Thursday, July 30, 2015

Text Mining Lafayette Shooting: Followup

Last week I posted some topic modeling results about the immediate twitter reaction to the Lafayette shooting (post found here).  I thought it would be interesting to revisit the data a week later, and see if the conversation has changed.  Short post here, but some interesting findings.

First the wordcloud:

Lots of "gun" and "control" talk.  But also the word "argument" .. and something about a comedian named Jim Jeffries.

Interesting, but what do the actual models look like?  This time, it split into fewer topics:

To summarize the topics:

  1. Focused on gun control directly.  With a lot of tweets linking to a video of a comedian named Jim Jeffries "destroying" arguments against gun control.
  2. Focused on facts of the case.  Specifically related to the injured and still living victims.
  3. Focused on gun control.  A lot of demands on republican candidates to refuse donations from the NRA.
  4. Focused on gun control generally.   Many tweets using the hashtag "#gunsense"
  5. Focused on the victims that died.  Specifically related to the two women, funerals, and other facts.

And finally by frequency:

So a few conclusive points:
  • About a a week after the shooting, 70% of tweets still using the hashtag relate to gun control topics.  This is roughly double the proportion from immediately after the shooting.
  • One thing missing is Zayn Malik.  And anything grossly off topic in general, some good news.  It appears that once a hashtag is no longer trending, spammers stop using it.
  • To close out, here is something interesting to look at.  The tweet that "ranks" (has highest probability of association) with each topic:

Topic 1:

Topic 2:

Topic 3:

Topic 4 (This is cool, because it show how topic modeling groups together "unobserved" topics.  This isn't explicitly saying gun control, but it certainly is a gun control related tweet)

Topic 5:

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