Chyron- Method 3 -Content Analysis

I did my research for method three on the black lives matter movement. For this I used the keywords #BlackLivesMatter, #killercops, and COPS. I wanted to analyze people reaction towards police between the day Mike Brown died and the year anniversary of his death. I learned from analyzing the tweets there seemed to be hostility towards police and unrest in the city of Ferguson. It seemed people became more engaged as more police presence showed, there were tweets that exposed the location of the police or showed them increasing in mass as protest began to gain supporters.
The codes I used were, Media Discourse, Meta-Discourse, Objective, and Affective. I am extremely confident in my use of codes and the way I tagged my data. I used these codes because these were the codes we had in our class experiment. I thought these codes were helpful in the process of tagging during the class experiment so I decided to keep them. There were some codes I thought were more helpful than others for example Objective and Affective. These two codes were used the most as I searched through the pulsar data due to most of the people tweeting being activists.
As the data and I began to scroll further down between the dates of Mike Browns murder all the way to the following year, I noticed there was a lot of hostility towards the police. There were tweets of pictures explaining how they were forming in different areas. There were tweets about how the police were arresting people and using different weapons for example tear gas. We can interpret the relationship between the police and the community on twitter had their share of differences. On another note there were tweets which were trying to stop people from black Friday shopping in different stores. Many of the tweets which claimed for these actions had the #BlackLivesMatter hashtag.
From my tagging I found I tagged more tweets as affective. This is because I determined most tweets posted were people’s opinion of situations or they were claims without supporting facts. This shows me most of the tweets in my data were most likely opinion based in attempt to show support to the #BlackLivesMatter hashtag. The results differ because I thought there would be more objective tweets or tweets that criticized the media and their reports. I did not expect to see a heavy amount of what seemed to be opinionated tweets.