Oil Spill vs. Arizona II: Location Analysis of Tweets

The Arizona Immigration Law and the Gulf Oil Spill have been among the biggest news stories this month. We’re tracking tweets on an ongoing basis for both stories.
We had a suggestion to map these Tweets by location (thanks to @hornOKplease!). Because the stories have a regional focus and national attention, it seemed like the perfect analysis. We took a sample of ~5,000 tweets for each story and used our handy location normalization feature to gather city, state, county and latitude longitude for the tweets. Take a look at these cool charts…

Chart Details

Tweets are mapped by latitude and longitude. Each point on the map represents at least 1 Tweet. The size of the bubble reflects the number of Tweets from each location from our sample. In Both Charts, the purple shaded background overlay provides population by county. The darker shading reflects higher populations.

Take-Aways:

  • Both stories appeared to reflect the regional focus. The Arizona story had greater volume in the Southwest, and border states. The Gulf Oil Spill discussions had a clear concentration in Gulf states such as Louisiana, Mississippi, and Florida.
  • The Gulf Oil Spill seems to have garnered broader national attention than the Arizona immigration story. The Arizona activity is more heavily concentrated in large cities. The Oil Spill also had high volume in major cities, but appeared to have more distributed activity in smaller markets relatively speaking.
  • Major media centers seems to consistently drive high volume. When it comes to news, Los Angeles and New York may be the center of the Twitterverse rather than San Francisco.

How to Run Your Own Tweet Mapping Analysis:

We used RowFeeder Enterprise to capture the Twitter data in Excel. We had the location normalization feature turned on. The mapping is done by running the RowFeeder Excel output into Tableau. Just a couple clicks in Tableau and the maps are done!

  • Categories: News, Politics

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  • http://www.duomark.com/ jay

    A 2D bar graph showing number of tweets on the left with cities from left to right by population would highlight bumps in the distribution. Use a background shaded distribution of normal expected number of tweets as a percentage of population to highlight how the tweets vary from noise. Provides a summary key to accompany the map.

  • http://twitter.com/HOTSMART Juan

    #Arizona now threatens L.A. Boycott Arizona