The proposal of the viral model of information which is considering social media interactivity

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This document discusses research analyzing social media usage in Japan. It examines changes in social media users over time and how companies use social media. The research aims to understand the behavior of social media recipients and analyze Facebook pages to classify social media into types. It also describes developing a probability hierarchy model to understand information diffusion on social media and conducting statistical tests and keyword extraction to study linguistic and nonlinguistic information. The conclusion discusses determining relationship types between social media users through structural equation modeling analysis.

Technology Business

Contents

Tokyo City University Systems Information Engineering 2

Research Background ～Consumer Side～

Figure 1: Changes in the users of SM in Japan (10 - 11)

Tokyo City University Systems Information Engineering 3

Research Background ～Company side～

Tokyo City University Systems Information Engineering 4

Purpose of this Research

Tokyo City University Systems Information Engineering 5

Result of Analysis ～Similarity of SM
～

Tokyo City University Systems Information Engineering 6

Labeling the SM Types

Tokyo City University Systems Information Engineering 7

Behavior of the Recipient Information

Tokyo City University Systems Information Engineering 8

Behavior of the Recipient Information

Tokyo City University Systems Information Engineering 9

Analysis of the Facebook pages

Tokyo City University Systems Information Engineering 10

Set the Probability Hierarchy
Transition

Tokyo City University Systems Information Engineering 11

Set the Probability Hierarchy
Transition

Tokyo City University Systems Information Engineering 12

Set the Probability Hierarchy
Transition

Tokyo City University Systems Information Engineering 13

Set the Probability Hierarchy
Transition

Tokyo City University Systems Information Engineering 14

Set the Probability Hierarchy
Transition

Tokyo City University Systems Information Engineering 15

Statistical Test about Linguistic &
Nonlinguistic Info

Tokyo City University Systems Information Engineering 16

Extraction of Keywords prompting
behavior

Tokyo City University Systems Information Engineering 17

Assumption Output ～Using SEM analysis～

Tokyo City University Systems Information Engineering 18

Conclusion

Tokyo City University Systems Information Engineering 19

Future tasks

Tokyo City University Systems Information Engineering 20

References

Tokyo City University Systems Information Engineering 21

Questions & Answers

I’m happy to answer your questions!

Tokyo City University Systems Information Engineering 22

Definition about the SM and SNS

Tokyo City University Systems Information Engineering 23

Definition about the VIRAL & Ex.

Empathy!
Diffusion

Buy!!

Case Study

1. Spreading of BAD Rumors at the time of the Earthquake
2. Obituary of bin Laden

Tokyo City University Systems Information Engineering 24

Companies Purposes of the SM

Tokyo City University Systems Information Engineering 25

Changes of the SNS by the trend

Tokyo City University Systems Information Engineering 26

Variation in the number of samples

Tokyo City University Systems Information Engineering 27

Details of the Probability Transition
Hierarchy

Tokyo City University Systems Information Engineering 28

Concrete data used in correspondence
analysis

Tokyo City University Systems Information Engineering 29

Result of Morphological Analysis

Tokyo City University Systems Information Engineering 30

Assumption Output ～ Concern about Ties ～

Tokyo City University Systems Information Engineering 31

Novelty & Usefulness of Research

Tokyo City University Systems Information Engineering 32

About Morphological Analysis

Tokyo City University Systems Information Engineering 33

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The proposal of the viral model of information which is considering social media interactivity

2. Contents Tokyo City University Systems Information Engineering 2

3. Research Background ～Consumer Side～ Figure 1: Changes in the users of SM in Japan (10 - 11) Tokyo City University Systems Information Engineering 3

4. Research Background ～Company side～ Tokyo City University Systems Information Engineering 4

5. Purpose of this Research Tokyo City University Systems Information Engineering 5

6. Result of Analysis ～Similarity of SM ～ Tokyo City University Systems Information Engineering 6

7. Labeling the SM Types Tokyo City University Systems Information Engineering 7

8. Behavior of the Recipient Information Tokyo City University Systems Information Engineering 8

9. Behavior of the Recipient Information Tokyo City University Systems Information Engineering 9

10. Analysis of the Facebook pages Tokyo City University Systems Information Engineering 10

11. Set the Probability Hierarchy Transition Tokyo City University Systems Information Engineering 11

12. Set the Probability Hierarchy Transition Tokyo City University Systems Information Engineering 12

13. Set the Probability Hierarchy Transition Tokyo City University Systems Information Engineering 13

14. Set the Probability Hierarchy Transition Tokyo City University Systems Information Engineering 14

15. Set the Probability Hierarchy Transition Tokyo City University Systems Information Engineering 15

16. Statistical Test about Linguistic & Nonlinguistic Info Tokyo City University Systems Information Engineering 16

17. Extraction of Keywords prompting behavior Tokyo City University Systems Information Engineering 17

18. Assumption Output ～Using SEM analysis～ Tokyo City University Systems Information Engineering 18

19. Conclusion Tokyo City University Systems Information Engineering 19

20. Future tasks Tokyo City University Systems Information Engineering 20

21. References Tokyo City University Systems Information Engineering 21

22. Questions & Answers I’m happy to answer your questions! Tokyo City University Systems Information Engineering 22

23. Definition about the SM and SNS Tokyo City University Systems Information Engineering 23

24. Definition about the VIRAL & Ex. Empathy! Diffusion Buy!! Case Study 1. Spreading of BAD Rumors at the time of the Earthquake 2. Obituary of bin Laden Tokyo City University Systems Information Engineering 24

25. Companies Purposes of the SM Tokyo City University Systems Information Engineering 25

26. Changes of the SNS by the trend Tokyo City University Systems Information Engineering 26

27. Variation in the number of samples Tokyo City University Systems Information Engineering 27

28. Details of the Probability Transition Hierarchy Tokyo City University Systems Information Engineering 28

29. Concrete data used in correspondence analysis Tokyo City University Systems Information Engineering 29

30. Result of Morphological Analysis Tokyo City University Systems Information Engineering 30

31. Assumption Output ～ Concern about Ties ～ Tokyo City University Systems Information Engineering 31

32. Novelty & Usefulness of Research Tokyo City University Systems Information Engineering 32

33. About Morphological Analysis Tokyo City University Systems Information Engineering 33

The proposal of the viral model of information which is considering social media interactivity

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