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Classifying Twitter Users

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  • 1. Classifying Twitter Users David Horn Cal Poly
  • 2. How can you classify social network users? What kinds of traits do they have? How does information spread on twitter?
  • 3. Power graph analysis kinda fails Twitter is no more a social network than IRC Following is cheap
  • 4. This just in: following spammers gets you spammed “When I unfollowed 106,000 people all my spam just stopped. Dead. “
  • 5. Mavens Discover new information Connectors Spread information to new communities
  • 6. Ineffectual Heavily engaged, but do not get a strong response from the community Consumer Less engaged with the service
  • 7. Engagement Metrics Engaged - How often others @mention a user Engaging - How often a user @mentions others
  • 8. Twitter User Engagement 80 70 60 50 Engaged by 40 30 20 10 0 0 10 20 30 40 50 60 70 80 Engaging
  • 9. Twitter User Engagement 80 70 60 50 Engaged by 40 30 20 10 0 0 10 20 30 40 50 60 70 80 Engaging
  • 10. Twitter User Engagement 80 70 60 Mavens Number of users engaging 50 Connectors 40 30 20 10 0 0 10 20 30 40 50 60 70 80 Number of users engaged
  • 11. Twitter User Engagement 80 70 60 Number of users engaging 50 40 30 Ineffectual 20 10 0 0 10 20 30 40 50 60 70 80 Number of users engaged Community-at-large
  • 12. Convert that to polar coordinates (x,y) => (r,θ) r engaged θ engaging
  • 13. Magnitude r  engaged 2  engaging 2 How engaged is this user with the service?
  • 14. Angle  engaged    sin  1   r  What kind of a user this is
  • 15. 90° Maven 60° Connector If magnitude > 15 40° Ineffectual 0°
  • 16. Meme flow: Larger community Connector Maven Source (anywhere) Time
  • 17. For any particular meme, you can expect a traffic breakdown: • 10% from trend leaders • 20% from community leaders • 30% from the overall community • 40% from ineffectual users
  • 18. Reach Reach = r/engaging*followers
  • 19. Chance a meme will spread P(spread) = θ/90
  • 20. http://engagerank.com Try it out!
  • 21. T

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