Abstract:
In the events following the fatal shooting of unarmed Michael Brown, a social discourse begins to take shape on Twitter. Using aggregated Twitter data on Pulsar, this research will uncover who the major players were, how they were tweeting, and how it influenced the public. The qualitative research covers the following two weeks after the shooting in order to uncover how and why Ferguson turned into the situation it did. Looking at sentiment and viewership of tweets allowed the research to find specific examples of affective and objective tweets, and how they influenced the general public.
Introduction:
For my research project I want to take a deeper look at the ways in which the Michael Brown shooting turned into a much larger social catastrophe. To best understand any social movement you must start at the beginning in order to get the full scope of the problem. For the ensuing two weeks (August 7th –August 22nd) of time I will be analyzing the Tweets that were sent out, on a variety of different levels. I want to know who the major players were from both the journalist and the activist side of the Ferguson. Also, I want to know how information was spread, and how accurate it was. Were these Tweets affective or objective from both sides? A final aspect of my research will include exploring how the use of objective and affective tweets, from both journalists and activists, fueled a social discourse and media driven catastrophe? For my research I want to see who the major players were, how were they tweeting during the first two weeks of Ferguson? The idea that journalists can have such an influence on mass-media platforms opens up the opportunity for sentiment and emotion to take over in such a high stress event.
In essence, I want to take a look at what shape the coverage took, did it lean towards truthful events that occurred, or were they more focused on emotionally driven posts. Utilizing the timeline of events, and looking at tweets to match, seeing where and when the emotion seemed to boil over into tweets that were affective to objective, from both journalists and activists. I believe this project will be best described with pictures and media, and its association with how emotion was swayed during the initial protests. The mystery of the true events of Ferguson may never be fully known, but I think with this project I can help provide a better understanding of what happened in those two weeks.
Literature Review:
If there were to be a list recapping the top five events that happened in 2014, the shooting of Michael Brown would have to be on that list. It was not just the shooting of an unarmed black teenager in Ferguson, Missouri that seemed to be the central issue. Adding together police brutality, a sense of racism, and the numerous prior tragic events much like this one, another shooting was the straw that broke the camel’s back. Ferguson, Missouri became the center of the country’s attention for the next several months. Continuous broadcasting, protesting, and especially Tweeting was done during this time. At such a high stress event, emotion can get the best of someone and the wrong news or objective news gets displayed. In a 2013 article by Steen Steensen, he defines two types of journalism that could potentially sway the public’s view of a given event. The two terms are interaction and interactivity. Interactivity is defined as “a measure of a media’s potential ability to let the user exert an influence on the content and/or form of the mediated communication.” (Steensen 2013). The important distinction between journalists and activists had prior clear cut boarders, but now with the rise in online media forums the accessibility to be a ‘journalist’ is ever more prevalent. This is important to note because groups of activists attempted to pose as journalists during this time to fit in and not get arrested. This lack in distinction between these two groups sparked a lot of chaos and hostility, in which journalists doing their jobs were arrested. Inevitably, this sparking a string of emotionally filled tweets.
During my following research of the Ferguson discourse, I found during the events nobody was ‘safe’—from being arrested. How each arrest was handled was a different story. For example, in an article written by Brian Stelter, published nearly one week after Michael Brown was shot, he recaps the arrests of two journalists in a McDonald’s while doing their jobs. While recording police officers arresting several protesters, seemingly unprovoked, events turned south and tensions boiled over. It is important to note, “citizens and professional journalists generally have the right to record police activities” After, one of the men arrested “wrote…a police officer "in full riot gear" "purposefully banged my head against the window on the way out and sarcastically apologized" (Stelter 2014). Although the events that transpired were not peaceful, the professionalism of media outlets to be bashing police during a time of civil unrest, paints a picture of unwarranted nerves.
One way to describe the research that will be conducted is through the way of conversation analysis. This is a term best described as a way to “to analyze interactions in a social context; turns at talk in conversation, or talk-in-interaction, as it is most commonly referred to – how, for instance, agreements and disagreements are articulated, how openings and closings of conversations unfold, and how conversations are organized” (Steensen 2013). By analyzing conversations and tweets that were sent out, aggregating a list of affective and effective tweets will help me better understand the types of journalism that was going on. Conversation analysis is an important tool in “assessing the coherence, and thus the meaningfulness, of a journalist-audience interaction…” (Steensen 2013). Steensen provides a clear picture of what is needed to successfully complete a content analysis of a group of comments, in our case its tweets on Pulsar.
It should be noted that the types of conversations that are going on during Ferguson are better understood through a visual lenses. The use of media, whether it be graphs, pictures, or charts, media displaying not only the types of events that were going on, but also the scope and expanse to which they are reaching. For example, understanding the potential viewership of a given tweet is important when looking at whether or not it’s an affective or objective tweet. Community clusters, support networks, and an understanding of crowd structures will also provide a methodological approach to how some tweets are aggregated (Smith 2014). Throughout the course of the further research I will be attempting to display how emotion seemed to take over in a time in which factual journalism and activism needed to be present.
Methods:
In order to aggregate an appropriate group of Tweets, I had to set a specific group of parameters in the Pulsar platform. Using the “journo-activist and j-tweeters combined” data set, narrowed down by the dates of August 7th thru August 22nd. From a group of 47 activists and 45 journalists, I created a subsample of data to help refine my search, in order to tailor my findings better. This left me with 993 Tweets. Aggregating a large number of Tweets allows me to provide a wide range of responses over the two week period. Looking at Tweets directly following the shooting all the way through the complete discourse of media clashing with the police. After sifting through the collection of tweets, I had to decipher if a single Tweet was affective or objective. By doing this it will provide me with a more organized list of the types of tweets, along with if they came from an activist or a journalist. The method of research that I will be utilizing is qualitative data analysis, due to idea that the content of the tweet holds more weight than the amount of views it gets.
(Figure 1: Influencers Network Map)
As you can see by the network map (Figure 1), there were a few major players in the world of Twitter during this time. Of the major players, both journalists and activists were heavily involved. One journalist from the Huffington Post, Ryan J. Reilly (@ryanjreilly), seems to have the biggest circle in the data set. Of the nodes that extend from Reilly’s circle, several of them go to other journalists, but interestingly enough, several of them go towards activists. These are the Tweets that I will be looking at intently. The interaction between journalists and activists could potentially lead me to some interesting information, which was either broadcasted for the world to see, or swept under the carpet due to the plight of the media. Another interesting finding I have drawn from the network map is that a majority of the influencers, both journalist and activist, had more than one node extending from it. In many of the other more specific data sets the ‘one way’ users are more prevalent. This is important to note because it will help me further know the inner workings of who was talking with how, and how that information could get passed along in a wider manner. I think a visual representation of the scope of this social movement will help paint a fuller picture of the events that transpired in those first two weeks.
Findings:
With a focus on the first two weeks of Tweets after Ferguson, several results could be drawn upon this topic. At this time in Ferguson, floods of news agencies were pouring into the suburban town, north of St. Louis and they all were essentially striving to do the same thing, report the news. But, somewhere along the way, the lines between reporting the news and letting the high stress of the event drive an emotional reaction. Along with massive amounts of public media outlets, civil rights and African American activists began their protest of the situation. One of the main players, who is a major activist in the Black Lives Matters movement, Deray McKesson summed up the chaos of who was media and who were activists/protestors. He Tweeted “Unclear who is media and who is protestor. Lots of media mixed in with main media center gone. #Ferguson” (@deray). Just by this observation, we can draw a finding of how increasingly poorly organized it truly was on the ground. Towards the end of the second week, the order to keep moving was in effect for everyone; nobody could stand still and either report or protest. This caused much distress among especially reporters, who had never really faced this type of reporting. Deray had another interesting observation at this time: “"You cannot stand still. You are subject to arrest." - police officer to media photographers and protestors #Ferguson” (@deray). Along with Deray, several journalists made their Twitter presents known during this time.
Another major player on Twitter during this time was a writer for the Washington Post, Wesley Lowery, who had one of the most viewed tweets during this time. “Ppl in #Ferguson v sensitive to media descriptions of rioting, so worth noting: only behavior accurately described as such happened Sun” (@WesleyLowery). This tweet is referring to the media coverage of the events and how behavior and emotion has taken over. Throughout Lowery’s two weeks of tweeting, a mixture of objective and affective tweets were posted on his account. After a wild first weekend in Ferguson, tensions began to boil over, and emotion began to take over on Twitter. Lowery tweeted: “[a]nother enraged resident screaming at media members. Tensions overflowing. Does not bode well for tonight in #Ferguson” (@WesleyLowery). This sort of reporting opens the doors for interpretation. The affective nature of this tweet gives the viewer a sense of unrest and inevitable violence that could transpire later that night.
Another journalist who struggled at times with keeping a professional manner to their tweeting is Laura Hettiger. Hettiger is a news reporter for a local television station KMOV based out of St. Louis, so a sense of pride seemed to take hold in her coverage of the events due to her close proximity to Ferguson. This sense of pride seemed to get hampered by the chaos and confusion of the events. Specifically, Hettiger wanted to let her followers know about when to tune into television and radio stations. But, when those potential broadcasts get cancelled, emotion driven by frustration began to take hold. One instance in particular happened on the morning of August 15th. Hettiger, attempting to do her job by live tweeting updates about an upcoming news conference, began to seek a more affective route to her tweeting when it inevitably got cancelled. She tweeted: “Absolutely insane. Now #Ferguson chief says bc I told him all media @QuikTrip newser will be here. Never been jerked around like this @kmov” (@LauraKHettiger). With this sense of frustration due to the confusion and lack of clarity might have further muddied the waters of the true events of what happened to Michael Brown.
The last of the ‘major players’ in my data set was a Huffington Post journalist named Ryan J. Reilly. Although Reilly only has 18 results for the entire data set, the importance of a single tweet from Reilly was enough to warrant his notoriety. After his controversial arrest for essentially doing his job and reporting, Reilly seemed to get ‘famous’ in a sense with the media in Ferguson. He tweeted: “Fellow reporter takes a lesson from my arrest, writes phone numbers on her arm #Ferguson” (@RyanJReilly). Accompanied by a picture of a journalist’s arm. This tweet alone shows the severity of the situation. Not only are just protesters getting arrested, but journalist presumably doing their jobs could be subject to arrest also. Due to the notion that nobody is safe from arrest, a sense of unrest began to set in for all those involved.
A theme that can be drawn from the types of tweets that were sent out during the two weeks in Ferguson is that there was a sense of fear invoked into the reporting. This sense of fear could be heard by not only those on Twitter but, also for those on the ground in Ferguson. Journalists were consistently being threatened by police which invoked fear into people who were there for their jobs. The fear spilled over into their tweets, which in turn painted a grimmer picture of what Ferguson was actually like.
Conclusion:
At the end of the two weeks that I researched, I had confounded a lot of data and analyzed the sentiment of a wide aggregation of tweets in Pulsar. After narrowing down several major players and specific examples of tweets that could have swayed public discourse I utilized my data set to decipher whether or not a tweet was affective or objective. Of the specific affective tweets, drawing out ways in which a sense of public discourse was achieved all came down to their influence on Twitter. One major limitation to my finding is that only speculations about the severity or reality of the social discourse. I provided a qualitative approach to the data but, seeing the results in real life might have further solidified my original theories. Another limitation to my findings are that I was the only researcher going through 992 tweets. There were most likely several tweets that slipped through the cracks and could have swayed my research one way or another. A finding that I can conclude on is that emotion and affective tweeting style did take hold during times of induced fear and stress. Further research on this topic might want to take a smaller sample size and narrowed down a more solidified group of major players, to see their sentiment in their tweeting.
Work Cited:
- Smith, Marc A., Lee Rainie, Ben Shneiderman, and Itai Himelboim. “Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters.” Pew Research Center’s Internet & American Life Project. Accessed December 2, 2014. http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
- Steensen, Steen. “Conversing the Audience: A Methodological Exploration of How Conversation Analysis Can Contribute to the Analysis of Interactive Journalism.” New Media & Society 16, no. 8 (December 1, 2014): 1197–1213. doi:10.1177/1461444813504263.
- Stelter, Brian. "Journalists Covering Michael Brown Shooting Say They Were Arrested." CNN. Cable News Network, 14 Aug. 2014. Web. 24 Apr. 2016.
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