Social Media Conversation Analyst
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Social media analytics for decision-making
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04 Jan 09 Taking popular sources out of the mix

As I’ve been working on the algorithm for hot Twitter cites, I’ve noticed that a handful of sources are responsible for most of the top 50 URLs cited.

They are:

  • Techcrunch
  • Engadget
  • Mashable
  • Cnet
  • Readwriteweb
  • Techmeme
  • Crunchgear
  • Lifehacker

Under the assumption that repeating content from popular sites doesn’t add much value, here’s what the current list looks like without those sites.

  1. YouTube – Broadcast Yourself. (32544 points)
  2. Phishing Scam Spreading on Twitter | Chris Pirillo (29291 points)
  3. Twitter / Tim: ack. how do i tell git to … (11197 points)
  4. Understanding Your Guests | chrisbrogan.com (7140 points)
  5. http://ow.ly/24v (6329 points)
  6. Seth’s Blog: When marketing goes nuclear (6126 points)
  7. Scientists discover true love – Times Online (5967 points)
  8. (5762 points)
  9. Seth’s Blog: Do ads work? (5494 points)
  10. Scientists: True love can last a lifetime – CNN.com (5248 points)
  11. Twitter be Nimble, Twitter be Quick, if you don’t know Jack, try these Twitter Tricks (4734 points)
  12. http://www.jpost.com/servlet/Satellite (4636 points)
  13. (4048 points)
  14. Seth’s Blog: Is everything okay? (3864 points)
  15. New! louisgray.com: Are We Too Connected to Social Media? (3798 points)
  16. New! I am getting a demo of something that will addict me much more than friendfeed in 2009. This is NOT good. – FriendFeed (3500 points)
  17. louisgray.com: 10 Predictions for 2009 In the World of Tech (3336 points)
  18. New! 100+ Remarkably Beautiful Twitter Icons And Buttons | Icons (3286 points)
  19. New! A Quick Public Service Announcement for Twitter Users | TheBusyBrain Blog! (3148 points)
  20. New! Sources: Burris won’t be allowed on Senate floor Tuesday – CNN.com (3075 points)
  21. New! How to Use Twitter to Grow Your Business — Copyblogger (2955 points)
  22. New! Religulous.avi (2955 points)
  23. New!
  24. New! Op-Ed Contributors – The End of the Financial World as We Know It – NYTimes.com (1927 points)
  25. Helloform » On Twply and giving out your Twitter password (updated) (1776 points)
  26. New! Digital Ethnography (1635 points)
  27. New! 10 Promising Free Web Analytics Tools – Six Revisions (1606 points)
  28. New! IPhone Apps: iSteam iPhone Steam Simulation App is Amazingly Cool (1554 points)
  29. New! Using social media in small business | Small Business Marketing Blog from Duct Tape Marketing (1413 points)
  30. Scobleizer — Tech geek blogger » Blog Archive Twitter warning: your account data is being sold « (1387 points)
  31. ReTweet Mapper (1279 points)
  32. New! Happy Tweets (1227 points)
  33. New! The Air Force’s Rules of Engagement for Blogging — Global Nerdy (1222 points)
  34. New! 60 New York Times profiles on Twitter | PRBLOGGER.COM (1077 points)
  35. New! Twitter Blog: Gone Phishing (1072 points)
  36. New! AppleInsider | Apple files for patent on winter-friendly iPhone gloves (1042 points)
  37. New! http://mrtweet.net?c=12 (1035 points)
  38. New! Robbie Madison’s Amazing Record Jump Video (918 points)
  39. New! ideasonideas – Eric Karjaluoto discusses design, brands and experience » Blog Archive » Why your web startup will fail (918 points)
  40. New! YouTube – Privacy Sucks In Social Media (917 points)
  41. New! Truemors :: Wikipedia Meets $6 Million Fundraising Goal (836 points)
  42. New! Israel and Gaza – The Big Picture – Boston.com (808 points)
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03 Jan 09 Added a page for hot Twitter cites

I’ve added a page to this blog for hot Twitter cites, based on the code I’ve been writing over the last few days.

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02 Jan 09 Updated: frequently Twittered URLs

I’ve added the necessary code to report on frequently posted URLs by day, so here’s an updated list of the URLs people have been frequently Twittering since yesterday (beginning 12:01 New Years Day).  Some of these were in the previous list, but some are new.  Comparing the list from day to day gives an idea of what’s gaining momentum.  I’ll be adding an hour-of-day column to the database, too, to get finer-grained acceleration reporting.  This is starting to come together into something I’ll automate soon, it looks like.
Anybody aware of a similar list that I can compare for a reality check?  I’m wondering if my sampling is intelligent enough to catch most, if not all, of the highly popular URLs being Twittered.
  1. 1 URL(s), 529 users: http://twitter.com/toni_stewart/statuses/1083734925
  2. 1 URL(s), 358 users: http://twply.com/
  3. 1 URL(s), 290 users: http://happytweets.com
  4. 1 URL(s), 258 users: http://water.alltop.com/
  5. 1 URL(s), 257 users: http://twitter.com/gfxmonk/statuses/1083729313
  6. 1 URL(s), 213 users: http://twit.pix.ly
  7. 10 URL(s), 121 users: http://www.searchenginejournal.com/top-20-twitter-posts-of-2008/8221/
  8. 13 URL(s), 120 users: http://www.copyblogger.com/grow-business-twitter/
  9. 2 URL(s), 120 users: http://dcortesi.com/tools/my-first-follow/
  10. 10 URL(s), 113 users: http://www.techcrunch.com/2008/12/31/top-social-media-sites-of-2008-facebook-still-rising/
  11. 1 URL(s), 113 users: http://nutrition.alltop.com/
  12. 8 URL(s), 107 users: http://www.chrisbrogan.com/27-blogging-secrets-to-power-your-community/
  13. 7 URL(s), 105 users: http://www.techcrunch.com/2008/12/30/large-form-ipod-touch-to-launch-in-fall-09/
  14. 1 URL(s), 99 users: http://mrtweet.net?c=12
  15. 6 URL(s), 92 users: http://www.prblogger.com/2008/12/60-new-york-times-profiles-on-twitter/
  16. 7 URL(s), 73 users: http://www.crunchgear.com/2008/12/31/all-zune-30s-crapping-out/
  17. 6 URL(s), 71 users: http://mashable.com/2008/12/30/how-to-simplify/
  18. 4 URL(s), 65 users: http://www.boston.com/bigpicture/2008/12/israel_and_gaza.html
  19. 6 URL(s), 63 users: http://gizmodo.com/5121311/30gb-zunes-failing-everywhere-all-at-once
  20. 1 URL(s), 58 users: http://www.zuneboards.com/forums/zune-news/38143-cause-zune-30-leapyear-problem-isolated.html
  21. 3 URL(s), 57 users: http://blog.wired.com/defense/2008/12/israels-info-wa.html
  22. 5 URL(s), 54 users: http://online.wsj.com/article/SB123051100709638419.html
  23. 1 URL(s), 54 users: http://twoogie.com/
  24. 3 URL(s), 51 users: http://mashable.com/2009/01/01/twitter-user-types/
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01 Jan 09 Implicit social networks: If Guy Kawasaki is right, so am I

Just read Guy Kawasaki’s Looking for Mr. Goodtweet: How to Pick Up Followers on Twitter, in which he offered the following tip:

Tip 4: Follow everyone who follows you. When I first started on Twitter, Robert Scoble told me to follow everyone who followed me. “But why, Robert, would I follow everyone like that?” The answer is that it’s courteous to do so and because when you do, some people will respond to you and eveyone who follows them will see this—which is more exposure for you.

Having said this, when you get to more than fifty or so followers, it’s impossible to read what all your followers tweet. At that point, you have to focus on direct private messages (“Ds”) and direct public messages (“@s””).

Yipes. 

The first analysis I did on Twitter was to count the followers of followers, as an illustration of how influence is a more-than-first-order phenomenon.  The number of followers your followers have almost surely correlates to your potential influence.  People like Guy are just about impossible to measure that way – they have so many followers that it is impractical to count their followers’ followers.

When I first looked at Twitterholic and saw how many followers Guy has, I thought “How the heck does anybody follow that many people?”   That question is answered – he doesn’t.  It is possible that Guy decided to do me a favor (along with everybody else who looks at implicit, in addition to explicit, social networks).  Guy knows that this is where I’ve focused for years… but, okay, that’s probably not why he advises people (who want a lot of followers) to follow everybody who follows them.  Still, it is good news for me.

The pattern of followers is Twitter’s explicit social network.  As soon as I started analyzing it I was stymied a bit by robotic auto-followers.  They play havoc with metrics that depend on the follower-followee relationship.  My friend Dave suggested that Twitter might need user-agent meta data, which would reveal whether or not a given Twitter user is a real person or not.  This would allow software to omit users like hashtags, twemes, AmazonGoldDeals, mrtweet and so forth, all of which automatically follow you if you follow them.  But it wouldn’t solve the problem as long as there are people like Guy with robot-like behavior, automatically following everybody who follows them.  I’m fairly certain that  Guy really is not a robot – even though, in addition to tip No. 4, he advocates somewhat mindless direct replies:

Tip 2: Send @ messages to the smores. They probably won’t answer you, but that’s okay. All you want to do is appear like you have a relationship with them to enhance your credibility. The theory is, “If she is tweeting with @scobleizeer, she must be worth following.” Bull shiitake logic, admittedly, but it helps. To bastardize what a famous PR person once told me, “It’s not who you know. It’s who appears to know you.”

That tip guarantees that I’ll be sending Guy an @ message about the tweet that points to this post.  But I actually do know him, unlike the 10 or 15 other people on Twitter who don’t, who will @ message him anyway, further confounding those who naively analyze the explicit social network by looking at @ message relationships.

There’s nothing wrong with analyzing the explicit social network, but it is a big mistake to trust the results by themselves.  So I focus on the real meat in Twitter, figuring out as much as possible which items are getting real human energy into them and what they imply about relationships.  When a hundred people post links to the same URL, odds are that the page they’re tweeting really is meaningful and those people are likely to influence each other in the Twitterverse, especially if they used the same “shrunken” URL.  Throw in screen names, hash tags and language patterns and perhaps something truly useful and meaningful will come out.  I hope so.

As long as Guy doesn’t start advocating retweeting the “smores” tweets, I’m probably okay.  So far, all he’s done in that direction is to tell people to repeat their own tweets.

If Guy is right that it is a good idea to follow everybody who follows you, then I must be right to insist that any analysis that depends only on the explicit social network is inherently flawed.  Now I just have to decide if I really want to follow, not just Guy’s advice, but everybody who follows me. A voice in my head is saying, “If everybody jumped off a cliff…”

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01 Jan 09 Twitter: Frequently cited URLs, Part 2

Following up on my last post, here’s the up-to-date list of URLs are are frequently cited on Twitter, among the pages and people that my scraper has explored.  (Look down a few posts to see how it is somewhat intelligently exploring.)  The list shows how many URL variations are being used (an indicator of how many people independently decide to tweet about the page) and how many people have included a URL in their tweets in the last few days.

The list appears below, ranked by the number of users citing the URL.  Below that is a list of URLs that had at least 50 cites, ranked by the number of unique  URLs, which would be the way to do it if you wanted to find hot topics that people are finding independently of each other.  

  1. 1 URL(s), 650 users: http://twitter.com/toni_stewart/statuses/1083734925 
  2. 3 URL(s), 319 users: http://dcortesi.com/tools/my-first-follow/
  3. 1 URL(s), 273 users: http://happytweets.com
  4. 1 URL(s), 258 users: http://water.alltop.com/
  5. 1 URL(s), 183 users: http://twit.pix.ly
  6. 11 URL(s), 170 users: http://www.searchenginejournal.com/top-20-twitter-posts-of-2008/8221/
  7. 7 URL(s), 128 users: http://www.techcrunch.com/2008/12/30/large-form-ipod-touch-to-launch-in-fall-09/
  8. 1 URL(s), 116 users: http://twply.com/
  9. 4 URL(s), 115 users: http://www.chrisbrogan.com/social-media-is-no-place-for-robot-behavior/
  10. 1 URL(s), 106 users: http://good-news.alltop.com/
  11. 6 URL(s), 104 users: http://online.wsj.com/article/SB123051100709638419.html
  12. 1 URL(s), 104 users: http://twitter.com/gfxmonk/statuses/1083729313
  13. 11 URL(s), 99 users: http://www.copyblogger.com/grow-business-twitter/
  14. 6 URL(s), 99 users: http://www.prblogger.com/2008/12/60-new-york-times-profiles-on-twitter/
  15. 6 URL(s), 97 users: http://www.chrisbrogan.com/27-blogging-secrets-to-power-your-community/
  16. 3 URL(s), 96 users: http://tweetree.com/
  17. 1 URL(s), 88 users: http://mrtweet.net?c=11!
  18. 1 URL(s), 85 users: http://nutrition.alltop.com/
  19. 9 URL(s), 82 users: http://www.techcrunch.com/2008/12/31/top-social-media-sites-of-2008-facebook-still-rising/
  20. 6 URL(s), 81 users: http://www.twitip.com/why-twitter-will-go-mainstream-in-2009/
  21. 7 URL(s), 73 users: http://www.crunchgear.com/2008/12/31/all-zune-30s-crapping-out/
  22. 5 URL(s), 71 users: http://farm4.static.flickr.com/3114/3141139302_45d5b3b0a6_o.jpg
  23. 4 URL(s), 71 users: http://mashable.com/2008/12/27/how-to-2008/
  24. 3 URL(s), 71 users: http://sixrevisions.com/wordpress/15-useful-tools-for-wordpress-bloggers/
  25. 3 URL(s), 71 users: http://www.techcrunch.com/2008/12/28/dmfail-another-reason-to-just-not-send-private-messages-on-twitter/
  26. 4 URL(s), 68 users: http://blog.wired.com/defense/2008/12/israels-info-wa.html
  27. 8 URL(s), 67 users: http://mashable.com/2008/12/29/brand-reputation-monitoring-tools/
  28. 7 URL(s), 66 users: http://www10.nytimes.com/2008/12/28/business/28digi.html?_r=5
  29. 4 URL(s), 66 users: http://crunchies2008.techcrunch.com/votes/
  30. 6 URL(s), 65 users: http://mashable.com/2008/12/30/how-to-simplify/
  31. 6 URL(s), 64 users: http://www.techcrunch.com/2008/12/28/the-future-of-social-search-or-why-google-should-buy-facebook/
  32. 3 URL(s), 63 users: http://www.readwriteweb.com/archives/2009_web_predictions.php
  33. 6 URL(s), 61 users: http://mashable.com/2008/12/29/benefits-of-social-media-marketing/
  34. 6 URL(s), 60 users: http://gizmodo.com/5121311/30gb-zunes-failing-everywhere-all-at-once
  35. 4 URL(s), 56 users: http://www.crunchgear.com/2008/12/30/md5-collision-creates-rogue-certificate-authority/
  36. 4 URL(s), 56 users: http://www.techcrunch.com/2008/12/29/its-not-how-many-followers-you-have-that-counts-its-how-many-times-you-get-retweeted/
  37. 4 URL(s), 56 users: http://www.techcrunch.com/2008/12/30/netflix-adobe-google-make-best-places-to-work-list-att-ebay-radioshack-among-the-worst/
  38. 4 URL(s), 55 users: http://mashable.com/2008/12/30/california-budget-crisis/
  39. 3 URL(s), 54 users: http://www.chrisbrogan.com/free-ebook-using-the-social-web-to-find-work/
  40. 1 URL(s), 54 users: http://tweetree.com
  41. 4 URL(s), 53 users: http://www.sociableblog.com/2008/12/29/top-twitter-tools-2009/
  42. 1 URL(s), 53 users: http://twinfluence.com
  43. 4 URL(s), 51 users: http://gizmodo.com/5120687/steve-jobs-health-declining-rapidly-reason-for-macworld-cancellation
  44. 1 URL(s), 51 users: http://twoogie.com/
  45. 6 URL(s), 50 users: http://digital-photography-school.com/blog/21-settings-techniques-and-rules-all-new-camera-owners-should-know/
  46. 3 URL(s), 50 users: http://www.win.tue.nl/hashclash/rogue-ca/
  47. 2 URL(s), 50 users: http://quizible.com/quiz/how-web-2-are-you/22

Here’s the list ranked by the number of unique URLs, for those that were cited by at least 50 users.

  1. 11 URLs, 170 users: “http://www.searchenginejournal.com/top-20-twitter-posts-of-2008/8221/
  2. 11 URLs, 99 users: “http://www.copyblogger.com/grow-business-twitter/
  3. 9 URLs, 82 users: “http://www.techcrunch.com/2008/12/31/top-social-media-sites-of-2008-facebook-still-rising/
  4. 8 URLs, 67 users: “http://mashable.com/2008/12/29/brand-reputation-monitoring-tools/
  5. 7 URLs, 128 users: “http://www.techcrunch.com/2008/12/30/large-form-ipod-touch-to-launch-in-fall-09/
  6. 7 URLs, 73 users: “http://www.crunchgear.com/2008/12/31/all-zune-30s-crapping-out/
  7. 7 URLs, 66 users: “http://www10.nytimes.com/2008/12/28/business/28digi.html?_r=5
  8. 6 URLs, 104 users: “http://online.wsj.com/article/SB123051100709638419.html
  9. 6 URLs, 99 users: “http://www.prblogger.com/2008/12/60-new-york-times-profiles-on-twitter/
  10. 6 URLs, 97 users: “http://www.chrisbrogan.com/27-blogging-secrets-to-power-your-community/
  11. 6 URLs, 81 users: “http://www.twitip.com/why-twitter-will-go-mainstream-in-2009/
  12. 6 URLs, 65 users: “http://mashable.com/2008/12/30/how-to-simplify/
  13. 6 URLs, 64 users: “http://www.techcrunch.com/2008/12/28/the-future-of-social-search-or-why-google-should-buy-facebook/
  14. 6 URLs, 61 users: “http://mashable.com/2008/12/29/benefits-of-social-media-marketing/
  15. 6 URLs, 60 users: “http://gizmodo.com/5121311/30gb-zunes-failing-everywhere-all-at-once
  16. 5 URLs, 71 users: “http://farm4.static.flickr.com/3114/3141139302_45d5b3b0a6_o.jpg
  17. 4 URLs, 115 users: “http://www.chrisbrogan.com/social-media-is-no-place-for-robot-behavior/
  18. 4 URLs, 71 users: “http://mashable.com/2008/12/27/how-to-2008/
  19. 4 URLs, 68 users: “http://blog.wired.com/defense/2008/12/israels-info-wa.html
  20. 4 URLs, 66 users: “http://crunchies2008.techcrunch.com/votes/
  21. 4 URLs, 56 users: “http://www.crunchgear.com/2008/12/30/md5-collision-creates-rogue-certificate-authority/
  22. 4 URLs, 56 users: “http://www.techcrunch.com/2008/12/29/its-not-how-many-followers-you-have-that-counts-its-how-many-times-you-get-retweeted/
  23. 4 URLs, 56 users: “http://www.techcrunch.com/2008/12/30/netflix-adobe-google-make-best-places-to-work-list-att-ebay-radioshack-among-the-worst/
  24. 4 URLs, 55 users: “http://mashable.com/2008/12/30/california-budget-crisis/
  25. 4 URLs, 53 users: “http://www.sociableblog.com/2008/12/29/top-twitter-tools-2009/
  26. 4 URLs, 51 users: “http://gizmodo.com/5120687/steve-jobs-health-declining-rapidly-reason-for-macworld-cancellation
  27. 3 URLs, 319 users: “http://dcortesi.com/tools/my-first-follow/
  28. 3 URLs, 96 users: “http://tweetree.com/
  29. 3 URLs, 71 users: “http://sixrevisions.com/wordpress/15-useful-tools-for-wordpress-bloggers/
  30. 3 URLs, 71 users: “http://www.techcrunch.com/2008/12/28/dmfail-another-reason-to-just-not-send-private-messages-on-twitter/
  31. 3 URLs, 63 users: “http://www.readwriteweb.com/archives/2009_web_predictions.php
  32. 3 URLs, 54 users: “http://www.chrisbrogan.com/free-ebook-using-the-social-web-to-find-work/
  33. 1 URLs, 650 users: “http://twitter.com/toni_stewart/statuses/1083734925
  34. 1 URLs, 273 users: “http://happytweets.com
  35. 1 URLs, 258 users: “http://water.alltop.com/
  36. 1 URLs, 183 users: “http://twit.pix.ly
  37. 1 URLs, 116 users: “http://twply.com/
  38. 1 URLs, 106 users: “http://good-news.alltop.com/
  39. 1 URLs, 104 users: “http://twitter.com/gfxmonk/statuses/1083729313
  40. 1 URLs, 88 users: “http://mrtweet.net?c=11!
  41. 1 URLs, 85 users: “http://nutrition.alltop.com/
  42. 1 URLs, 54 users: “http://tweetree.com
  43. 1 URLs, 53 users: “http://twinfluence.com
  44. 1 URLs, 51 users: “http://twoogie.com/

Note:  There actually were a large number of unique URLs that point back to Twitter home page and Facebook, but I think that they didn’t resolve properly.  In the case of Facebook, I suspect a second redirect is taking place because they all resolved to the login page.  As for the ones that point back to the Twitter home page, I don’t know what’s going on there.  User error, perhaps, but I’ll have to dig deeper.

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01 Jan 09 Twitter: Recent frequently cited URLs

My Twitter scraper allows me to see which web pages are being cited by the most people (out of the relatively small, but closely related, sample of Twitterers that it has explored). I can see how many variants of “shrunken” URLs are pointing to the same page, which gives a rough idea of how many people are independently finding and citing a page.

Below is a list of people who recently cited How to Use Twitter to Grow Your Business in a tweet.  This was one of the most widely cited URLs with a relatively high number of URL variations that my scraper found over the last few days.

This page was cited by 98 people* with 11 URL variations.  They appear below, in chronological order with some typographic hints.  Each unique shrunken URL is color-coded, which indicates that those people are near one another in the Twitter social network.  That’s especially cool because it did not require scraping through all of their followers to figure out, which would have consumed far more resources.  The publication time is underlined for the first person who used each unique URL.  Those people are likely to be opinion leaders, particularly if they created the shrunken URL (which a Google on the URL would be likely to reveal).  The people who tweeted the URLs earlier are more likely to be opinion leaders, since there are more others who may have been influenced by them.

In addition to identifying hot topics and cliques within the social network, this is also a means of finding people who might be worth following, given that the first people to cite a URL that becomes popular are either influential or are good at spotting trends early (a distinction that can be impossible to resolve).

  • prblogs (http://tinyurl.com/7lmjsv – 2008-12-31 18:21:33)
  • copyblogger (http://tinyurl.com/8c5lc6 – 2008-12-31 18:28:49)
  • DrimoN (http://tinyurl.com/7lmjsv – 2008-12-31 18:35:14)
  • BSwafford (http://tinyurl.com/8c5lc6 – 2008-12-31 18:46:19)
  • mollermarketing (http://tinyurl.com/8c5lc6 – 2008-12-31 18:51:04)
  • ronhekier (http://tinyurl.com/8c5lc6 - 2008-12-31 18:52:17)
  • SimonFord (http://tinyurl.com/7lmjsv – 2008-12-31 18:56:42)
  • ZnaTrainer (http://ping.fm/w6Fk7 – 2008-12-31 19:06:35)
  • jimsharp (http://tinyurl.com/8c5lc6 – 2008-12-31 19:09:53)
  • thomaspower (http://ff.im/-s1Dm – 2008-12-31 19:21:02)
  • marykw (http://ping.fm/w6Fk7 – 2008-12-31 19:24:58)
  • ducttape (http://fleck.com/BBA3L - 2008-12-31 19:43:01)
  • SuzanneBHarris (http://fleck.com/BBA3L – 2008-12-31 19:47:14)
  • eseiberling (http://fleck.com/BBA3L – 2008-12-31 19:47:27)
  • TheVCF (http://fleck.com/BBA3L – 2008-12-31 19:47:55)
  • redkitedesign (http://fleck.com/BBA3L – 2008-12-31 19:50:15)
  • PageSage (http://fleck.com/BBA3L – 2008-12-31 19:50:51)
  • jeaniemarshall (http://fleck.com/BBA3L – 2008-12-31 19:51:31)
  • ParrotGuy (http://fleck.com/BBA3L – 2008-12-31 19:52:21)
  • axsystechgroup (http://fleck.com/BBA3L – 2008-12-31 19:54:32)
  • silentbutsmart (http://fleck.com/BBA3L – 2008-12-31 19:54:40)
  • lilyhill (http://tinyurl.com/7lmjsv – 2008-12-31 20:10:34)
  • dhelbig (http://fleck.com/BBA3L – 2008-12-31 20:22:10)
  • TheTeliosGroup (http://fleck.com/BBA3L – 2008-12-31 20:23:14)
  • TommOH (http://is.gd/eiOS – 2008-12-31 20:27:58)
  • TweetThinking (http://tinyurl.com/Twitter4Biz - 2008-12-31 20:29:08)
  • appirio_kirk (http://fleck.com/BBA3L - 2008-12-31 20:33:20)
  • stevebuttry (http://bit.ly/UDXw – 2008-12-31 20:35:03)
  • lizstrauss (http://is.gd/eiOS – 2008-12-31 20:35:38)
  • heathermilligan (http://is.gd/eiOS – 2008-12-31 20:36:11)
  • soultravelers3 (http://is.gd/eiOS – 2008-12-31 20:36:46)
  • jerryroberts (http://is.gd/eiOS – 2008-12-31 20:38:20)
  • roylan (http://tinyurl.com/8c5lc6 – 2008-12-31 20:38:25)
  • brentrinehart (http://tinyurl.com/8c5lc6 – 2008-12-31 20:38:38)
  • LynnMcFarlane (http://tinyurl.com/8c5lc6 – 2008-12-31 20:39:34)
  • bmckay (http://tinyurl.com/8c5lc6 – 2008-12-31 20:40:25)
  • AnnFeinstein (http://is.gd/eiOS – 2008-12-31 20:42:08)
  • timage (http://is.gd/eiOS – 2008-12-31 20:43:56)
  • witchlinks (http://tinyurl.com/7lmjsv – 2008-12-31 20:45:30)
  • franswaa (http://is.gd/eiOS – 2008-12-31 20:46:53)
  • EGoddess (http://is.gd/eiOS – 2008-12-31 20:47:48)
  • franswaa (http://ff.im/-s4fF – 2008-12-31 20:48:28)
  • michellerafter (http://is.gd/eiOS – 2008-12-31 20:50:15)
  • lisahickey (http://ping.fm/w6Fk7 – 2008-12-31 20:52:56)
  • ChadALevitt (http://tinyurl.com/8c5lc6 – 2008-12-31 20:54:16)
  • techandlife (http://tinyurl.com/Twitter4Biz – 2008-12-31 20:56:15)
  • damphoux (http://tinyurl.com/8c5lc6 – 2008-12-31 20:57:04)
  • trishlambert (http://is.gd/eiOS – 2008-12-31 21:01:17)
  • cocoagal (http://bit.ly/UDXw – 2008-12-31 21:02:30)
  • NewEvolution (http://tinyurl.com/8c5lc6 – 2008-12-31 21:03:29)
  • shinils (http://tinyurl.com/8c5lc6 – 2008-12-31 21:05:04)
  • brettbittner (http://fleck.com/BBA3L – 2008-12-31 21:10:48)
  • twittea (http://twurl.nl/wmjiuv – 2008-12-31 21:11:26)
  • twittea (http://twurl.nl/c1y6ur – 2008-12-31 21:11:27)
  • nicknanton (http://tinyurl.com/8c5lc6 – 2008-12-31 21:20:53)
  • ZaTaylor (http://is.gd/eiOS – 2008-12-31 21:23:30)
  • poneal (http://tinyurl.com/8c5lc6 – 2008-12-31 21:35:34)
  • TracyOConnor (http://is.gd/eiOS – 2008-12-31 21:35:59)
  • Net2 (http://tinyurl.com/8c5lc6 – 2008-12-31 21:50:21)
  • brianjcarroll (http://tinyurl.com/8c5lc6 – 2008-12-31 21:50:40)
  • blogsir (http://tinyurl.com/8c5lc6 – 2008-12-31 21:52:11)
  • JesseNewhart (http://bit.ly/UDXw – 2008-12-31 21:54:42)
  • integrateit (http://tinyurl.com/8c5lc6 – 2008-12-31 21:57:41)
  • angelcaido666x (http://bit.ly/UDXw – 2008-12-31 22:00:08)
  • Rick__S (http://bit.ly/UDXw – 2008-12-31 22:19:16)
  • mgco (http://is.gd/eiOS – 2008-12-31 23:48:40)
  • dickmansfield (http://is.gd/eiOS – 2008-12-31 23:52:16)
  • indyfromoz (http://tinyurl.com/8c5lc6 – 2009-01-01 00:36:15)
  • CindyKing (http://is.gd/eiOS – 2009-01-01 00:48:32)
  • namtrok (http://tinyurl.com/Twitter4Biz – 2009-01-01 01:52:43)
  • MicheleTune (http://tinyurl.com/8c5lc6 – 2009-01-01 03:51:29)
  • Aaron111 (http://tinyurl.com/8c5lc6 – 2009-01-01 05:47:46)
  • judithstephens (http://bit.ly/UDXw – 2009-01-01 05:56:58)
  • pincock (http://tinyurl.com/8c5lc6 – 2009-01-01 07:59:58)
  • Mike_Stelzner (http://tinyurl.com/8c5lc6 – 2009-01-01 08:03:05)
  • Phil_Adams (http://tinyurl.com/8c5lc6 - 2009-01-01 09:38:16)
  • problogger (http://tinyurl.com/8c5lc6 – 2009-01-01 11:03:06)
  • jamesramya (http://tinyurl.com/8c5lc6 – 2009-01-01 11:04:36)
  • RicRaftis (http://tinyurl.com/8c5lc6 – 2009-01-01 11:08:23)
  • CeciliaEdwards (http://tinyurl.com/8c5lc6 – 2009-01-01 11:19:30)
  • CindyKing (http://tinyurl.com/8c5lc6 – 2009-01-01 11:20:08)
  • RicRaftis (http://bit.ly/UDXw – 2009-01-01 11:23:44)
  • gideonshalwick (http://tinyurl.com/8c5lc6 – 2009-01-01 11:48:58)
  • TwitLinksRSS (http://tinyurl.com/8c5lc6 – 2009-01-01 11:49:29)
  • ChristinePilch (http://tinyurl.com/8c5lc6 – 2009-01-01 13:14:39)
  • neuralmarket (http://tinyurl.com/8c5lc6 – 2009-01-01 13:29:49)
  • laretal (http://ping.fm/w6Fk7 – 2009-01-01 13:31:39)
  • jbeardsley (http://bit.ly/UDXw – 2009-01-01 13:54:13)
  • imawriter (http://ping.fm/w6Fk7 – 2009-01-01 14:36:15)
  • divitodesign (http://tinyurl.com/8c5lc6 – 2009-01-01 15:07:50)
  • stelzner (http://tinyurl.com/8c5lc6 – 2009-01-01 16:06:36)
  • natthedem (http://is.gd/eiOS – 2009-01-01 16:28:04)
  • amrithallan (http://tinyurl.com/8c5lc6 – 2009-01-01 16:36:53)
  • alanlhammond (http://tinyurl.com/8c5lc6 – 2009-01-01 16:40:34)
  • _McLaughlin (http://tinyurl.com/8c5lc6 – 2009-01-01 16:52:09)
  • beulahgg (http://tinyurl.com/8c5lc6 – 2009-01-01 17:12:38)
  • rebeccacoleman (http://tinyurl.com/8c5lc6 – 2009-01-01 17:26:01)
  • Rwilliard (http://tinyurl.com/8c5lc6 – 2009-01-01 17:29:30)
  • BIMarcom (http://tinyurl.com/8c5lc6 – 2009-01-01 20:01:17)
  • Terri_Rylander (http://tinyurl.com/8c5lc6 – 2009-01-01 20:01:27)
  • debbiestier (http://tinyurl.com/8c5lc6 – 2009-01-01 20:32:03)
  • SternalPR (http://tinyurl.com/8c5lc6 – 2009-01-01 20:33:12)

* I’m getting the first 100 search results for each URL, which means that for each “shrunken” url with 100 or fewer results, they are comprehensive.  However, there’s no way to know, short of looking at the entire Twitter timeline and resolving all the URLs, if there are more people citing the same page.  I’m planning to use the words associated with the URLs to do further searches to discover more people who cited popular pages.

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31 Dec 08 Twitter: Massively parallel self-organizing points of view

There is no shortage of speculation about Twitter’s future, particularly its business model.  Just search on monetize Twitter.   The company’s site describes it as “a real-time short messaging service that works over multiple networks and devices.”  Snore.  Also this: The idea arose when “Jack Dorsey had grown interested in the simple idea of being able to know what his friends were doing.”  Less of a snore, but not a business, but a focus.  The companyalso describes itself this way: “Twitter solves information overload by changing expectations traditionally associated with online communication.”  On track, but still very broad.

Please realize that none of that was criticism.  I’ve been a founder, manager and advisor to many startups over the last 20 years, which led me to think quite a bit about the natural tension between creative invention and focused follow-through.  Solving a broad problem often drives popular new technology at first.  The challenge Twitter faces, like any startup that gets this far, is to find a market focus.  This is hard and relatively rare for startups because the kind of people who are good at inventing stuff are usually unsatisfied, often bored, at using their inventions in just one or two markets when they can see dozens.  However, a market focus is almost always essential to success.  Or as Paul Dali once said memorably, the five most important things for a startup are focus, focus, focus, distribution and focus.  So, Twitter, where to focus?

The old saw in developing a business plan is to ask yourself what business you are in.  Twitter has some interested uses that are probably not revenue makers.  For example, knowing that my friend Dale is making goat cheese is interesting, I’m not at all sure there’s revenue there.  Let me be clear – I have a deep appreciation for the non-commercial value of the Internet, the gift economy in which we are all collaborators.  But it doesn’t pay the electric bill.

What business is Twitter in?  As the headline above says, I think Twitter’s revenue business will arise from the part of it that is a people-driven, massively parallel headline organizer. It helps me learn things that interest people who I think are interesting.  The people I follow are people who I choose to allow to influence what I read; they are people who have interesting points of view.  Not interesting facts.  I can usually uncover facts without much trouble.  Developing tools that help people find valuable points of view is much harder and far more, well, exciting.  It has been the goal of much of my career.

Ten years ago, Jakob Nielsen wrote a piece on “microcontent,” with arguments for why web  headlines, page titles and subject lines should not be written like newspaper headlines.  His advice is mostly on target for Twitter, but with one big difference – Twitter headlines come in the context of somebody’s point of view.  It’s not just anybody’s point of view, but somebody who Twitters often enough, interestingly enough, but not too often or too dull, so that you’re willing to follow them.  

I am reminded of a conversation I had a couple of years ago (about the war in Iraq, where a member of my extended family was killed in action) with Mike Honda, my representative in Congress.  Mike said that he doesn’t meet with constituents to get information.  He has a staff for that.  They can dig up just about any information he wants.  Constituents give him something more valuable – stories that energize him to go back to Washington and keep working despite all the obstacles and the temptation to do something that pays better and yields faster results.

If Google is like my research staff, Twitter is like my constituency… but the metaphor breaks when you consider that the U.S. government is democratic, but the Internet is as different from democracy as democracy is different from a monarchy.  A democracy is self-regulating, the Internet goes one step further – it is self-organizing, so there is no equivalent of a member of Congress.  We are all each others’ research staff and constituencies.

When I decide who to follow on Twitter, I’m looking for strong points of view.  The words “check out” are a big ho-hum, but they sure are heavily used.  Please ban “check out” from your Twitter vocabulary.  Show me, don’t tell me, as every writing instructor says.  I promise to do my best to write tweets that reveal more than just facts.

Looking through my recent followees, here’s a tweet that really works for me, from Tim O’Reilly (who keeps popping up in my viewfinder as somebody who uses Twitter well).  It works because it starts with the word “Love,” so I know there’s a strong opinion there.  It also mentions another Twitterer I’ve heard of, which helps raise my interest.

I’m not sure how hashtags fit into this model.  They represent a number of peoples’ point of view of categorization by topic.  Although I am a fan of topical organization, I automatically think of trees and directed graphs, yet hashtags are one-dimensional.  I don’t know if that works.  Structure can be implicit in tags, but that’s hard even when each message has several tags and there isn’t room for that in Twitter.  Perhaps out-of-band tagging would work better.  The only hashtag that I have really appreciated was #svtweetup, which got me to a pretty good face-to-face networking event.

Why organize points of view?  I see these benefits:

  • Helps keep me informed about the hot topics and buzz so that I’m staying current.
  • Is a “serendipity engine” that leads me to find things I didn’t know to look for.
  • Stimulates creativity by helping me see the same old stuff in new ways.

Of course, it doesn’t have to be Twitter that does this.  But of everything that the Internet has spawned, Twitter seems to have the most potential.  As I work with the APIs and data, I’m starting to get some ideas about where revenue might be, but I’ll save that until I’ve done a bit more work with it.

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Filed in Influence, Patterns, social change with Comments

30 Dec 08 Influencing and being influenced; tracking topics on Twitter

One of the most common naive errors in statistics is to confuse correlation with causality.  Common sense tries to tell us that when two events co-occur, the first one is causing the second to happen (which often is the case).  Red traffic lights  correlate to cars stopping and sure enough, we know that red lights cause (most) people to stop.  But sometimes things correlate because a third, external mechanism is influencing them.  Drownings increase as ice cream sales rise, but ice cream isn’t causing drownings.  The external factor is summer, of course.

This is on my mind because over the last few days, when cold medicine hasn’t fogged my brain up so much that I couldn’t think, or at least couldn’t think logically, I’ve been working with the Twitter APIs to see what I could come up with in terms of tracking topics as they move around on Twitter.   I’m attracted to Twitter because its immediacy and brevity make it relatively easy to analyze.

Eventually, what I hope to do is find useful patterns in the interplay of words, Twitter screen names, URLs cited and hashtags (and any other entities that could be extracted).  I’m focusing first on URLs, since they are sort of the “stories behind the headlines” on Twitter.  My friend Dave Land this morning mentioned that writing a tweet is like writing a headline.  If so, then the cited URLs in those tweets are like the stories behind the headlines.  

I’ve put Python and SQL to work scraping statuses from Twitter, pulling out word pairs (I’m planning to analyze them with the other entities via LSA), screen names and URLs.  I’m resolving all the little URLs to the pages they actually point to, since Twitter users, limited to 140 characters, frequently use services like TinyURL to shorten them, but I want to see when people are citing the same URLs even if the shrunken URLs are different.  In fact, looking at the ratio of shrunken URLs to actual URLs is interesting – if it is high, that means that a lot of people are finding the cited page independently, rather than retweeting it or getting it from the same source external to Twitter.

As I find cited URLs, I’m using Twitter’s search API to get the most recent mentions of them, then storing the identities of the users who also cited them and when they did so.  That gives me a timeline of URL citations.  I’m not tracking explicit retweets, so I don’t know if the first people to cite a URL first are more influential or not.

I haven’t asked Twitter to white-list me yet, so I’m working within the limitations of their API – 100 requests per hour.  That forces me to be as smart as possible about how my code explores the data.  I started by choosing somebody who has a decent number of followers, but not too many, so that it wouldn’t take too long to scrape the person’s followers’ tweets.  I chose Tim O’Reilly because I suspect he is fairly influential on Twitter and we’ve had some conversations that go back to the mid-90s about how to figure out “what the Internet is thinking today.”  

O’Reilly’s company was one of the first, if not the very first, to measure social media for market research.  Many years ago, they were scraping Usenet to help decide which technologies would make good topics for books.  I recall that one of the first decisions they made from that data was to choose between two open-source database projects, MySQL and mSQL.  They chose MySQL… and therein lies another reminder of causality v. correlation.  Did MySQL succeed because O’Reilly chose to focus on it, or did O’Reilly succeed because it chose the right books to publish?  There is no way of knowing, but I have personal evidence that O’Reilly doesn’t always choose the right topics… or perhaps the right authors.  That’s a story for another day.

After a lot of wrangling with Python, MySQL and technical issues having to do with Unicode and my inability to write a correlated subquery under the influence of Sudafed, I have something working.  It started by scraping Tim’s recent tweets and then searched for people who also cited the same URLs.  Then it explores those who it has found cite the greatest number of URLs overall.  It adds 10 people at a time and then re-ranks to see who it should explore next.

I’ve been running this for a couple of days now in various forms.  So far I have found about 7,000 unique URLs.  Only about 300 of them are duplicates – different shrunken URLs for the same page.  The URLs have been mentioned about 20,000 times by 7,500 users.  I have found about 40,000 two-word phrases (stop words, URLs, screen names and hashtags are excluded) and 52,000 mentions of those phrases, which means that a number of phrases are being used by multiple people.

What the heck, here are the top phrases and the number of Twitters (Twitterers? Twits?) who used them over the last few days (remember, this is far from comprehensive):

  • check out 52
  • blog post 47
  • New Year 36
  • social media 34
  • New blog 33
  • new years 29
  • New York 25
  • ice storm 24
  • sad true 23
  • emergency generator 23
  • about attitudes 22
  • gas tax 21
  • mornings paper 19
  • Attention Influence 19
  • one best 19
  • Equal Authority 19
  • prices people 18
  • Jeff Jarvis 18
  • Its morning 18

I suspect that the words “check out” on Twitter are much like the words “click here” were in the early days of the web.  ”Emergency generator” intrigued me, so I linked it above to Twitter search.  Hint: it has to do with the Toyota Prius.  I suspect those same people included a link to a New York Times article about it.  Interestingly, a number of the people who cited it were not retweeting (at least not explicitly)… but many of them were using a shrunken URL cited by – guess who – Tim O’Reilly.  The interesting thing about the popularity of the phrase is that it gives my code a way to discover the other shrunken URLs in a single search, instead of having to scrape everything and resolve every shrunken URL to the actual page.  Tim may or may not have influenced those people to look at the article, but it is clear that he is in tune with a topic that people are interested in, which makes him interesting, whether he is an influencer or just well-influenced, so to speak.

Time to publish this post, I guess, even though I’m tempted to wait until today’s cold medicine has worn off to proofread it one more time.

More results here as I come up with them.

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Filed in Influence, Patterns, statistics with Comments

22 Dec 08 Influence measurement on Twitter

Social media proponents (a/k/a people who want your money for social media products and services) urge us many ways, but it all boils down to “People are talking about you on the Internet, so you’d better pay attention.” Tapping into the swelling ground, grabbing a long tail or otherwise engaging in social media is supposed to help you make better products, faster, leading to happier customers and more money. But how do you decide who to listen to?

Success generates noise. Millions of customers means millions of comments. The first and easiest answer to this dilemma, which may be good enough – for now – is to figure out which comments are most popular. See which ones are getting the most page views, the most Diggs, Tweets or other indicators that somebody cares.

The problem with that approach is that by the time something is popular, it is often too late do so anything about it. This is particularly true in two situations:

  • You are in an industry where “hits” generate the profits and are short-lived. Unless you’re growing corn, it seems like all products increasingly fit this description. Maybe corn, too.
  • Your customers are angry and unhappy.

If you have a hit on your hands and you find out too late to create more inventory before the crowd has moved on, you missed an opportunity. There’s a corollary to this: the sooner you find out you have a dud, the faster you can stop wasting resources on it.

The really unfortunate thing about angry and unhappy people is that they consistently have more energy to invest in bad-mouthing than happy people have for paying compliments. That’s a well-known fact of marketing. And life. By the time grumpiness about you and your stuff becomes popular, a lot of damage has been done, obviously. Knowing who is influential can help you prevent grumpiness in the first place or do better at quelling it before it becomes popular opinion.

In mass media, the important metrics focus on popularity. Although popularity still matters, digital social networking allows us to measure influence, at least indirectly. The difference between being popular and being influential is very simple to understand in principle.

If I have 5,000 followers on Twitter, I’m obviously fairly popular. I probably am also influential. There are ways to figure that out, such as by measuring how much interaction I engage in or how many times my tweets are “re-tweeted.”

If I have one follower, does that mean I am not influential? Not if that one follower is a fellow named Barack Obama, who has more than 150,000 followers, according to Twitterholic. That is, if that Obama fellow really is following me. I mean really following me, the way we mean “follow” in the real world.

In other words, a few influential followers can be far more significant than thousands with limited influence.

I’m using Twitter as an example because I’ve been working with the Twitter APIs to see how hard it would be to come up with measures of influence. Twitter is growing on me and I think that part of the reason is the language it uses.

  • Blogs and RSS have readers; Twitter has followers.
  • Forums, blogs, mailing lists and such have posts or messages; Twitter has updates.
  • Blogs have feeds; Twitter has APIs.

From a social network analysis standpoint, Twitter is much easier to deal with. Mostly.

Popularity is a first-order measurement. My popularity on Twitter is the number of followers I have. Influence is a second-order or greater measurement. The simplest measure of potential influence is to see how many followers my followers have. The API makes this very easy to measure (for people who aren’t so popular that the API limits become an obstacle).

My Twitter followers are followed by a bit more than 10,000 people. Pretty good, I think, since I only have 28 followers (I haven’t been on Twitter long).

My friend Dave Land has 95 followers and those people are followed by almost 175,000 others. Wow. Dave’s followers are followed by a lot more people than mine are.

Some of my followers are people I believe are influential in the world of web analytics. Let’s see how they do (a third-order measurement of my potential influence, if I did it for all of them). In no particular order:

  • Anil Batra: 232 followers, who are followed by 235,000 others.
  • Eric T. Peterson: 689 followers, who are followed by 373,000 others.
  • June Dershowitz: 266 followers, who are followed by 122,000 others.
  • Marshall Sponder: 839 followers, who are followed by 727,000 others.

Avinash Kaushik, Google’s web analytics evangelist, isn’t following me (hey, bub!), but anybody whose title is “evangelist” is supposed to be influential. At the risk of exceeding the Twitter API limits, I ran my gizmo to get his stats. Avinash has about 2,000 followers, who are followed by almost 600,000 others.

If you rank these people by popularity (followers), Avinash is No. 1, hands-down. But if you rank by potential influence, Marshall Sponder’s followers are followed by the most people, which is especially surprising given that Avinash appears to be more than twice as popular.

Dave Land comes in at No. 1 when this group is ranked by the ratio of second-order followers to followers. That means he is doing the best job of attracting followers who attract followers, which is what you need to do if you want your influence to scale beyond immediate popularity. But I should note that having a lot of followers will inevitably dilute your second-order influence, which should comfort Avinash, who came in last on that measurement (thereby saving me from last place in all three rankings).

I should note a messy bit of this measurement – sites like Woot, Twemes, hashtags.org and others that automatically follow you when you follow them. Ugh. I haven’t figured out a good way to exclude them, so I’m just doing it manually… and I haven’t thoroughly made sure I caught all of them. So there’s hope, Avinash – maybe Marshall is just signed up for more of those. In any case, don’t take these numbers too seriously. I’m going to work on some additional data points – number of replies and such, to strengthen the results.

Or can somebody save me this work and point to a site that has already done this sort of analysis? I searched but didn’t see anybody looking at second-order popularity.

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Filed in Influence with Comments

19 Dec 08 Better Twitter-WordPress integration here now

I have switched from hashtags.org to Twemes for the Twitter tag feed for web analytics (#wa) that appears in the sidebar. Sorry, hashtags, but you just weren’t reliable.

I have also added a Flash widget for Twitter, showing my tweets, to the top of the sidebar. It’s kind of flashy, so I might switch to the text version.

Finally, I have added TwitThis (shouldn’t that be “TweetThis”?) at the bottom of each post, allowing you, my fine readers, to Tweet, with ease, I hope, any post you choose.

Ah, the joy of actually using social media!

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