SNA 1.1

1.1.1 Key Concepts & Definitions

Social Media: Tools (email, blogs, wikis, etc.) where billions of people create trillions of connections daily. These platforms make previously “invisible” ties visible and machine-readable.
Social Network Analysis (SNA): The application of Network Science to human relationships. It uses mathematical techniques and computer programs to map the shape and key locations within a landscape of links.
Social Networks: Collections of connections among groups of people and things. They are “primordial”—they existed long before the internet through any exchange of help or information.

Network Science: The study of patterns in physical systems (chemical/genetic), biological systems (food webs), and human phenomena (markets, trust, collective action).
Scientific Evolution:
- Measurement: Similar to Lord Kelvin’s emphasis on measurement to advance science.
- Mathematics: Social scientists are developing math for network evolution/decay, similar to how Newton/Leibniz created calculus for physics.
- Visualization: New tools act like Galileo’s telescope or Hooke’s microscope, revealing structures (isolated groups, influential participants) never seen before.

Social media is not just for consumers (Facebook/Twitter); it is a “critical internal nervous system” for corporations.

Beyond the Firewall: Employees use wikis, blogs, and message boards to share documents and lower costs.
Business Value: SNA reveals the actual organizational structure versus the formal “org-chart.”
Key Roles Identified by SNA:
- Bridges/Brokers: Individuals who connect otherwise separated departments.
- Information Sources: People who serve as central hubs for knowledge.
Gartner Group Insights: SNA is an “untapped information asset” providing intelligence on:

1.1. Organizational Network Analysis: Internal ties.
1. Value Network Analysis: Vendor ties.
2. Influence Analysis: Consumer ties.

Bottom-Up Initiative: Individual contributions (edits, posts, likes) aggregate to create public wealth (e.g., Wikipedia, Open Source Software).
The “Forest vs. Trees”: Analysts must focus on individual behavior while understanding emergent properties (the collective whole).
The Dark Side: Social media can be used by criminals, for racial hatred, or by oppressive governments. Critics (like Jaron Lanier) warn of a loss of individual responsibility.

Governments use social media to address large-scale challenges:

Disaster Response: Volunteer sites for Hurricane Katrina coordination or Wikipedia pages for the Virginia Tech shooting.
Community Safety: “Nation of Neighbors” (neighborhood watch) and “Amber Alert” (abducted children).
Collaborative Governance: “Peer-to-Patent” (citizens helping patent examiners) and “Citizen Science” (NASA’s “Be a Martian” or Encyclopedia of Life).
Information Dissemination: Rapid alerts for flu vaccinations or weather threats. People often trust friends/family on social media more than official TV broadcasts.

Europe: Focused on “Public Services 2.0” to re-engage citizens in politics, though they note it “disrupts existing power balances.”
China: Focuses on foundations of social computing and cyber-physical systems.
Web Science: Aims to understand the web as a socio-technical phenomenon—acknowledging that the success of a site depends more on social rules and communities than just the hardware/software.
Sociotechnical Systems (STS): An approach that balances technology design with human needs and values to increase motivation and responsibility.

NodeXL: (Network Overview Discovery and Exploration add-in for Excel).
- A free, open-source tool designed for ease of use.
- Allows non-programmers to visualize and analyze social media networks.

Stakeholder	Benefit of Social Network Analysis
Business Leaders	Identify key contributors, find gaps in communication, and locate expertise.
Marketing Directors	Track compliments/complaints and guide product promotion.
Government Agencies	Disseminate health info, coordinate disaster response, and engage citizens.
Researchers	Evaluate which social strategies lead to success vs. failure using evidence.