This document discusses a system called Asia Trend Map that forecasts the popularity of Japanese content such as anime, manga, and games in Asian countries over the next 6 months. The system collects data on these cultural products from Twitter, Wikipedia, and search engines in different Asian languages and uses this web data along with past Japanese sales data to train a model that can predict future trends. The results showed the system could improve its predictive accuracy by combining data sources and that Wikipedia data, especially page content attributes, was particularly helpful for predicting longer term trends.

1. Asia Trend Map: Forecasting “Cool Japan”
Content Popularity on Web Data
Shuhei Iitsuka
The University of Tokyo
Ohma Inc.
2013/08/20 1

2. Background
• Anime, Manga and Game has become popular around the world.
• Japanese content industries are willing to promote their products
overseas under the brand of “Cool Japan”.
• However, localization processes (translation, promoting etc.) take
costs a lot of money and time.
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Japan in London: Sushi, Manga, Cosplay and Camden – visitlondon.com
http://blog.visitlondon.com/2010/09/japan-in-london-sushi-manga-cosplay-and-camden/
à Sellers need to estimate the product’s popularity in the
target market and allocate their resources strategically.

3. Purpose
• Forecasting each product's popularity around Asian countries
based on web data from Twitter, Wikipedia and a search engine.
• Why Asia?
– Close to Japan geographically and culturally à direct economic effect
– Growing market
• Why web data?
– Unauthorized copies are widely distributed around the country
à There’s difficulty in catching the trend from the sales data
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?

4. Demonstration: Asia Trend Map
• This system can forecast about 4,000 Japanese content’s
popularity trends following 6 months for 13 countries in Asia.
2013/08/20 4

5. Model Overview
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Twitter
Wikipedia
search
engine
web
data
forecasting
model
consumer,
user
tweet
edit
search
crawl
crawl
crawl
attribute
extraction
training
data
(Sales
in
Japan)
SVR

6. Wikipedia Data Attributes
• Edit
– Monthly Edit Count, Monthly Unique Editor Count, Average Edit
Count Per User ...
• Link
– Number of Forward Links, Number of Backward Links ...
• Content
– Number of International Links, Page Size, Number of Sections ...
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jp.wikipedia.org
zh.wikipedia.org
ko.wikipedia.org
NARUTO
나루토
火影忍者
Jump
(Magazine)
Ramen
Forward
Link
Backward
Link
International
Link
Wikipedia
link
example:

7. month:
m
Twitter and Search Engine
• Twitter: Extract number of tweets which includes the product
name (monthly)
• Search Engine: Extract number of times the product name is
searched (monthly)
• We get each product’s local name utilizing Wikipedia database.
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NARUTO
Wikipedia
火影忍者
나루토
Twitter
Search
Engine
T_(m,
China)
T_(m,
Korea)
S_(m,
China)
S_(m,
Korea)

8. Pre-processing on Training Data
• Sales of Manga suddenly increases when new volume is out.
à We connect the peak with lines and make use of this as training
data.
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9. Experimental Results
• Prediction precision is improved by applying attributes of
multiple web services.
• Especially, Wikipedia data took an importance role in predicting
the trends in more distant future.
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10. Experimental Results
• Among the Wikipedia data attributes, Page Content (Number of
international links, Page size, etc.) took the most important role
in predicting the trend.
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11. Conclusion
• We built the forecasting system of Japanese cultural products
from web data
• We launched a website based on this system: Asia Trend Map
• We'd like to contribute to strategic planning process of "Cool
Japan" with this.
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