM, tweets containing the
surname of more than one PM were excluded. This results in 5,590,028 tweets by
users 818,043 users. We use this dataset for the rest of the analyses. Table 1
summarizes the name of the previous and new PMs, the date of declaring
resignation, the date of inauguration, and the number of tweets for each
change timing. We note that the administrative party changed twice in those
eight change timings between the Liberal Democratic Party (LDP) and the
Democratic Party of Japan (DPJ). Also, the dataset includes Abe’s two
appointments.

##  4\. RQ1: How does the honeymoon effect appear on social media?

In this section, we examine whether the honeymoon effect also occurs on social
media. Specifically, we analyze the change in sentiment toward the PM before
and after the change of PM using sentiment analysis and a regression
discontinuity design (RDD).

###  4.1. Sentiment Analysis

Predictive Model: We first predict the sentiment of each tweet. As a
predictive model, we use Asari, an open-source sentiment quantification model
for Japanese sentences (Hirosan, 2019a). Asari is an SVM-based model that
returns a sentiment value between 0 and 1 (the closer to 1, the more positive)
given a Japanese sentence. We chose Asari because it is reported to perform
with compelling accuracy as BERT-based models and is fast (Hirosan, 2019b).
Although dictionary-based methods such as LIWC (Pennebaker et al., 2015) (or
J-LIWC for Japanese texts (Igarashi et al., 2021)) are frequently used for
sentiment analysis, we did not use those methods because we found that some
PMs had only a few tweets with sentiment words in the dictionary. As a
bootstrap confirmation, we randomly sampled 30 tweets each from the upper half
(i.e., more than the median score) and the bottom half (i.e., less than the
median score) in terms of sentiment to see whether a Japanese author could
classify them, resulting in an F1 score of 0.73.

Aggregation of sentiment scores: We aggregate sentiment on a daily basis
because the number of tweets may vary by time of day. Also, to mitigate the
influence of the more vocal users who tweet multiple times a day, we first
averaged the sentiment of each user per day, and then averaged the sentiment
of all users who tweeted that day. The sentiment $S$ for a day $t$ for a given
PM $p$ is depicted as follows:

(1) $S(t,p)=\sum_{u:user}\frac{\sum_{d\in
D_{u,p,t}}s(d)/|D_{u,p,t}|}{|U_{t}|}$

where $t$ is the time (day), $p$ is the PM, $d$ is the document (tweet),
$s(d)$ is the sentiment score of the tweet $d$ by Asari, $D_{u,p,t}$ is the
set of the user $u$’s tweet about $p$, and $U_{t}$ is the set of all users who
tweeted on that day $t$.

###  4.2. Regression Discontinuity Design

To see the change in sentiment before and after the change of PM, we perform a
regression discontinuity design (RDD). We employ a linear model:

(2) $S_{t}=\alpha_{0}+\beta_{0}t+\alpha i_{t}+\beta i_{t}t+\epsilon_{t}$

where $S_{t}$ is the sentiment toward a given PM on a given day $t$ (eq 1),
$t$ is a date, which takes values from -60 to +60, starting 60 days before the
previous PM’s declaration of resignation and ending 60 days after the new PM’s
inauguration date, and $i_{t}$ is an indicator variable, which is 1 after the
change of PM (i.e., $t>0$) and 0 otherwise. Here, the inauguration date
($t=1$) is hypothetically set to be the next day of the date of the
resignation declaration ($t=0$). We assumed the lag between the actual
resignation declaration and the inauguration date to be a “grace period”
(following (Horta Ribeiro et al., 2021)), and the data for that period is
omitted because PM’s resignations are often sudden and tend to cause
instability in sentiment. This model allows us to capture not only the change
in the quantity of sentiment before and after the change of PM, represented by
$\alpha$, but also the trend in sentiment before and after the change,
represented by $\beta_{0}$ and $\beta$; thus, we can understand the change in
sentiment more deeply than by simply comparing the average of sentiment before
and after change timing. Lastly, $\epsilon_{t}$ is an error term at $t$.

In the introduced RDD model, we input the sentiment of the previous PM before
the resignation declaration and the sentiment of the new PM after the
inauguration into the model. This is in line with the traditional survey that
changes the targets from the previous PM to the new PM before and after the
change of PMs.

###  4.3. Results

Figure 2 shows the results of the RDD. The $\alpha$ of each subplot shows that
sentiment increased significantly in four of the eight change timings (change
timing: 1,3,7,8), while three did not show a significant change (2,4,5), and
one showed a significant drop (6). In other words, we observed that social
media tends to show the honeymoon effect, but not at every change timing.
While it has been known that the demographics of Twitter are not necessarily
the same as in the real world (An and Weber, 2015), this result demonstrates a
possibility of utilizing social media for the analysis of the honeymoon effect
using sentiment toward the PM.

It is also interesting to see clear jumps in sentiment in the last two changes
(Figure 2-7: Abe$\rightarrow$Suga; Figure 2-8: Suga$\rightarrow$Kishida).
Abe’s second administration lasted about eight years, the longest among all
the Japanese PMs in history, and the number of Twitter users increased during
that time. In fact, the number of tweets jumped dramatically during the last
two change timings (Table 1). In other words, the demographics of Twitter
might have approached the actual population over the eight years, and the fact
that the honeymoon effect was clearly confirmed twice in these circumstances
may suggest that the correlation between changes in actual approval rating and
sentiment has increased because the number of Twitter users increased.

Figure 2.  Change in daily sentiment before and after the change of PMs. The
left side of the red line shows the period before the PM announced the
resignation, and the right side shows the period after the new PM took office.
The blue line shows the daily volume of tweets, and the orange dots show the
average daily sentiment, with the left side about tweets about the former PM
and the right side about tweets about the new PM. The green line depicts the
model fitted to the sentiment in the regression discontinuity design (RDD).
Above the subplots, we report coefficients associated with the PMs’ change in
the model ($\alpha$ and $\beta$). Coefficients for which $p<$0.001, 0.01, and
0.05 are marked with ***, **, and *, respectively.

##  5\. RQ2: What aspects contribute to the change of sentiments for a new PM?

In RQ1, we found that sentiments toward PMs significantly change at multiple
change timings of PMs. Here we examine from which aspects the sentiment causes
these changes using topic modeling.

###  5.1. Topic Modeling

Model selection and data: We use the Biterm Topic Model (BTM) (Yan et al.,
2013) for the topic modeling. This model is a derivative of LDA (Blei et al.,
2003) and is known to be able to extract topics with higher accuracy for short
sentences. For this analysis, we focus on the tweets during the 60 days after
the inauguration of PMs at each change timing. We combine the corpus for all
new PMs for the later comparative analysis of topics across PMs. To reduce the
imbalance in data volume, we limit the number of tweets for each PM to
(randomly sampled) 30,000 for the model training. Then, we remove the
stopwords from texts and leave only nouns (Martin and Johnson, 2015), and use
BTM to label each tweet with the topic with the highest possibility calculated
by BTM. After training the model, we predict the topic of tweets that were not
used for training and include them for later analysis.

Number of topics and separation of unique and common topics: To determine the
number of topics, we set the number of topics large at the beginning and then
merge them to find the optimal number of common topics as in (Balasubramanian
et al., 2022). Here, it is possible that some topics are unique to some PMs,
and some are common to all PMs. Topics common to all PMs allow for comparison
of sentiment across PMs, but this is not the case for unique topics.
Therefore, we leverage the topic number selection method to classify unique
topics and common topics in the process of merging topics. Specifically, for
each topic, we calculated $R(T,p)$, the ratio of the amount of text as for
topic $T$ assigned to each PM $p$. Then, if $R(T,p)$ of a PM $p$ about a fixed
topic $T$ outstands among PMs, this topic can be considered a unique topic for
PM $p$. We calculate $R(T,p)$ while addressing the imbalance in tweet volume
among PMs. To calculate $R(T,p)$, we first compute the ratio $r(T,p)$: how
dominant a topic $T$ is from the tweets for a PM $p$.

(3) $r(T,p)=\frac{\left|\left\\{\text{tweet}\in T\mid\text{tweet for a PM
}p\right\\}\right|}{\sum_{T^{\prime}:\text{topic}}\left|\left\\{\text{tweet}\in
T^{\prime}\mid\text{tweet for a PM }p\right\\}\right|}.$

Next, we calculate $R$ by modifying $r$ so that the sum of $r$ among PMs is 1:

(4) $R(T,p)=\frac{r(T,p)}{\sum_{p^{\prime}:\text{PM}}{r(T,p^{\prime})}},$

i.e., $R(T,p)$ is the normalized version of $r(T,p)$. Here, assuming that
tweets about each PM are equally assigned in terms of a given topic $T$, the
expected value of this score $R$ should be equally divided by 8 and be 0.125.
We divide the topics generated by BTM into unique and common topics using a
certain threshold of max $R$ among the $R$ scores for all PMs.

We tested 25, 50, 75, 100, and 125 topics and chose 75 as the initial number
of topics by comparing the perplexity scores (Zhao et al., 2015). Then, we
conducted BTM for all the tweets within the period of 60 days after all the
inaugurations. In order to set the threshold for $R$ to classify the unique
and common topics for PMs, we sorted the topics by the max value of PMs’ $R$
scores. After the first author, who is fluent in Japanese, manually examined
the top representative words of each topic in terms of the possibility of
belonging assigned by BTM, we decided the threshold as 0.285, below which the
topics are no longer unique for any of the PMs. In other words, for a topic
$T$, the smaller the max $R$ is, the flatter the distribution of $R$ for all
PMs becomes. Finally, the Japanese authors manually checked and replaced the
labels of a total of 12 topics with the $R$ scores around the threshold (7
from unique and 5 from common topics). As a result, we obtained 52 unique
topics and 23 common topics for all the PMs. As for unique topics, we got 5
topics for Fukuda, 5 for Hatoyama, 4 for Kan, 8 for Noda, 3 for Aso, 10 for
Abe, 7 for Suga, and 10 for Kishida. The details on common topics are shown in
Table 4 in the appendices, while we omitted the details on unique topics for
the reason of space.

Merging Common Topics: Some of the 23 common topics overlap in terms of their
contents; thus, we merge them to find an optimal number of common topics.
Here, we propose a clustering method to systematically merge similar topics.
Specifically, we first connected ten representative words of each topic into
one pseudo-sentence and made 23 sentences. Then, we use the trained BTM to
calculate the probability of each sentence belonging to each topic and
calculate the Hellinger distance between the sentences using the probabilities
(Srivastava and Sahami, 2009). Lastly, we make the distance matrix based on
the Hellinger distance of the sentences and conduct hierarchical clustering
with Ward’s method (Murtagh and Legendre, 2014) to aggregate similar
sentences. We manually determined the threshold for aggregation by
hierarchical clustering by looking at the dendrogram and the contents of
resulting clusters (Figure 6 shown in the Appendices). We note that we also
tried clustering based on the average of the top word embedding by BERT model
(Devlin et al., 2019; Suzuki, 2019) and Euclidean distance between topics, but
the clusters made more sense when using the topic distribution by BTM and the
Hellinger distance.

As a result, we obtained four clusters. We label them as Diplomacy, Economy,
Cabinet personnel (Personnel), and Diet administration (Diet) (i.e., handling
and running the government) by examining the representative words of the
topics that comprise the cluster (shown in Table 4 in the Appendices).

###  5.2. Results

First, we compare the sentiments of common and unique topics. Expectations and
criticisms of the PM can occur on both common and unique topics. For example,
it is natural to assume that the economy and diplomacy are common topics among
PMs. On the other hand, occasional topics, such as disasters and epidemics,
and personal topics, such as scandals, would be considered unique topics.
Depending on which sentiment is lower, common topics or unique topics, the
government’s policy would vary. For example, if sentiment on common topics is
low, there is much that should be learned from past PMs. On the other hand,
when unique topics indicate low sentiments, especially scandals, the PM and
the government would elaborate unique solutions for the matter.

Table 2 shows the average sentiments of common/unique topics for each PM with
the results of the Mann-Whitney U test. We note that we use a user-based daily
average of sentiments to mitigate the influence of extremely active users in
the same way as eq (1). The results show that sentiment is largely lower for
unique topics: four PMs have significantly lower sentiment for their unique
topics, two PMs have significantly higher sentiment for common topics, and two
PMs do not have significant results. Here, we look at the unique topics with
the lowest sentiment for four PMs who had lower sentiment for unique topics:
Kan for consumption tax hike (Blair, 2010) (sentiment value: 0.643), Noda for
nuclear power plant operation (Tsukimori and Kebede, 2011) (0.626), Suga for
pressure on academia (i.e., the Science Council of Japan) (Shimbun, 2020)
(0.640), and Kishida for unconditional cash-only handouts for COVID-19 (Press,
2021) (0.542). The results actually show that sentiment was low on issues that
were unique to the time or the PM’s personal policy. Conversely, when the
honeymoon effect occurs on social media, it can often be caused by a common
topic.

New PM | Common | Unique | Significance
---|---|---|---
Fukuda | 0.848 | 0.849 | -
Aso | 0.781 | 0.885 | <<<
Hatoyama | 0.782 | 0.828 | <<<
Kan | 0.751 | 0.736 | >>>
Noda | 0.738 | 0.734 | >
Abe_2 | 0.636 | 0.636 | -
Suga | 0.721 | 0.698 | >>>
Kishida | 0.704 | 0.679 | >>>

Table 2.  A comparison of sentiment on common and unique topics For each PM.
Significance shows the results of the Mann-Whitney U test, where the number of
brackets indicates 3: $p<0.001$, 2: $p<0.01$, 1: $p<0.05$. The direction of
the brackets indicates the greater values (e.g., for Aso, unique topics have
higher sentiment than common topics).

Next, we conduct a comparison of sentiments between PMs about common topics.
Here, since the average sentiment differs from topic to topic and from PM to
PM, we correct the sentiment values in order to make the comparison. In
particular, we create a PM-topic sentiment matrix, calculate the expected
value $E(T,p)$ for each cell in the matrix, and then calculate the deviation
of each sentiment value from that expected value (i.e., residuals) (Sharpe,
2015). If this deviation is positive, it means that the sentiment is higher
than expected. The expectation and residual can be calculated as follows:

(5) $\displaystyle E(T,p)$
$\displaystyle=\sum_{T}{\sum_{p}{S_{T,p}}}\times\frac{\sum_{p}{S_{T,p}}}{\sum_{T}{\sum_{p}{S_{T,p}}}}\times\frac{\sum_{T}{S_{T,p}}}{\sum_{T}{\sum_{p}{S_{T,p}}}},$
(6) $\displaystyle Residual_{T,p}$
$\displaystyle=(S_{T,p}-E_{T,p})/\sqrt{\smash[b]{E_{T,p}}},$

where $S_{T,p}$ means the average of tweets’ sentiment for a topic $T$ and a
PM $p$.

Figure 3 is a heatmap visualizing the residuals, which shows that sentiment
value varies by topic. First, we could observe the overall trend in the
figure: in the last four PMs, the sentiment was basically high on Personnel
and low on Economy. It makes sense that cabinet personnel is attracting higher
sentiments since it has been said that the attitude of Japanese people toward
politics weighs on “personality rather than policy” (Curtis et al., 2004;
Miyazaki et al., 2021), meaning that who is in power is important to the
Japanese people. Also, as for economic topics, the outlook of Japanese people
for the future economy is known to be highly pessimistic, especially for the
next generation’s economy, with 76% of respondents saying it will get worse
off, which was the worst among the 27 countries surveyed by the Pew Research
Center (Center, 2018).

Figure 3.  Average sentiments of the tweets with respect to each PM for each
topic. The color of each cell corresponds to the value of residual (eq 6); the
redder the color, the higher the sentiment. We clipped the values above an
absolute value of 0.06 for visibility.

Looking at the comparison of PMs in Figure 3, we could confirm the differences
in sentiments by topics. For example, Kishida was said to be expected of high
diplomatic skills (Shinbun, 2021), which corresponds to the result that there
is positive sentiment for him. Conversely, Kishida has been criticized for the
cash handouts for COVID-19 that he proposed at the beginning of his term
(Press, 2021), indicating that there are low expectations for the economy. In
addition, Suga was known to have been favorably received about the appointment
of cabinet ministers (Lies, 2021), which corresponds to his higher sentiment
score toward Personnel. These results suggest an alignment between the actual
political situation and the initial public expectations on social media, which
would indicate the topics that should be anticipated first at the timing of
the PM’s inauguration.

##  6\. RQ3: Who are changing the sentiments toward the prime ministers?

For the administrative party, one of the intentions of the change of PM is to
aim to calm users with negative feelings toward the PM. However, the current
hearing survey system for PM’s approval rating does not track individual
users, making it difficult to determine whether people with negative sentiment
have decreased or people with positive sentiment have increased. Therefore, in
this section, we conduct an analysis to capture changes in the sentiment at a
user level by tracking individual users before and after the change of PMs.

Here, we examine whether there is an overlap in the user base of the previous
and new PMs’ tweets. Specifically, we first collected tweets for each change
timing for two periods: 60 days before the resignation of the former PM
(Before) and 60 days after the inauguration of the new PM (After). Then, we
classified the authors of the tweets of each change timing into four groups:
Prev, users who tweeted only about the previous PM; New, users who tweeted
only about the new PM; Both, users who tweeted about both the previous and the
new PM; None, users who did not tweet about either the previous and the new
PM. Figure 4 shows the transition of these users Before and After.

We found the two types of users dominate the majority of users in each change
timing: Prev$\rightarrow$None, i.e., users who tweeted about the previous PM
in Before but did not tweet about either the previous PM or the New PM in
After; None$\rightarrow$New, i.e., users who did not tweet about either the PM
in Before but tweeted about only the New PM in After. To measure the presence
of these users, we computed the following two indices:

$\displaystyle F_{out}$
$\displaystyle=100\times\frac{|\text{Before\\_Prev}\cap\text{After\\_None}|}{|\text{Before\\_Prev}|}$
$\displaystyle F_{in}$
$\displaystyle=100\times\frac{|\text{Before\\_None}\cap\text{After\\_New}|}{|\text{After\\_New}|}$

where A_B is the user group $B$ in the period $A$. $F_{out}$ indicates the
percentage of users who stopped tweeting about either PM among those who
tweeted about only the previous PM. $F_{in}$ is the percentage of users who
did not tweet about either PM among those who newly tweeted about only New PM.
These two indicators are annotated in Figure 4.

The figure shows that $F_{out}$ is mostly above 50% and $F_{in}$ is all above
65%, indicating that the majority of users who mention the previous and new
PMs are being replaced before and after a change timing of PMs. In other
words, when there is a change in sentiment before and after a change timing of
PMs, it is more likely that users with different perceptions have replaced the
whole opinion rather than a change in the same individual’s state of mind.

Figure 4. Sankey diagrams indicating user group transitions at each change
timing. At each Sankey diagram, each rectangle represents the size of the user
base mentioning the prev/new PM. The left side shows the users before the
resignation of the previous PM and the right side shows the users after the
inauguration of the new PM. “Both” indicates users tweeting about both PMs,
“Prev” indicates users tweeting about the previous PM, “New” indicates users
tweeting about the new PM, and “None” indicates users not tweeting about
either PM. The line connecting the rectangles shows the transition of the user
base from left to right. $F_{out}$ indicates the percentage of users tweeting
neither PM among users who tweeted about the previous PM before his
resignation, and $F_{in}$ indicates the percentage of users who newly started
tweeting about the new PM among those who did not tweet about either PM before
the inauguration.

We also compare the sentiment among groups in user transition. We chose the
groups for the comparison as the Prev$\rightarrow$None and
None$\rightarrow$New groups, which are in the majority of the user
transitions. In addition, we compare the sentiments of the same PM as New PM
and as Prev PM, i.e., we compare the None$\rightarrow$New groups and
Prev$\rightarrow$None for the same PMs. Figure 5 shows the result of the
comparison.

First, comparing Prev$\rightarrow$None and None$\rightarrow$New at the same
change timing, we find that there was a significant difference in sentiment in
seven out of eight times. This means that the majority of tweets are
substituted by a group with different sentiments. In five of those timings,
the None$\rightarrow$New group had higher sentiment, and in 2 timings, the
None$\rightarrow$New group had lower sentiment. This was in almost perfect
agreement with the timing at which the jump in Figure 2 was confirmed. In
other words, the change in sentiment that occurred around the time of change
timings was confirmed to be largely due to the substitution of these groups of
tweets.

Furthermore, a comparison of sentiments about the same PM at different timings
shows that there was a significant difference in five out of eight cases.
Moreover, four of the eight times, Prev$\rightarrow$None sentiment was
significantly lower, meaning that the PM’s sentiment that was high when he
took office was lower just before he resigned. This could be a case of the
honeymoon effect being confirmed in another way.

Here, we should note that only Abe exhibits extremely low sentiment. Abe has
apparently lower sentiment than other PMs at the end of his first
administration, as well as at the beginning and end of his second
administration. It seems that the low sentiment at the end of the first
administration carried over into the second administration. While a PM’s name
recognition often jumps when they become PM, Abe’s name recognition was
already strong at the beginning of his second administration. This result may
explain why the RDD results in Figure 2 indicate a drop in sentiment only when
Abe started his administration.

Figure 5.  Boxplot of sentiment for groups of user transitions at each change
timing. The sentiment of the groups of Prev$\rightarrow$None and
None$\rightarrow$New are compared in each change timing. Also, the sentiments
for the same PM in different change timings are also compared. The number of
brackets indicates the result of the Mann-Whitney U test: 3: p ¡ 0.001, 2: p ¡
0.01, 1: p ¡ 0.05. The direction of the brackets is greater for the direction
of the open side of the brackets. Visualization by (Charlier et al., 2022).

Finally, we compare the same PM at the same change timing. Here, we compare
Prev$\rightarrow$None and None$\rightarrow$Prev for the same PM. The PMs
resign as a form of taking responsibility for their low approval ratings, and
we test whether this eases the harsh atmosphere against them. Table 3 shows
the results of the comparison. We got the result that three out of eight
times, the sentiment of None$\rightarrow$Prev is significantly higher than
that of Prev$\rightarrow$None (Hatoyama, Abe_2, and Suga). Conversely, on one
timing, None$\rightarrow$Prev was lower (Kan). This indicates that there is a
tendency for some users to emerge to appreciate a previous PM’s labor,
although not always.

Prev PM | Prev=>None | None=>Prev | Significance
---|---|---|---
Abe_1 | 0.561 | 0.609 | -
Fukuda | 0.647 | 0.628 | -
Aso | 0.699 | 0.714 | -
Hatoyama | 0.706 | 0.710 | <<<
Kan | 0.685 | 0.669 | >>>
Noda | 0.697 | 0.703 | -
Abe_2 | 0.540 | 0.626 | <<<
Suga | 0.627 | 0.711 | <<<
Table 3.  The sentiments for the previous PMs before and after their change
timing and the result of comparison. The meaning of brackets is the same as
Table 2.

##  7\. Discussion and Conclusion

###  7.1. Main Findings

In this study, we attempted to explore and characterize the honeymoon effect
on social media by analyzing Japanese Twitter users’ reactions to eight
changes in the PM in Japan. The results showed that in the RDD for the
sentiment of the previous and new PMs before and after their change (RQ1), we
confirmed a significant jump in sentiment in four out of eight times, with one
timing when sentiment significantly declined and no significant results in the
remaining three times. The results suggest that there is an overall tendency
to have the honeymoon effect on social media, although not in all cases.

In the analysis of sentiment by topics (RQ2), the sentiment was often
significantly lower for PM-specific topics than for common topics to all PMs
(4 out of 8 times). Comparisons among PMs using topics common to all PMs
showed that topics with positive sentiment differed by the PM, demonstrating
the effectiveness of social media in understanding the relative position of
each PM and the issues expected of each PM.

Finally, the user-based analysis (RQ3) revealed that the majority of users
were replaced between those who tweeted about the previous PM before the
resignation and those who tweeted about the new PM after the inauguration.
Furthermore, the sentiments of these replaced user bases were significantly
different in many cases (7 out of 8 times), indicating that the user groups
which the administration should appeal to on social media may differ depending
on whether it is immediately after the inauguration or when the approval
rating is declining. In addition, the sentiment of users who tweeted about the
same PM after the inauguration and before the resignation was significantly
different (5 out of 8 times), and in many cases, the sentiment was lower
before the resignation (4 out of 5 times), confirming the honeymoon effect in
a different way.

###  7.2. Implications for practitioners

The honeymoon effect was observed in social media at the PM change timings,
although not always, suggesting that if the honeymoon effect emerges, social
media could be used as a gauge of public response to policies that are
implemented during the honeymoon period. For example, the pressure Suga put on
academia soon after taking office received the lowest sentiment, which is
claimed to have actually influenced the unnecessary drop in his approval
rating (Shimbun, 2020). We confirmed that social media had captured this
impact, and considering that the typical approval surveys are conducted on a
monthly basis, monitoring social media may have enabled a quicker reaction to
the drop in sentiment.

When a country’s leadership changes, the new PM must draw lessons from the
cases of the previous leaders in managing the government, but each politician
also has unique characteristics and expectations, making it challenging to
apply the lessons. It could be easier to communicate with the public if we
could identify the features that are popular for each PM, as we have just
confirmed. For example, sentiment is particularly higher about cabinet
appointments for Suga, while Kishida has positive sentiment on foreign
affairs. It would be important to recognize the strengths of each PM and take
them into account in their communication strategies.

PMs have greater control over whom they interact with on social media than in
traditional media. In this regard, it would be important to determine whether
users are continuously vocal or one-time users in terms of different change
timings and PMs. In fact, as we confirmed, users who only tweeted about a new
PM, not a prev PM, tend to generate higher sentiment right after the
inauguration of the new PM. Therefore, an essential tactic for enhancing
general attitude is likely to be how to involve these one-time posters in
communication.

###  7.3. Limitation and future work

RDD setting and more elaborate model: In conducting the RDD, we used
sentiments toward the different PMs before and after the threshold. We made
this decision in order to be consistent with traditional surveys on approval
ratings, but we could not fully take into account the impact on the potential
impact of individual PM on sentiment. Modeling the potential impact on the
PM’s personal sentiment is challenging; for example, using sentiment prior to
the PM’s inauguration would result in a different nature of sentiment because
a PM’s name recognition surges only after becoming PM in many cases. However,
the result of this study showed that only Abe, who was appointed to PM twice,
received anomaly sentiments; thus, we assumed the same model could basically
be applied to figures who served as PM for the first time. Nevertheless, there
is still room for improvement in the modeling of the elaboration of the impact
of each individual PM, including the change of administrative party (LDP and
DPJ), for example. In addition, we did not explicitly incorporate exogenous
influencing factors into our model (e.g., the 2007 financial crisis). In
comparing the previous and new PMs, the impact of external factors is likely
to be small because the collection timing of sentiments for two PMs is close.
However, external factors would affect comparisons between the change timings
regarding the extent of the honeymoon effect.

Consideration of user scale: The second Abe administration lasted eight years,
during which time the number of Twitter users increased dramatically, which
might affect the behavior of sentiment at an aggregated level. In fact, for
the two PMs after Abe (i.e., Suga and Kishida), the behavior of sentiment
toward them was specifically similar, including RDD results; thus, it is
reasonable to anticipate that the analysis of PMs will be continued in the
future in order to confirm this tendency with a sufficiently large number of
users. On the other hand, in the early days of the service (e.g., 2007),
Twitter was not as popular as it is today, and as this study showed, there
were fewer users who mentioned older PMs; thus, the results were likely to be
less robust about the older the PMs. Since the number of previous tweets
cannot be increased, it becomes even more crucial to carry out comparable
experiments in the future to verify the validity of the honeymoon effect on
social media.

Other countries and domains: The honeymoon effect in actual approval ratings
have also been observed in other countries besides Japan; thus, the analysis
of other countries (e.g., the United Kingdom, which has a similar
parliamentary system) is also expected to be conducted. Politics is one of the
main topics on Twitterverse worldwide (Huszár et al., 2022); thus, if
democracy works and people’s will is effectively reflected in the selection of
the national leader, the same observation as this study would be anticipated
even in other countries. Also, in addition to politics, companies have also
used executive resignations to recover from scandals and business downturns;
thus, it would be interesting to analyze the effect of such actions, including
the linkage with stock prices.

Stance detection: Sentiment analysis is used in this study to examine the
honeymoon effect. An alternative approach would be stance detection. Since
stance and sentiment are known to differ from one another (AlDayel and Magdy,
2021), undertaking stance analysis is also useful for gaining a more
multifaceted understanding of the phenomena.

The end of the honeymoon period: We focused on the honeymoon effect and
analyzed it at the timing of PM changes. On the other hand, it would also be
important to analyze the timing of the end of the honeymoon period, i.e., when
the high approval ratings of PMs start to fall.

Topic selection and classification: In this study, we propose a data-
processing flow to classify unique topics and common topics for PMs. However,
this flow requires manual adjustment at the end, and it would be desirable to
be able to efficiently extract unique/common topics using a more sophisticated
method.

Aspect-based sentiment analysis: We measured the sentiments for topics by
calculating the average sentiments of tweets of a topic. An alternative
approach would be to use aspect-based sentiment analysis (Pontiki et al.,
2016), which trains the sentiments of texts with certain aspects.

Potential missing tweets: We retrieved the tweets in November 2022 via the
Twitter API. Thus, we could not obtain tweets that were deleted (Bhattacharya
and Ganguly, 2016). The analysis of recent PM changes may be more robust,
considering that older tweets are more likely to be erased and that the volume
of tweets about more recent PMs is larger.

###  7.4. Ethical Considerations

We pay the utmost attention to the privacy of individuals in this study. We
did not include personal names except for PM or account names in our analysis.
We examined public tweets only. When sharing our tweet data, we will publish
only a list of tweet IDs according to Twitter’s guidelines.

## Appendices

As mentioned in section 5.1, we obtained 23 topics for eight new PMs, then we
aggregated them using a hierarchical clustering method, the dendrogram of
which is shown in Figure 6. We manually determined the threshold for
aggregation by looking at the dendrogram and also the contents of resulting
clusters (Table 4). As a result, we obtained four clusters, and we labeled
them as Diplomacy, Economy, Cabinet personnel, and Diet administration by
examining the representative words of the topics that comprise the cluster.

We found Diplomacy as the topic on the United States, China, South Korea, and
North Korea, with which Japan has close relations. For Economy, we observed
the topic of budgets, taxes, exchange rates, and wages. Cabinet personnel
include words for bureaucrats and ministers as well as some individual names,
while Diet administration consists of party names, approval ratings, press
conferences, and speeches. As for the tweet volume, Diplomacy and Diet
administration are both the most popular topics, followed by Economy, and
finally Cabinet personnel.

Figure 6.  Dendrogram of hierarchical clustering of common topics. The
distance between topics is the Hellinger distance for each topic’s
representative word calculated using the probability of belonging to all
topics. The y-axis is the distance between clusters when they are combined
using Ward’s method. We set the threshold to 1 based on the shape of the
dendrogram and the resulting cluster contents. The x-axis contains the ids of
each topic, the contents of which can be found in Table 4.

Merged topic | Topic id |  Top words | Tweet num | Total
---|---|---|---|---
Diplomacy | 24 |  Korea, Japan, Japan-Korea, President, summit meeting, teleconference | 111,824 | 505,899
33 |  Sign, North Korea, abduction, release, kidnapping | 50,269
41 |  summit meeting, China, visit, diplomacy, minister | 90,324
45 |  worship, Yasukuni shrine, Yasukuni, Yasukuni visit, China | 38,771
52 |  Okinawa, Futenma, relocation, governor, meeting | 59,881
54 |  China, diplomacy, Senkaku, U.S., Biden | 123,616
70 |  speech, announcement, UN, reduction, summit | 31,214
Economy | 22 |  bureaucrats, finance ministry, people, tax hike, Ozawa | 94,702 | 481,475
25 |  enterprise, wage increase, request, economy, professor | 30,518
32 |  tax hike, consumption tax, income, finance, taxation | 67,136
43 |  budget, government, direction, politics, meeting | 72,676
61 |  people, policy, bureaucracy, tax hike, LDP | 68,554
66 |  yen weakening, economy, policy, stock price, expectation | 98,173
72 |  economy, recovery, measures, employment, challenges | 49,716
Cabinet personnel | 12 |  bureaucrats, personnel, appointment, LDP, charge | 46,961 | 170,082
30 |  inauguration, cabinet ministers, Tokyo, economy, press conference | 80,378
38 |  minister, Renho, cabinet minister, minister in charge, allegations | 42,743
Diet administration | 3 |  policy speech, speech, concept, LDP, Osaka | 50,677 | 532,439
4 |  LDP, people, debate, opposition party, diet | 66,684
23 |  approval rating, support, poll, polls, survey, cabinet approval rating | 146,087
29 |  Diet, opposition parties, questions from representatives, policy speeches, extraordinary diet session | 115,450
53 |  question, press, pressure, incident, problem | 43,924
73 |  people, language, politics, society, unhappiness | 109,617

Table 4.  Common topics that are aggregated by clustering. “Merged topic”
indicates the name of the cluster as labeled by the authors. “Topic id” is a
random id assigned to a topic by BTM. “Top words” are the five most possible
words belonging to each topic. The number of tweets for each topic and cluster
is also shown.

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