SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 4(2); September (2023), pp. 98–105
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

A Study of Human Emotion on the Virtual Crocodile with an
Artificial Life span of 100days
Taiki Sugimoto, Jun Nakamura

Faculty of Global Management, Chuo University, Higashinakano Hachioji-shi, 192-0393/Tokyo, Japan.

ARTICLE INFO
Article History
Received 22 November 2022
Accepted 06 November 2023
Keywords
Sentiment analysis
Key graph analysis
ML-AskSNS
Flaming
Artificial Life

ABSTRACTThis study is aimed at capturing the impact of flaming generated through social media on content
by Emotion analysis and Key Graph Analysis. The analysis and discussion clarify those threepoints. First, humans want to mourn the death of comic strip character, as an Artificial Life.
Second, Reply tends to be relatively positive throughout the entire flaming period, thus they do
not fully reflect the feelings of the recipients. Third, content that has been under flaming will notstop being criticized for a long period of time, and that being flaming experience itself will be
consumed as content.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/)

unspecified number of people in a short period becauseof the promoted. In this respect, it is worth noting that it
is rare for content that was both trending and flamed not
to lead to successful sales.
An important part of this paper is the connection between
Artificial Life and the outpouring of strong emotions thatyield flaming. This paper deals with the death of acrocodile, as an Artificial Life that lasts 100 days in theordinary life, and the human reaction to the event. In
other words, through the events of this content, this paper
is to consider not only the impact of the flaming on thecontent, but also how humans deal with the death of thecrocodile, which is just a comic strip character as an
Artificial Life, and how they wish to be treated.

Introduction
This paper studies a comic strip of a crocodile that lived
for 100 days, as an Artificial Life. The subject matter
addressed in the paper is a comic strip posted on Twitter
entitled "A Crocodile Who Will Die in 100 Days. It was
updated daily on Twitter i by comic artist Yuki Kikuchi
on Twitter for 100 days, from December 12, 2019, to
March 20, 2020.
"A Crocodile Who Will Die in 100 Days" was promoted
for commercial deployment shortly after it recorded thehighest number of "likes" on Twitter in Japan's history.
Therefore, both the content and the product were widely
recognized, and it can be said to have succeeded in
attracting the attention of the AISAS model ii of Internet
consumption behavior. However, the trending that briefly
attracted favorable attention from an unspecified numberof people turned overnight and got under the flame, inwhich attacks and criticisms flooded in from an

Previous ResearchThis thesis focuses on trending and flaming on social
networking services (SNS). This chapter describes the

A social networking service launched by Obvious (Twitter, Inc.) in
July 2006 and used by 217 million users per day as of April 2021 [1].
Proposed by Dentsu in 2004. The acronym stands for Attention,
Interest, Search, Action, Share, and is used to understand consumers'

attitudes and behavior in order to develop marketing strategies
appropriate for each stage.

i

ii

Corresponding author’s E-mail: a19.3gem@g.chuo-u.ac.jp, jyulis.77f@g.chuo-u.ac.jp URL: https://www.chuo-u.ac.jp

98Emotion analysis and Key Graph Analysis are conductedon the Tweets and Replies.

positioning of this thesis regarding previous research on
flaming on the Internet in an SNS.
Concerning flaming, defined flaming as "a requirementthat a large amount of criticism is written on CGMs suchas social media in a short period of time and that thecriticism spread to multiple online services rather than
only to a single online service" [2], "A Crocodile Who
Will Die in 100 Days" which meets the above definition
because the criticism spread across various media,
including Twitter, in a short period after the promotion
for commercial deployment.
The previous research also addressed actual flame cases[2], this paper, similarly, addresses a real flame case.
Approaches to flames have been attempted bycategorizing Tweets and aggregating them in time series.
This paper differs from the previous study in that it
focuses on a single case of "A Crocodile Who Will Die
in 100 Days" and uses Emotion analysis and Key Graph
Analysis to study the flames. The research on thepresence of others on SNS has indicated the desire to
examine the factors that cause flaming by content and to
elucidate the processes that lead to flaming [3]. Since thispaper quantitatively analyzes and examines the flamingof content, this paper is academically and sociallysignificant research that also contributes to theclarification of the process.

Emotion analysis
Emotion analysis analyzes the Tweet data and Reply data
for each time series, and quantitatively quantifies theemotional fluctuations seen in the user's transmissions.
Emotion analysis using ML-Ask is an Emotion analysis
method that focuses on a wider range of emotions.
Emotions are classified into 10 categories using the
Emotional Expression Dictionary: “sadness”, “shame”,“anger”, “dislike”, “fear”, “surprise”, “like”,“excitement”, “peace”, and “joy” [4].
Key Graph analysis
Key Graph Analysis analyzes the same Tweet data andReply data for the same period as the Emotion analysis,
to objectively visualize the information and impressions
that users were transmitting.
For the analysis, the Dice coefficients based on equation(1).
2|𝐴𝐴∩𝐵𝐵|
𝐷𝐷𝐷𝐷𝐷𝐷(𝐴𝐴, 𝐵𝐵)＝ |𝐴𝐴|+|𝐵𝐵|(1)
Results
This chapter shows Increase/ decrease of collected data,
Emotion analysis results, Key Graph analysis results.

PurposeThe purpose of this paper is to clarify the impact offlaming on content. This paper shows how flaming haveaffected peoples' emotions.

Increase/ decrease of collected data
The Key Graph analysis and Emotion analysis in thispaper focuses on the months of before the film's release(April 1, 2020) to around the end date of the film release(August 8, 2021).
However, to get an overall picture of this content at the
beginning, here are the monthly changes in Tweet and
Reply data from July 1, 2019, to July 25, 2022, fivemonths before this content was serialized.

Methods
This chapter describes the data, Emotion, and Key Graph
Analysis used in this thesis.
This paper collected a total of 169,307 Tweets and18,180 Replies. Tweets are including either or both theofficial name of the subject, "100 日後に死ぬワニ," theabbreviation of the subject, "100 ワニ," and the author of
the subject, "きくちゆうき." Replies are collection bysent to the author, Yuki Kikuchi (@yuukikikuchi), 100days to die crocodile official (@100waniOfficial). Tweet
data and Reply data were both collected from two periods,
based on the events and Google trends. before the film's
release (April 1, 2020) to (June 30, 2021) and Fromaround the start date of the film release (July 1, 2021) toaround the end date of the film release (August 8, 2021).

99

Fig. 1: Increase/ decrease of Tweet data (July 1, 2019 July 25, 2022)

Fig. 3: Tweet Emotion analysis (April 1, 2020 - June 30,
2021, and July 1, 2021 - August 8, 2021)
Fig. 2: Increase/ decrease of Reply data (July 1, 2019 July 25, 2022)
Emotion analysis results
Using the collected data, Emotion analysis was
conducted for each period, and the percentage of each
Emotion was calculated for each period. This was done
to make it easier to compare the transition, since thenumber of Tweets and Replies are differed from period
to period. Fig.3, Fig.4 are below show a graphicalsummary of the percentage transition of each estimatedemotion.
Fig. 4: Reply Emotion analysis (April 1, 2020 - June 30,
2021, and July 1, 2021 - August 8, 2021)
Key Graph analysis results
In this chapter, the clusters that can be read from the Key
Graph Analysis results are indicated by numbers in eachfigure to clarify what kind of context was present in the
Tweets and Replies.

100Fig. 5: Tweet data Period Ⅰ (April 1, 2020 - June 30, 2021)(ⅰ)
Information and reaction about the film of this content.
(ⅱ)
Negative Tweets about this content book.
(ⅲ)
Ridicule this content. E.g., compared film of this content with other popular work box-office (“Demon Slayer”(Koyoharu Gotouge)). The café of this content that closed within three days of its opening. The crane game of goods was
priced at ¥23 per game. The problem of shill in live distribution, etc.
(ⅳ)
Reactions and ridicule a lawsuit filed by the author of this content in response to a defamation statement.
(ⅴ)
Information and reaction to this content being hot topic about.

Fig. 6: Tweet data Period Ⅱ (July 1, 2021 - August 8, 2021)

101

(ⅰ)
Reaction to the flaming by the alleged relationship between this content and Dentsu.
(ⅱ)
Information and reaction to the fact that the film for this content has a running time of 60 minutes and the priceof the film ticket is ¥1,900.
(ⅲ)
Tweets about trolling behavior, such as rampant low-rated reviews before the film's release, or reservations
without intent to see the film after its release.
(ⅳ)
Information and reactions about this content film.
(ⅴ)
Positive comments on the film of this content.
(ⅵ)
Information and ridicule of the box-office revenue for the film in this content.

Fig. 7: Reply data Period Ⅰ (April 1, 2020 - June 30, 2021)(ⅰ)
Replies to the support and cheer up this content author.
(ⅱ)
Favorable reactions to the commercial development of this content (book adaptations, film adaptations, etc.) andwork by this content author.
(ⅲ)
Replies about new work by this content author.
(ⅳ)
Reaction to the flaming by the alleged relationship between this content and Dentsu.

102

Fig. 8: Reply data Period Ⅱ (July 1, 2021 - August 8, 2021)(ⅰ)
Criticism of the commercial development of this content. The appearance of ridicule of the film adaptation of thisContent as a failure.
(ⅱ)
Favorable comments on the author's solo exhibition of this content.
(ⅲ)
Replies that support and cheer up the author of this content.
(ⅳ)
Reactions to a lawsuit filed by the author of this content in response to a defamation statement.
(ⅴ)
Reactions to the collaboration goods of this content and Jagarico (Calbee Co., Ltd.).

to reflect on its death, which was one of the factors that
contributed to the flaming of the content.

Discussion
In the following sections, combining the data obtained
from the results of the Emotion analysis and the resultsof the Key Graph Analysis, this section discusses theemotional swings in each period.

Comparison of Period I and Period II
The Tweet data shows a slight increase in "dislike" and"anger" and a decrease in "like" and "joy" (Fig. 3). These
factors can be read from the clusters of trolling andcriticism generated against this content film, and theslight decrease but some prominence of "sadness" (Fig.
5, Fig. 6) may also be a factor. As for the increase in"fear" and "excitement," judging from the clusters (Fig.
6), this is most likely due to people worry about the film
adaptation.
In the Reply data, "sadness" increased and "dislike"slightly increased, while "like" and "joy" decreased.
Clusters of criticism of these commercial developments,
including film adaptations, and reactions to the trial weregenerated (Fig. 8), which can be interpreted as the releaseof the film and the accompanying trolling that hasbrought the content back into the topic, as well as thenegative replies. The slight increase in "excitement" is

One of the factors in the outbreak of the flaming
To begin with, let us describe what caused the flaming by
the death of the crocodile, as an Artificial Life, isdescribed below.
From the results of the Emotion analysis results (Fig. 3),
the large percentage of "dislike" Tweets can be clearly
seen. From the clusters of the Key Graph analysis results(Fig. 5, Fig. 6). The cluster shows that one factor thatcaused this content flaming was the treatment of thecrocodile's death.
For readers who have followed the death of the crocodile,as an Artificial Life that spends its daily life as they do,
many of people were disgusted by the extensive
commercial advertisement of the crocodile without time103

shown in the Increase/ decrease of collected data (Fig. 1),
Emotion analysis results (Fig. 3), and Key Graph analysis
results (Fig. 5, Fig. 6). The flaming period was
unremarkable since it was seen as an issue to be discussed,
but since then it has become content as "flaming content,"
and there is a segment of the population that gleefully
watches court cases brought against harassment andslander by the authors. Here, ridicule and sarcasm arealso included, in our view. Although the content haschanged in nature and has not led to sales due to its
topicality, it is also clear that the number of Tweets has
increased with the release of the movie (Fig. 1).

attributed to reactions to author’s exhibitions, judging
from the clusters (Fig. 8).
Comparison of Tweet data and Reply data
In the comparison of emotions between Tweet data andReply data, the ratio of positive emotions such as "like"and "excitement" was high in the Reply data, while theratio of negative emotions such as "dislike" and "anger"
was low. This is thought to be due to the difference in
Tweets and replies. This can be explained by the fact that
replies, unlike Tweets, are issued to communicate to theauthor and are therefore more likely to contain positive
content. This is supported by the fact that clusters of
support and clusters of positive responses to the author’s
exhibition were actually generated (Fig. 7, Fig. 8).
In addition, the percentage of "joy" in both the Tweet and
Reply data is large and remains high in the overall data.
However, from the results of the Key Graph analysis that
there are different reasons for this in both data, at least in
Period I. In both the Tweet data for Period I and the Reply
data for Period I, nearly 30% of both data are perceived
as "joy" (Fig. 3, Fig. 4). In the Reply data, there are someclusters that show reactions about the flaming, butclusters that are expected to have positive feelings, suchas support for the author, favorable reactions tocommercial development, and reactions to the author's
new content, stand out. However, no clusters withexpected positive Emotion were found in the tweet data,and several clusters were noticeably derisive of thiscontent. In response to this result, an examination of theoriginal data from which the classification was madesuggests that the cause lies in the limitations of the
Emotion analysis. Emotion analysis estimates emotion
by counting each emotional expression from the words
that make up the text based on the words in the Dictionaryof Emotional Expressions. Therefore, Emotion analysis
cannot correctly estimate texts in which sarcasm or
euphemisms are used. For example, if a sarcastic text inwhich the word "enjoyment" is used is read, it will becounted as "joy" based on the word fit. An actual reviewof the classified data revealed that most of the Tweets
classified as "joy" in Period I were negative Tweets.
Compared to replies, Tweets are more likely to use
euphemisms than replies because they are not uttered tocommunicate to the recipient, making this phenomenon
more likely to occur.
In addition, the reason for the high level of “joy” in the
Tweet data may be due to the content of flaming, as

Increase/ decrease of collected data
In Period II, there are clusters of criticism and ridicule ofthe commercial development of this content, but even so,
positive content such as support for the author, positive
reactions to the commercial development, and positive
reactions to the author's new content and author’s
exhibitions are prominent in the replies. However, as
shown in Fig. 2, the total number of replies has decreased
significantly compared to the start and duration ofserialization. Not only did the author lose the fans he wasable to gain, but he also received an average of 52.5
replies per month for the five months prior to the postingof this content (July 1, 2019, to November 1, 2019),
whereas the average number of replies from September
2021 to June 2022 was 33.2 per month, which indicatesthat the It is possible that existing fans may have left due
to the impact of this content posting. In contrast, thenumber of Tweets still exceeded 2,000 during the same
period, as shown in Fig. 1. In addition, the number ofTweets exceeded the number of replies throughout theentire period, indicating the influence of Tweets. The
power of Tweets to transmit not only buzz and flaming
but also to influence the image and brand of the subjectof attention.
Future researchThis paper does not fully investigate the bias of the users.
In the case of a specific flaming, it is possible that the
noisy minority phenomenon is being caused [5]. Thisphenomenon is the diffusion of information only by users
belonging to a specific community.
Therefore, it is necessary to quantitatively evaluate
whether the information spread at the time of the flaming
incident is biased toward users in a certain community by104

investigating user bias using KL Divergence, as in theprevious study.

Media.” Transactions of the Japanese Society for
Artificial Intelligence, 35(4).

Conclusions
The purpose of this paper is to visualize “A Crocodile
Who Will Die in 100 Days,” flaming, which is a flaming
period, and to understand how the flaming has affected
peoples' emotions.
The following three points were considered in our
analysis of the impact of trending and flaming on content.
First, humans want to mourn the death of an Artificial
Life (a mere comic strip character) with whom theyshared a universal daily life in the same days.
Second, during a flaming period on social media, there isa large gap between the tendency of comments directly
directed from recipients to senders and the tendency ofcomments shared for a whole user. Comments directed
directly to the sender tend to be relatively positive
throughout the entire flaming period, thus they do not
fully reflect the feelings of the recipients.
Third, even if the content has experienced trending, it
will continue to receive criticism for a long period, andthe fact that it has gone up in flames itself will be
consumed as content, thus there is a possibility that it will
not only lose fans, but also potential fans and consumers.

Authors Introduction
Mr. Taiki Sugimoto
He is currently a 4th year student, amember of the Jun Nakamura
Laboratory of Chuo University, Japan.
The areas of his research interest are
cognitive science and Artificial
Intelligence. In 2023, he will join to themaster’s degree course of Chuo
University.

Dr. Jun Nakamura

Acknowledgments
The data collection and Emotion analysis result in thispaper were collected with the help of Mr. Yuto
Kobayashi and Mr. Kazuki Nakamura of the Faculty of
Global Management, Chuo University. Thank you forproviding your time and talents. I would like to express
my gratitude to them.
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He is a professor at the Faculty of
Global Management, Chuo
University, Japan. Previously he wasworking as a professor at theGraduate School of Engineering
Management, Shibaura Institute ofTechnology. His main research areas
are cognitive science and humanities.