1. Conceptual Design
toward a Visualization System of
University’s Web Presence
Simple Analysis and System Development Using Twitter
MIHO FUNAMORI, NATIONAL INSTITUTE OF INFORMATICS
MASAO MORI, TOKYO INSTITUTE OF TECHNOLOGY
6. Social Perception can affect
the university business
It may affect:
Enrollment number
University Rankings
Funding
Research Collaboration and Funding
External Relations
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9. PHYSICAL
Established
Almost fixed
University-based
Macroscopic
Limited Reach
DIGITAL
Fluid
Changes frequently
Event-driven
Microscopic
Global Reach
Two different Reputations
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10. PHYSICAL
Printed References
Interviews
Questionnaire
DIGITAL
Go Internet!
How to survey
the two different Reputations
Much Work
Taking time
Expensive
… Still not representing the
whole picture
Moderate Work
Swift
Inexpensive
Not representing everything,
but showing digital aspects
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12. Visualization System of
University’s Web Presence (VUWP)
Personal Comments
Profile of Senders
(Twitter, Facebook,
Blog, etc.)
University’s Web Presence
(by several aspects,
real time, visual)
1. Sorting comments by its
content and profile of
sender
2. Extract university
characteristics by different
aspects
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14. Estimated User of VUWP
University Administration
Check the social perception towards the
university
Identify marketing points
General People and Prospective
Students
Check the university’s vividness
(Accountability Purpose)
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15. System Requirement:
INPUT
Input data
Personal comments
Profile of senders
Sent time of the comments, etc.
Comments mainly on the SNS
used
Twitter, Facebook, blogs
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16. System Requirement:
ANALYSIS
Social perception towards the
university is extracted:
Real-time
Quantitatively
People’s impression towards
university is:
Expressed qualitatively
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17. System Requirement:
OUTPUT
Output should be:
Visual
Easy-to-understand manner
Clear and simple
To let prospective students
choose university:
Function to compare universities
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18. UNIV RANKINGS
Works mostly for Top
Universities
Numerical measures
Updated once a year
and expensive
VUWP
Works also for middle
and lower-ranked
universities
Expresses social
perceptions
Real time and
inexpensive
Feature of VUWP
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20. Methodology of
Systematic Tweets Analysis
Use the 1000 newest tweets of
certain Japanese university
Term Frequency Analysis
Consider 1000 tweets as one document
and count the appearance of the words
Tem Specificity Analysis
Use TF-IDF method
Obtain relative specificity of words for
respective university
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21. TF-IDF method
Term Frequency – Inverse Document Frequency
Term Frequency
Document Frequency
TF-IDF value
(w: word, d: document, #: count)
𝑑𝑓𝑤 = # 𝑑 ∈ 𝐷 𝑤 appears in 𝑑}
𝑡𝑓𝑖𝑑𝑓𝑤,𝑑 = 𝑡𝑓𝑤,𝑑 × log(
#𝐷
𝑑𝑓𝑤
)
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𝑡𝑓𝑤,𝑑 = the number of 𝑤′
s appearance in 𝑑
Fix the set of Documents D
22. Preparation:
Morphological analysis
1. Removes emoticons, such as (^o^),
from the document.
2. Morphological analysis
Decomposes sentences into minimal
significant linguistic elements and classifies the
elements.
Picks up nouns, adjectives, and verbs which
are independent words
Independent words: Words having proper meanings for itself.
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23. Example of analyzing frequency and specificity of tweets
for national universities
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24. Example of analyzing frequency and specificity of tweets
for private universities
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25. Analysis of frequent and specific
terms for national universities
Categories
Examples remarkable frequent &
specific terms
University
Academic matters
菌 (germ)
ピロリ (pylori)
Hokkaido University
ノーベル (Nobel),
天野 (name of Nobelist)
Nagoya University
小惑星 (asteroid),
惑星 (planet)
Kyushu University
Social matters
原発 (nuclear plant),
事故 (accident)
Tohoku University
皇族 (royal family),
秋篠宮 (one of royal family)
Ochanomizu University
中核 (student movement’s name),
日教組 (union of teachers)
Kyoto University
宗教 (religion),
実現 (realization)
Osaka University
Student life
スキー (ski),
弓道 (archery)
Hitotsubashi University
合コン (party)
Tokyo Institute of Technology
General university
matters
東京理科大学, 東京電機大学, etc.,
(series of rival university names)
早稲田大学, 東京外国語大学,etc.,
(series of rival university names)
Hitotsubashi University
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26. Technical Remarks
1. Need to have a dictionary of emoticons.
2. Need to have the news histories of various
concurrent events at all times.
3. Need to compare the historically fixed ideas
and image of the university to the analysis
result.
4. Need to decide whether to include
automatic tweets or not.
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29. Categorizing the tweets
Academic Matter
Social Matter
General Matter
Student Life
Other
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30. Categorizing the tweets
in detail
Content of Tweets Sender
Student
life
Student clubs, other student activities Enrolled students
Entrance exams
Prospective students,
Enrolled students,
General Public
Academic
matters
Research achievements
Staff members,
Research labs,
The University
international meetings, academic conference,
departmental events, lab activities
Staff members,
Enrolled students
Faculty recruitment Staff members
General
University
matters
Official tweets by the University The University
Day-to-day tweets
Staff members,
Enrolled students
Campus scenery, events
Visitors,
Enrolled students
Social
matters
Social issues Staff members
Media coverage Mass media
The University, staff members General public
Re-tweets of university-related tweets General public
Serious incidents the university has caused
Mass media,
General public,
Staff members
Other
Not classifiable tweets ―
Calumny, abuse General public
―
Alumni
Member of University 29
31. University Characteristic Aspects
derived from Tweets
Activeness Popularity
・Public Relations
・Academic Outreach
・Academic activity
・Student life
・Social Outreach
・Selectivity of university
・Social attention
・Community-based
・General activity
・Jealousness
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32. Next Steps
1. To find associations or rules between
the five categories and ten aspects.
2. To clarify the methodology in order to
refine the associations or rules by
machine learning.
3. To specify the evaluation method of
the above methodology.
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33. Future Work
Extend the analysis to other SNS
such as Facebook or Blogs.
Twitter is difficult to specify the user
profile.
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