MMRCDISCUSSION PAPER SERIES
No. 293Social network analysis of word of mouth on the internet
Yufu KUWASHIMA
Faculty of Management, Toyo UniversityMarch 2010

東京大学ものづくり経営研究センター
Manufacturing Management Research Center (MMRC)
Discussion papers are in draft form distributed for purposes of comment and discussion. Contact the author forpermission when reproducing or citing any part of this paper. Copyright is held by the author.
http://merc.e.u-tokyo.ac.jp/mmrc/dp/index.html

Social network analysis of word of mouth on the internet
Yufu KUWASHIMA
Faculty of Management, Toyo University
E-mail: yufu@jd5.so-net.ne.jp

Abstract:
The purpose of this research is to analyze the diffusion of movie reviews on the web.
We analyze the network of Yahoo! Japan Movie Users using the social network
analysis and compare with the Rogers’ diffusion model. As Rogers suggested, we cansee large influence between the neighbor categories. However, the data alsosuggested that there are also some influences of innovators. The fact that innovators
have a large influence was not considered in Rogers’ model.

Keyword: diffusion model, social network analysis, word-of mouth

Today, a wide variety of evaluations of commodities and services are available on the Web, suchas those on personal blogs and online stores like Amazon.com. Many people refer to such web
pages for the user’ comments on experience products before they make decisions whether theyshould buy them or not. The motion picture industry is an example of a typical experience productmarket characterized by product-quality information asymmetry between firms and consumers(Eliashberg and Sawhney 1994; Nelson 1974). In distinguishing between sellers of high and low
quality product before consumption, consumers in such market look for credible information thatfirms can provide using signals, such as advertising expenditure (DiOrio 2001), warranties(Boulding and Kirmani 1993), and brand names (Wernerfelt 1988).
Consumers find judgments from both professional critics and amateur comments. Professional
critics commonly provide reviews and ratings (Boulding and Kirmani 1993; Kirmani and Rao2000). Although amateur consumers can obtain useful information from critics, they sometimes
have different feelings with professionals because their experiences and preferences are different( Chakravarty, Liu, and Mazumdar 2008; Holbrook 1999; Wanderer 1970). Therefore consumers
often search for amateurs’ opinion which are alike with theirs).
Liu (2006) collected the word of mouth of mouth site of Yahoo! Movie, and analyzed therelationship between the content (positive/negative evaluation) of the word of mouth and theamount of sales. As a result, there was no relationship between the content and the amount of sales;
however, the amount of word of mouth did have an influence on the sales.
When consumers refer to other consumers’ opinions, they will not equally consider every
reviewer’s opinion. As they can read other reviewers’ histories, they can find judgments from theirfavorite reviewers’ opinions. But in previous studies, it had not been considered how the reviewers
evaluate each other mutually. Hence, in this research, I took the relationship among reviewers intoconsideration. It is effective for movie marketing to investigate the effects of word of mouth fromthe social network perspective. So, I focused on the reviewers’ linkage using social network
analysis. In this research, I analyzed the network in the site of Yahoo! Japan movie.
When consumers refer to the reviews, of course, they can see only reviews which were written
before they see movies. Hence, the timing of reviews is also important. Rogers (2003) researchedwhen the consumer adopted the innovation. He classified consumers into five categories. The
people who belong to these categories follow normal curve. According to Rogers, first 2.5 %people who adopt a new innovation are called innovators. And the next 13.5% people are called
early adopters, following the next 34% as early majorities, next 34% are late majorities, last 16%
are laggards. Early adopters are also called opinion leaders. Opinion leaders tell about theinnovation to the people around them. This is called word of mouth. The people around earlyadopters, opinion leaders, become early majorities. And the people around early majorities becomelate majorities. Their characters are skeptical, and dare not adopt innovation immediately. Laggards
are stubborn, and are hardly influenced by other people. For example, most Japanese people carrymobile phones but laggards don’t want to buy until when they forced to.
In this study, I analyzed the relationship between the buying movies) and the network structure of
the consumer in the site of Yahoo! Japan movie, using the social network analysis and comparewith the Rogers’ diffusion model. behavior (watching ofThe network in the site of Yahoo! Japan movie
About the site
In this study, I took the data from this web site called Yahoo! Japan Movie. There is an American
version of this web site. Users write movie reviews and they grade them. But Japanese version hasan additional function called ‘fan list’. With this function, users can put their favorite users as fans.
For example, if I want to see a certain movie, I’ll watch the movie’s page of this site. I can read themovie reviews there. And if I find a good comment, I’ll go to his or her page to see his or her otherreviews. And if he or she and I share favorite movies, I’ll put him or her as my favorite user. Then Ibecome his or her fan. In this way, users construct networks, and I called it ‘fan network’. Fannetwork is a directed graph.
The whole fan network can be seen as figure 1(Number of nodes; 2406 Density; 0.002 Clustering
coefficient; 0.074 Average distance; 3.73). This whole network is too complex to be analyzed. So I
focus on fan networks of some movies. The left line is an aggregate of isolated points, which haveno fan, and they are not anybody’s fan.

Figure 1 whole fan network

MethodThe first movie I focused on is called ‘Love and Honor’ in English, ‘Bushi no ichibun’ in Japanese.
This is a Japanese movie. This movie was released on 2006/12/1 in Japan, and the DVD wasreleased on 2007/6/1. So I took the data from 2006/12/1 to 2007/5/31. Its box-office revenue wasabout 410billion yen, about 4.1billion dollars.
I will apply Rogers’ diffusion theory to this movie. First, I took out the people who wrote a reviewof this movie, and examined the relationship among them. On this site, the number of people whowrote reviews during this time is 915, and there are 1150 ties between them. Then I mapped the fan
network of this movie. Figure 2 shows the network of this movie.

Figure 2 Fan network of ‘Love and Honor’

I apply the diffusion theory to this network. I classified them in to five categories according tothe time they wrote reviews. The first 2.5 % users are innovators. They are 23 people, and plotted
as figure 3. They wrote reviews on the first day the movie released. As you see, there are no ties
between them.

Figure 3 Innovators of this movie

The next 13.5% users are early adopters. They wrote reviews by 2006/12/4. Figure 4 shows earlyadopters including innovators. As you see, the ties between users emerged. The total number ofinnovators and early adopters is 146, and there are 64 ties between them.

Figure 4 Early adopters

The next 34% users are early majorities. They wrote reviews by 2006/12/22. As you can see in
figure 5, the number of ties increased. The total number of innovators, early adopters and earlymajorities is 458, and there are 288 ties between them.

Figure5 Early majorities

I repeat the procedure to late majorities and laggards. The next 34% users are late majorities.
They wrote reviews by 2007/1/21.There are many more ties. The total number of innovators, earlyadopters, early majorities and late majorities is 769, and there are 778ties between them. And thelast 16% users are laggards. They wrote reviews by 2007/5/31.
Then, after plotting all the five categories, there is the whole fan network of this movie (figure2).
The total number is 915, and there are 1150 ties between them.Table1 shows the summary ofnetwork variables at each time.

Table1 Comparison at time series

density

Clustering

Averagecoefficientdistance0∞

Early adopter 0.0024

04.418

Early majority 0.0031

0.027

4.821

Late majority 0.0027

0.062

4.458

0.0028

0.056

4.251

0Innovator

Laggard

Next, I examined how users in each category influenced each other. Table 2 shows the summery.
For example, each innovator and each early adopter has 0.52 ties between them on average. Andeach early adopter and early majority has 0.8 ties between them, and so on.

Table2 Influence between categories on ‘Love and Honor’

Early EarlyLateInnovator

Laggardadopter majority majority

Innovator 00.52

0.52

000.4

0.8

0.4

0.27

0.37

0.72

0.26

0.78

0.55

Early
adopterEarlymajority
Latemajority
Laggard

0.51

The point to observe here is the influence between early adopters and early majorities, and earlymajorities and late majorities. Based on Rogers’ model, early adopters have the greatest influenceon early majorities, and early majorities have on late majorities. And this phenomenon can be seenin these figures. Those are the largest percentages in this table, that is, early adopters have the great
influence on early majorities and early majorities have on late majorities. I interpret that the data

support Rogers’ theory.
However, the data point to another possibility. Coming back to table 2, there is another point toobserve. Please take a look at the innovators’ figure. They show that innovators have a big
influence on early adopters and early majorities. I should note that these numbers are nonnegligible.
Rogers said that innovators are enthusiasts, they have not so much influence on others, but they
have some influence on others in this network.
Then, I repeat this procedure to other movies; ‘Pirates of Caribbean ~At World’s End’ and ‘Red
cliff Part Ⅰ’. ‘Pirates of Caribbean ~ At World’s End’ was released on 2007/5/25 in Japan, and theDVD was released on 2007/12/5. So I took data from 2007/5/25 to 2007/12/4. Its box-officerevenue was about 1050 billion yen, about 10.5billion dollars. Table 3 shows the summary of theinfluence between categories on this movie.

Table 3 Influence between categories on ‘Pirates of Caribbean ~ At World’s End’Innovato Early Earlyr

Late

Laggaradopte majority majorit dryInnovato
0.02

0.51

0.73

0.65

0.11

0.16

0.57

0.22

0.22

0.12

0.54

0.29

0.18

0.39

r
Early
adopter
Earlymajority
Latemajority
Laggard

0.42‘Red cliff Part I’ was released 2008/11/1 in Japan, and the DVD was released on 2009/3/1. So I
took data from 2008/11/1 to 2009/2/28. Its box-office revenue was about 500 billion yen, about5billion dollars. Table 4 shows the summary of the influence between categories on this movie.

Table4 Influence between categories on ‘Red cliff Part I’

Innovato Early Earlyr

Late

Laggaradopte majority majorit dryInnovato
0.02

0.51

1.15

0.71

0.1

0.15

0.57

0.27

0.18

0.24

0.45

0.21

0.1

0.29

r
Early
adopterEarlymajority
Latemajority
Laggard

0.32

In Table 3 and Table 4, I can find the same phenomenon as Table 1. I can see large influences
between early adopters and early majorities, and early majorities and late majorities; and I can alsofind large influence between innovators and other categories.

Conclusion and discussion
As Rogers’ suggested, I can see large influence between the neighbor categories. However, ourdata also suggested that there are also some influences of innovators. The innovator’s influence was
not considered in Rogers’ model. This is a new finding of this study.
Based on these findings, I would like to propose two implications. The first point is concernedwith modeling of diffusion of innovations. Rogers’ model is an aggregate model; he paid little
attention to each consumer. This is because when Rogers propose this model, they did not haveaccess to detailed data such as who bought, what, when, and where. But Social Network Analysis
enables us to sophisticate the model of diffusion. Social Network Analysis and of course thedevelopment of computers allow us to analyze the network of consumers and how they influenceeach other.
The second point is concerned with marketing activities. Opinion leaders are often focused inmarketing, but I had better pay attention not only to opinion leaders but also to innovators, that ismaniacs.

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