St. Lawrence University
Emily Balter

Question: During the week that Michael Brown was killed in Ferguson, how do the tweets written by journalists differ from the tweets written by activists? When filtering in the key words Protestors OR Police OR Media, who is saying what? Which tweets are more affective or objective? Who is using more video/visuals? Who is telling the truth?

For my search, I decided to filter in the key words, Protestors OR Police OR Media, because I was curious to find out how activists and journalists originally tweeted about these topics the week immediately following the death of Michael Brown. I was interested in seeing how activists and journalists responded and reacted to the police, media, and protestors using media or only text. I labeled most tweets as either objective or affective because much of the information was fact or opinion. I also used the codes media criticism to tag tweets that directly mentioned or criticized the media for being untruthful, and meta-discourse to tag tweets by journalists reporting about their own reporting.

Although most of my tweets fit into the already established codes, there were tweets that I questioned what code to use. For example, one activist tweeted “Tearing up highway 70 so I can get home to stand in solidarity w my people. PM meeting @ # Ferguson police dept w @ ReverendNupe & others” (activist, 8/10/14). This tweet could be objective since it specified where a meeting is and with who. However, this tweet also represents the personal experience of this activist, who was going to be participating in the protests. Therefore, I created the tag “active protestors” to distinguish who was actively participating in the protests. These active protestors were different from those who were passively observing or tuning into the action.

I also noticed a trend of activists expressing anger towards the police, and the violence the police perpetuated. Armed police were invading peaceful protests. From this observation, I made a tag called “anger at police”. Additionally, I found tweets with quotation marks difficult to interpret. The tweets were objective because they quoted exactly what protestors or police said. However, in some cases, it was unknown if the original source was in fact objective. For example, one activist tweeted with quotation marks “This isn’t a black or white issue. This is a POLICE BRUTALITY ISSUE” (8/11/14). On this one tweet, I do not know who originally said this piece of information. The activist who tweeted this is objective because she quoted these exact words, but the original source is affective as the tweet expresses an opinion towards the Ferguson events. As a result of this confusion, I created the tag “direct quotation” to highlight the tweets that were quoted from outside sources beyond the original tweeter.

In terms of overall patters in the data set, I observed that a substantial number of journalists posted about events happening in the exact moment of the tweet, providing live updates to the public on the protests and police. Many provided live photos and videos of the protests, armed police, and scene where Michael Brown was shot. They also informed the local public about roads that were closed due to protests. Activists who were participating in the action also tweeted about the real- time action of where and when protests were taking place, or about the information they believed to be true. A few activists did post affective responses in reaction to the police or institutionalized racism. The activist, Antonio French had an influential voice in my data set, and used many images and video to portray the live action. From these patterns, it can be concluded that Ferguson journalists tweeted to inform the public with live details of the action. Activists, specifically those who were participating in the protests also had an objective to inform the public. Activists were more likely than journalists to post affective tweets, expressing their emotion and opinion towards the police or the media. Both activists and journalists used image and video to visually portray Ferguson to their audiences. Affective tweets typically did not use image or video.

From my initial impression of the data, I assumed that most tweets were objective real- time coverage of the events occurring in Ferguson. After looking more in depth at the data, I observed interesting tweets and trends, which lead me to create original tags. Tagging helped me categorize tweets, and interpret each piece of information. Not all tweets fit into the clear categories of objective and affective, as a few were in between. Fact, opinion, and emotion are not always distinguishable.