I’ve figured out an easy way to get at interesting data. First I fire up the tweet nabber written in ruby, then run this cheap piece of crap and extract only time data from tweets pulled.

After getting the time data, I open the *.csv in Minitab and create a histogram with it. I then have the frequency with which a person tweets over 3k tweets by time, so I can approximate when they’re most active throughout the day based on my relative time zone.

Awesome.

I hope to figure out more interesting ways to analyze data to garner information like this.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  require 'csv' require 'time' require 'tzinfo' fil = ARGV[0] stuff = Array.new tweets = CSV.read(fil) tweets.each do |x| if x[1] != "created_at" and x[3].start_with?("RT @") == false then times = Time.parse(x[1].to_s) shift = times.getlocal("-05:00") stuff << "#{shift.hour}:#{shift.min}:#{shift.sec}" end end puts stuff

So basically:

1. Fire up tweet scraper.
2. Parse CSV with this script.
3. Import (HH:MM:SS) in Minitab and get a histogram like this:

Simple.

Note: I’ve edited this thing like eighty times. This last iteration filters retweets out, since I’m pretty sure we don’t need that polluting our data. Now you should have pure origin tweets to discern when people are most active. Shift your time zone accordingly, and I’m sure you’ll be able to effectively stalk twitter users in no time! DOXDOXDOXDOXDOXDOX