St. Lawrence University
Brett McCormack

Professor Barnard

Twitter & Society


Method 3: Content Analysis


    When I was thinking about the search parameters for Method 3, I realized that I wanted to learn more about the first couple months following the shooting of Michael Brown. I wanted to somewhat organize the chaos that unfolded in this time. A hearty basket of emotion without a doubt, I selected the dates August 9th, 2014 - October 9th, 2014. I used the filter “journalist OR activist OR media” in my search. For my codes, I created the tags “Media Treatment” and “Public Treatment”. Additionally, I selected the filter of only seeing images. I did this because I wanted to see if the media and their safety was getting tweeted about more than the public's or vice versa. I had 166 total results from this search.

    Media treatment had 31 tags, public treatment had 9 tags, and both had 8 tags. I tagged a tweet as “Media Treatment” if it depicted meta-discourse and media biased behavior or actions. I tagged a tweet as “Public Treatment” if it was critical of the media or showed/talked about protesters. I tagged a tweet as “Media Treatment” and “Public Treatment” if it described actions by both parties involved. I used these codes because I wanted to see if the journalists/activists tweeted more about the protestors/public or the media.

    I am pretty confident in the use of my codes. The only thing that I am a little unsure about is the tweets that are tagged with both of the codes. It was difficult when the tweets mentioned the media and the public, as I couldn't decipher which code was more applicable, so I tagged with both.

    A pattern that arose from the data was the more than 3x the amount of tweets being about the media than the public. My explanation for the bias towards media based tweets is that media behavior is more readily captured and posted about than public/protest behavior. I don’t know if this is exactly true or not, but it is what I believe to be true. Other than the huge bias, I did not really see any other patterns.

    My initial impression of the data was that it was going to be mostly public/protest based. As it turns out, most of the data was on the media and their actions. I had thought that since protests usually get shown by tv stations that social media would have the same trend. Most of the tweets were about how the media was treated by police rather than how the public was treated. For example, @alanblinder tweeted “It’s not hard to find one of these on the pavement of what was going to be a media area in #Ferguson” with a picture of a rubber bullet. Instead of describing how the public may have been affected by the bullets, he talked about the potential danger that the media faced.