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Twitter:Friends,
  Followers, and
Set wise Operations
How many friends/followers do I have?
Who am I following that is not following me
back?
Who is following me that I am not following
back?
Who are the friendliest and least friendly people
in my net work?
Who are my "mutual friends"?
Given all of my followers and all of their
followers, what is my potential influence if I get
ret weeted?
Twitter's RESTful API
/users/show
OAuth
A Lean, Mean Data-
Collection Machine
401(Not Authorized), 503(Over
Capacity) Error
t witter module throws
TwitterHTTPError
Use try/except block
Registering an app at http://
dev.t witter.com/apps/new
friend/follower data will return up to
5,000IDs per call
Users could retrieve up to 1,750,000 Ids
before rate limiting.(350 request/hour)
ID values seem to be in reverse
chronological order(the first->most
recently followed, the last-> the first
person you followed)
How to navigate IDs
Store/Retrieve
     OAuth token

read_token_file
write_token_file
Redis : A Data
 Structures Server
key-value store
Data structure ser ver that keys can
contain strings, hashes, lists, sets and
sorted sets.
make V=1
sudo make install
easy_install redis
redis-server
Redis : A Data
 Structures Server
key-value store
Data structure ser ver that keys can
contain strings, hashes, lists, sets and
sorted sets.
make V=1
sudo make install
easy_install redis
redis-server
Set Operations
Redis command
Souping Up the Machine
  with Basic Friend/
   Follower Metrics
Who isn't
following me back?

following -> 663 < 100*7
follower -> 1,423,704 < 100*350*41
Randomly sample and calculate
measures of statistical significance ->
srandmember
Calculating Similarity

 Computing common friends and
 followers is just set operation away.
 Use a set intersection -> sinterstore
EX 4-5
EX 4-5
EX 4-5
Measuring Influence

One trivial way to measure the relative
influence of t wo or more users is to
simply compare their number of
followers.
MAVENS - well connected and like to
share information.
Measuring Influence
Remove top 10 followers -> avg : 284
Remove less than 10 followers -> 1,000
Construction Friendship
       Graphs
Clique Detection and
      Analysis



An analysis of the similarity/diversity
of t weet content among members of a
large clique would be a very worthy in
t weet analysis
The Infochimps
"Strong Links" API
3D Graph Visualization

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MTSW-Twitter-1

  • 1. Twitter:Friends, Followers, and Set wise Operations
  • 2. How many friends/followers do I have? Who am I following that is not following me back? Who is following me that I am not following back? Who are the friendliest and least friendly people in my net work? Who are my "mutual friends"? Given all of my followers and all of their followers, what is my potential influence if I get ret weeted?
  • 3.
  • 7. A Lean, Mean Data- Collection Machine 401(Not Authorized), 503(Over Capacity) Error t witter module throws TwitterHTTPError Use try/except block
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. Registering an app at http:// dev.t witter.com/apps/new friend/follower data will return up to 5,000IDs per call Users could retrieve up to 1,750,000 Ids before rate limiting.(350 request/hour) ID values seem to be in reverse chronological order(the first->most recently followed, the last-> the first person you followed)
  • 15. Store/Retrieve OAuth token read_token_file write_token_file
  • 16. Redis : A Data Structures Server key-value store Data structure ser ver that keys can contain strings, hashes, lists, sets and sorted sets. make V=1 sudo make install easy_install redis redis-server
  • 17. Redis : A Data Structures Server key-value store Data structure ser ver that keys can contain strings, hashes, lists, sets and sorted sets. make V=1 sudo make install easy_install redis redis-server
  • 20. Souping Up the Machine with Basic Friend/ Follower Metrics
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26. Who isn't following me back? following -> 663 < 100*7 follower -> 1,423,704 < 100*350*41 Randomly sample and calculate measures of statistical significance -> srandmember
  • 27. Calculating Similarity Computing common friends and followers is just set operation away. Use a set intersection -> sinterstore
  • 31. Measuring Influence One trivial way to measure the relative influence of t wo or more users is to simply compare their number of followers. MAVENS - well connected and like to share information.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37. Remove top 10 followers -> avg : 284 Remove less than 10 followers -> 1,000
  • 39. Clique Detection and Analysis An analysis of the similarity/diversity of t weet content among members of a large clique would be a very worthy in t weet analysis
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45.

Hinweis der Redaktion

  1. \n
  2. \n
  3. \n
  4. \n
  5. \n
  6. \n
  7. lean and mean &amp;#xAE30;&amp;#xB97C; &amp;#xC4F0;&amp;#xACE0;, &amp;#xB208;&amp;#xC5D0; &amp;#xBD88;&amp;#xC744; &amp;#xBC1D;&amp;#xD788;&amp;#xACE0;,\n
  8. \n
  9. \n
  10. \n
  11. secret_code\n
  12. &amp;#xD638;&amp;#xCD9C;&amp;#xB2F9; 5,000&amp;#xBA85;\n350*5,000 = 1,750,000\n&amp;#xC774;&amp;#xC804;&amp;#xD654;&amp;#xBA74;&amp;#xC5D0;&amp;#xC11C; user id&amp;#xB294; &amp;#xCC98;&amp;#xC74C;&amp;#xC774; &amp;#xCD5C;&amp;#xCD08; &amp;#xD3F4;&amp;#xB85C;&amp;#xC789;, &amp;#xB9C8;&amp;#xC9C0;&amp;#xB9C9;&amp;#xC774; &amp;#xCC98;&amp;#xC74C; &amp;#xD3F4;&amp;#xB85C;&amp;#xC789;.\n
  13. \n
  14. \n
  15. &amp;#xC2A4;&amp;#xD2B8;&amp;#xB9C1;,&amp;#xD574;&amp;#xC26C;,&amp;#xB9AC;&amp;#xC2A4;&amp;#xD2B8;,&amp;#xC14B;&amp;#xC744; &amp;#xD0A4;&amp;#xB85C; &amp;#xAC00;&amp;#xC9C8; &amp;#xC218; &amp;#xC788;&amp;#xB2E4;.\n
  16. \n
  17. scard - &amp;#xBA64;&amp;#xBC84;&amp;#xC218;\n
  18. \n
  19. \n
  20. \n
  21. \n
  22. \n
  23. \n
  24. &amp;#xB450;&amp;#xBA85;&amp;#xC758; &amp;#xC720;&amp;#xC800;&amp;#xAC00; &amp;#xB9CE;&amp;#xC740; &amp;#xC218;&amp;#xC758; &amp;#xB3D9;&amp;#xC77C;&amp;#xD55C; &amp;#xC0AC;&amp;#xB78C;&amp;#xB4E4;&amp;#xC744; &amp;#xD3F4;&amp;#xB85C;&amp;#xC789;&amp;#xD558;&amp;#xACE0; &amp;#xC788;&amp;#xB2E4;&amp;#xBA74; &amp;#xADF8; &amp;#xC0AC;&amp;#xB78C; &amp;#xB450;&amp;#xBA85;&amp;#xC740; &amp;#xC720;&amp;#xC0AC;&amp;#xD55C; &amp;#xAD00;&amp;#xC2EC; &amp;#xC0AC;&amp;#xD56D;&amp;#xC774; &amp;#xC788;&amp;#xC744; &amp;#xAC83;&amp;#xC774;&amp;#xB2E4;.\n
  25. &amp;#xACF5;&amp;#xD1B5;&amp;#xC758; &amp;#xCE5C;&amp;#xAD6C;&amp;#xC640; &amp;#xD3F4;&amp;#xB85C;&amp;#xC6CC; &amp;#xACC4;&amp;#xC0B0;&amp;#xC744; &amp;#xAD50;&amp;#xC9D1;&amp;#xD569;&amp;#xC744; &amp;#xD1B5;&amp;#xD574; &amp;#xC218;&amp;#xD589;&amp;#xD55C;&amp;#xB2E4;.\nscard - &amp;#xBA64;&amp;#xBC84; &amp;#xC218;.\n
  26. &amp;#xACF5;&amp;#xD1B5;&amp;#xC758; &amp;#xCE5C;&amp;#xAD6C;&amp;#xC640; &amp;#xD3F4;&amp;#xB85C;&amp;#xC6CC; &amp;#xACC4;&amp;#xC0B0;&amp;#xC744; &amp;#xAD50;&amp;#xC9D1;&amp;#xD569;&amp;#xC744; &amp;#xD1B5;&amp;#xD574; &amp;#xC218;&amp;#xD589;&amp;#xD55C;&amp;#xB2E4;.\nscard - &amp;#xBA64;&amp;#xBC84; &amp;#xC218;.\n
  27. &amp;#xB2E8;&amp;#xC21C;&amp;#xD55C; &amp;#xBC29;&amp;#xBC95;&amp;#xC740; &amp;#xD3F4;&amp;#xB85C;&amp;#xC6CC; &amp;#xC218;&amp;#xAC00; &amp;#xB9CE;&amp;#xC740; &amp;#xC0AC;&amp;#xB78C;.\n
  28. &amp;#xAC00;&amp;#xC7A5; &amp;#xB2E8;&amp;#xC21C;&amp;#xD55C; &amp;#xCE21;&amp;#xC815; &amp;#xBC29;&amp;#xBC95;&amp;#xC740; &amp;#xC790;&amp;#xC2E0;&amp;#xC758; &amp;#xD3F4;&amp;#xB85C;&amp;#xC6CC;&amp;#xC911; &amp;#xD3F4;&amp;#xB85C;&amp;#xC6CC;&amp;#xC218;&amp;#xAC00; &amp;#xB9CE;&amp;#xC740; &amp;#xC0AC;&amp;#xB78C;.\neasy_install prettytable\n
  29. &amp;#xD3F4;&amp;#xB85C;&amp;#xC6CC;&amp;#xC758; &amp;#xCE5C;&amp;#xAD6C;/&amp;#xD3F4;&amp;#xB85C;&amp;#xC6CC; &amp;#xC218;&amp;#xB97C; &amp;#xC218;&amp;#xC9D1;&amp;#xD558;&amp;#xC5EC; redis&amp;#xC5D0; &amp;#xC800;&amp;#xC7A5;&amp;#xD55C;&amp;#xB2E4;.\ndepth =1\n
  30. &amp;#xC800;&amp;#xC7A5;&amp;#xB41C; &amp;#xCE5C;&amp;#xAD6C;/&amp;#xD3F4;&amp;#xB85C;&amp;#xC6B0;&amp;#xC5D0;&amp;#xC11C; &amp;#xAC00;&amp;#xC7A5; &amp;#xB192;&amp;#xC740; &amp;#xC21C;&amp;#xC73C;&amp;#xB85C; &amp;#xCD9C;&amp;#xB825;&amp;#xD55C;&amp;#xB2E4;.\n
  31. &amp;#xC0C1;&amp;#xC704; 10&amp;#xBA85;&amp;#xC744; &amp;#xC81C;&amp;#xC678;&amp;#xD558;&amp;#xBA74; 284,\n&amp;#xD558;&amp;#xC704; 10&amp;#xBA85;&amp;#xC744; &amp;#xC81C;&amp;#xC678;&amp;#xD558;&amp;#xBA74; 1000\n
  32. \n
  33. &amp;#xD2B8;&amp;#xC717;&amp;#xBD84;&amp;#xC11D;&amp;#xC744; &amp;#xD560; &amp;#xB54C; &amp;#xD070; &amp;#xD074;&amp;#xB9AC;&amp;#xD06C;&amp;#xC5D0;&amp;#xC11C; &amp;#xD2B8;&amp;#xC717;&amp;#xC758; &amp;#xC720;&amp;#xC0AC;&amp;#xC131;&amp;#xACFC; &amp;#xB2E4;&amp;#xC591;&amp;#xC131;&amp;#xC744; &amp;#xBD84;&amp;#xC11D;&amp;#xD558;&amp;#xB294; &amp;#xAC83;&amp;#xC740; &amp;#xD6A8;&amp;#xC728;&amp;#xC801;&amp;#xC774;&amp;#xB2E4;.\n
  34. &amp;#xD074;&amp;#xB9AC;&amp;#xD06C;&amp;#xB97C; &amp;#xCC3E;&amp;#xB294; &amp;#xBC29;&amp;#xBC95;&amp;#xC740; &amp;#xB9E4;&amp;#xC6B0; &amp;#xC5B4;&amp;#xB835;&amp;#xC9C0;&amp;#xB9CC; NetworkX&amp;#xC5D0;&amp;#xC11C; find_cliques&amp;#xB97C; &amp;#xC9C0;&amp;#xC6D0;&amp;#xD55C;&amp;#xB2E4;.\n
  35. \n
  36. \n
  37. \n
  38. \n
  39. \n