BlackHat according to Twitter

For the first time in a decade I didn’t attend BlackHat USA in Las Vegas. I learned that South Africa in August is much colder than i recalled, but also had the chance to observe the conference from through a twitter-lense.

It seemed as if there was more talk about parties, than content so I decided to grab all the tweets i could (#blackhat through the twitter search API) to do some simple grouping*.
Whats clear straight off is that my intuition was wrong. Although party talk makes up a significant percent of all tweets, tweets about “talks & training” clearly dominate. (This possibly means that i need to start following a better class of hax0r)

A quick explanation of the grouping (which was done pretty coarsely):
  • Talks & Training : Tweets related to a talk (or training session)
  • Misc : (General catch-all for tweets about coffee / *)
  • Spam : People who stole the hashtag to push traffic to their own site (used by quite a few big name vendors to draw traffic to their reports *cough* shady rat *cough*
  • Pimpage : Speakers / Vendors / People shamelessly self promoting
  • Vegas/Parties/Social : This are the typical “Vegas Baby!” tweets
  • Bluehat Prize : This are tweets about the Microsoft Bluehat prize
  • Not There : Tweets from people who wish they were at BlackHat
  • Recruitment : erm.. recruitment related
  • Pwnies : pwnies related tweets
  • BoothBabes : the kerfuffle over McAfees use of booth babes
  • anonymous,antisec,lulzsec : Tweets about Anon doing BlackHat

Since we have this data, we can extract some other (arb) pieces of information like:

Most commonly used words in tweets about “talks & training”

(this is a quick (cheap) way for us to see which talks /speakers dominated the twittersphere)
It also (kinda) interestingly allows us to list the top tweeters by volume (with 1318 individual tweeters in total):
  • 36 @TechJournalist
  • 32 @wireheadlance
  • 30 @chriseng
  • 25 @jadedsecurity
  • 23 @IOActive
  • 21 @Llana
  • 18 @click_finders
  • 18 @cindyv
  • 18 @bdognet
  • 18 @InsiderThreats
Finally (because we couldn’t help but add another pie graph,) we can check the most popular twitter clients used to create this traffic:

(Its worth noting that we only grabbed data for the #blackhat hashtag. This is in part because it was most obvious, and in part because we were afraid to grab the results of #barcon)
You should follow me on twitter: here
* We made use of the python twitter module. You can download a python pickle object here, which is a dictionary of all tweets snagged.

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