My focus for Method 3 was how the media, specifically journalists, portrayed law enforcement in the 48 hours after Michael Brown was fatally shot by police officer Darren Wilson. Therefore, the dates I selected were August 9th to August 11th, in which I filtered my search to only collect original posts, as I wanted to view primary sources and retweets would simply give me repeated results. I further filtered my research by selecting the tag “Journalists” to view tweets that were posted only by journalists. The keywords I relied on were “cop,” “police,” and “law enforcement,” as this would give me tweets in which journalists were discussing law enforcement in some form. I created two tags to organize my data, the first being “Amanda: affective” and the second being “Amanda: objective.” Those tweets tagged objective would represent posts that were free of emotion, critique, or personal opinion, where those tagged as affective presented some form of emotion or personal reflection.

     Within the 71 tweets my search collected, I used content analysis to determine that 71 tweets were objective and 14 tweets both objective and affective. Content analysis provided me the most thorough and effective form of coding due my choice of tags. Clearly, far more journalists posted tweets that were objective versus affective. I was surprised to see that in giving each tweet a tag, I didn’t find a single tag that was solely affective. This could be due to the fact that journalists, by nature of their occupation, tend to rely on factual details when presenting news, instead of personal opinion. Of course, this cannot be said for all journalists.

     The results led me to several conclusions in regards to how journalists portrayed the police during this time. First, I noticed that the tweets I tagged as both objective and affective tended to present an image, as well as text. It was in the images that I felt the police were being portrayed in a way that could frame them as the public enemy instead of protector. In other posts, the photographs presented the image of protestor versus police, a clear divide between the two groups. In other cases, I tagged tweets as both objective and affective because the wording of the post could potentially frame the police in a negative light. These type of posts may not have originally appeared to be affective, but a closer analyzation made it difficult to view the tweets solely as objective.

     My process of coding was not difficult for me to rationalize because I had an idea of what I was looking for, but another viewer could have analyzed the data set differently than I had, depending on how they define the terms objective and affective. The results showed that journalists mainly used Twitter to circulate news, rather than reflect on the events they were covering at the time.