This document outlines Cheng-Jun Wang's vision for computational communication. It discusses backgrounds in network science, computational thinking, and types of digital data available. The vision is to use network science, statistical linguistics, and studies of human communication dynamics to learn from digital media, big data, and digital traces in order to understand data journalism, computational advertising, and what is currently happening. The goal is to pave the way for computational communication.

1. The Way to Computational Communication
通往计算传播学之路
v i s i o n
Cheng-Jun Wang
王成军 @ 深圳大学
20130104
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2. Outline
• Background 背景
• Three metaphors of theory 关于理论的三个比喻
• Sandglass of generalization 归纳理论的沙漏
• Five levels of research 研究的五个阶段
• Network science网络科学
• Computational thinking 可计算化思维
• The way to computational communication 通往
计算传播学之路
• Vision 愿景
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3. Background
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4. Three metaphors of theory
A=B Follow
Your
Logic
Immanuel Kant: All our knowledge begins with the senses, proceeds then to the
understanding, and ends with reason. There is nothing higher than reason.
Net Lens Map
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5. Sandglass of generalization
Theory
Hypothesis
Claim • Because of
Method
Sandglass
Model of
Generalization
Reason • Based on
Findings
Discussi
on Evidence
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6. Five levels of Research
principle
mechanism
law
pattern
data
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7. Network Science
c
g
h d a
e b
f
UGC (LOG)
Public Attention (LOG)
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8. Computational Thinking
Physics
Computer Science
Biology
Computability
Computer
Mathematics
Science
Network Science
Computational
Science
Economics
Natural
Science
Communication
Years since born
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9. The Way to Computational
Communication
Digital Data
Big Data
Longitudinal Data
Relational Data
Behavioral Data
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10. Vision：
Where are we?
• Network science
What we can • Statistical linguistics
learn from? • Human Communication dynamics
• Digital media
What we have? • Big data& digital traces
What is
• Data journalism
happening or • Computational advertising
have done?
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11. Immanuel Kant: Two things awe me most, the starry
sky above me and the moral law within me.
Thank you for your attention!
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