A Faceless Mob: Mainstream Media Portrayals of Occupy Wall Street Protesters
Group 1: Brittany Bolz, ChaeEun Cho, Alaina Schwartz, Tim Murphy, & Orinna Weaver
Our group examined how the media represents protesters involved in the Occupy Wall Street (OWS) movement by tracking CNN.com’s coverage of this story over a six month period. This is an important trend to research because the media serves as the primary source of information regarding the OWS movement for most Americans and many people around the world. As a popular news site, CNN.com’s representations of the movement are highly influential in affecting how the demonstrations are viewed by the public at large. Depending on the general public’s perception of the movement, they can either help facilitate or inhibit the overall success of the movement. In our analysis, we found that CNN.com, generally, portrayed protesters negatively, but that within this trend, CNN.com’s representation of the protesters improved over time. In our examination of CNN.com’s coverage we will introduce the three methods we will use to analyze the texts: Word frequencies, authorization, and qualitative coding. Each method offers valuable tools to analyze texts and provides evidence of the progressively more positive representations of the protesters. Lastly, we will present the findings of the analysis and explain how this data relates to my claim.
Our group took a constructionist approach to this project, meaning, we know that the truth is socially constructed, and thus we needed to unpack how it is constructed through the text. In this paper, we use the methods of both corpus linguistics and qualitative coding. To draw on corpus linguistics, we examined lexical and grammatical choice as discussed in Chapter 10, “Dialogue in Institutional Interactions” by Paul Drew et al. According to Drew, trends in word choice in multiple texts help us to identify the “norm”. By analyzing lexical choice, we can identify trends in the text and what is normal in certain situations, and thus, what is also irregular in the text. We also found John Sinclair’s “The Lexical Item” to be very helpful when using corpus linguistics. Sinclair makes the point that meaning is influenced by both word choice and context. Sinclair’s methods such as semantic reversal and collocation can tell us how a word can take on certain meanings based on what other words surround it.
Using the Key Word in Context (KWIC) program, we were able to calculate word frequencies within the CNN.com corpus. Keyness is a measure of word occurrence, which is defined as “the statistically significantly higher frequency of particular words or clusters in the corpus, or a comparable specialized corpus” (Baker, Gabrielatos, Khosravinik, Krzyzanowski, McEnery, & Wodak, 2008). Keyness is important because by looking at what words are used most frequently one can deduce common themes throughout the sample. Furthermore, you can use word frequencies as a base for comparison between words, as we did with protestor/protester versus protesters/protesters.
Furthermore, we used representation of social actors to define who are empowered and disempowered in the text. In addition, analyzing whether protesters are described as active or passive will help reveal whether they are influential in society or powerless (Fairlough, 2003).
Lastly, we used “Methods of Critical Discourse Analysis” by Ruth Wodak and Michael Meyer, to conduct qualitative coding of my data set. This source specifies that Critical Discourse Analysis is not interested in investigating a linguistic unit, but in studying a social phenomenon. To better understand CDA, we studied the seven dimensions Wodak and Meyer outline in their article. We found that analyzing the relationship between language and society could tell me quite a bit about how CNN wants to portray Occupy Wall Street protesters.
Our group focused on CNN.com for our discourse analysis project. We believe CNN is a mainstream media news source that is particularly influential with the general public. We chose to examine CNN as our discourse genre because it is often seen as providing a moderate or slightly liberal viewpoint. By examining this particular mainstream media outlet, we can gather a thorough understanding of how an “objective” source represents the protesters of the Occupy Wall Street Movement. CNN was the optimal choice for our group because it is a moderate mainstream news outlet, whereas Fox and MSNBC tend to voice more extreme conservative or liberal viewpoints.
Our group’s CNN corpus is a specialized report (topical corpus), which has a specific genre format, modality, topic and time frame. We collected the data for our corpus through the academic search engine LexisNexis. We made sure that our corpus included these three important parameters: genre of interest (homogeneity), topics of interest (theoretical relevance) and time frame of interest (synchronicity). Our genre of interest, CNN, was easy to search. Our group simply selected the CNN search tab to query within the CNN database. We then used the key term “Occupy Wall Street” to search for topics of interest within the CNN database. We also specified our time frame of interest as September 2011 – April 2012. Our finalized CNN corpus had 187 articles, 133,126 words, and a type-token ratio of .19 (25,642/133,126). This is a valid corpus for analysis because it has a large sample, and balances characterization with comparison.
Pattern 1: Use of the Word ‘Arrest’ Over Time
To first decide whether or not the attitudes towards the Movement have changed, I decided to use the tool of Norming word frequencies, to see how often “arrest” has been used in our corpus. I counted any use of any form of the word, and I came out with very pleasant results Below, you can see the graph that shows how the use has gone down over time. For example, during the first months of protesting, “arrest” consisted of nearly 7 of every thousand words about the Movement on CNN.com. Compare that to April when it is just over 1 per thousand words.
But how is it that this data can represent my thesis? Now we know exactly how the protesters have been represented for the past 8 months. We see that by being identified as arrestees, the protesters become undesirable in the eyes of the readers. It is not often that the people against the government (represented here by the police) are supported by the majority of the public, in fact, by connecting the words protester and arrested together in every 7 of 100 words, it is hard for the readers to disregard that association (Adolph 46). However, by the time April came around, feelings on the protesters had improved, and though they were just as active, there was a more positive discourse about them.
The next pattern we observed was the tendency to refer to the participants in the Occupy Wall Street movement as a collective group. This occurrence can be measured using word frequencies, or keyness, within the corpus. In order to measure this group vs. individual notion, I looked at the frequency of the word protester vs. protester in the text. Due to multiple spellings in the corpus I combined protesters and protesters into one category, as well as protesters and protesters. The number of times protesters/protesters occurred in the text was 690, whereas protester/protestor only occurred 21 times. Moreover, “demonstrators” is used 233 times, while “demonstrator” was only used 14 times. Although simply looking at the frequency that a word occurs in a text may not be as telling as a more in-depth strategy such as coding, it can still be useful to the discourse analysis process. This clearly shows that, in general, the movement was referred to as a broader group effort – with individual accounts used less often.
By almost exclusively grouping the protestors together as a single entity, CNN is making them seem less personal. Fairclough discusses this concept concerning representations of social actors in terms of personal or impersonal and named or classified (2003, p. 146). In relation to the protesters of the Occupy Wall Street movement, they would be categorized as impersonal because they rarely get their own voice as individuals, and classified as opposed to named because personal information does not often get reflected. As a result of this grouping tendency, the individual protestors rarely get to speak for themselves, and instead are discussed, and therefore seen, as a collective.
Pattern 3: Protesters are Passive Social Actors Whose Behaviors are Affected by Authorities
Journalists reproduced power relations in their coverage of protests can be seen in choices in the representation of social actors. When speaking about legitimacy of protesters staying at parks, journalists rarely allowed protesters to become social actors. Instead, Supreme Court has authority to decide whether protesters are allowed to stay at parks. For instance, a December 9, 2011 CNN.com story included the following sentence:
In a move similar to McIntyre’s ruling, a New York Supreme Court announced last month that Occupy protesters would be allowed to return to Zuccotti Park — considered a home base for demonstrators — but would be restricted from camping overnight.
The main social actors included are ‘a New York Supreme Court’, ‘protesters’, and ‘demonstrators’. While New York Supreme Court is activated (‘announced’), protesters are passivated (‘be allowed to return’ and ‘would be restricted from’). The formers leads a process of activity ‘announce’ and the latter are affected by the former’s decision. Paying attention to ‘activation’ and ‘passivation’ is significant because it tells who have capacity for agentive action (Fairclough, 2004). In this instance, a New York Supreme Court has authority to allow protesters to stay at Zuccotti Park and, therefore, the court controls the protesters’ actions. Protesters, who are passivated, are subjected to processes and affected by the actions of the court. Following excerpts show similar patterns:
A New York Supreme Court ruled Tuesday that Occupy protesters will be allowed to return to Zuccotti Park, but they can’t bring their tents and generators — once a mainstay of the movement.
As seen in the above cases, protesters cannot make their own decision to return to camp without the court’s approval because they are lack of authority to legitimize their protests. Passivation of protesters reflects that protesters are subjugated to the authority in society (Fairclough, 2003).
Pattern 4: Police Aggression is Justified, which Portrays Protesters as Powerless Out-Group
Lastly, our group compared CNN’s portrayals of police aggression to the portrayals of protester aggression. We found substantially less occurrences of police aggression (60 occurrences compared to 115 occurrences of protester aggression). Further, when analyzing the individual excerpts, I found there to be a pattern of mythopoesis legitimization. Often, when speaking about police actions against protesters, CNN journalists used language to legitimize a logical outcome. For example, excerpts from January and February of 2012 said:
Officers were forced to remove several people clinging to a wood structure erected in the square, and a total of 31 people were arrested over the course of the day, the police said.
In the violence, a crowd of several hundred people threw rocks and shot fireworks at officers after being asked to leave the scene, prompting officers to fire tear gas, authorities said.
These excerpts are also examples of ways journalists recontextualize social events. According to the Fairclough reading, social events happen but they are understood through how they are represented in language use. Journalists recontextualize certain events and reconstruct the meanings of those events for the public. In this case, the CNN journalists reconstructed the language in the excerpt to legitimize the actions of the police. By legitimizing the actions of the police and condemning the actions of the protesters, the journalists made the police appear active and powerful, and the protesters powerless and passive.
We found in our analysis that corpus linguistic strategies such as keyness and lexical choice, as well as qualitative discourse strategies such as Critical Discourse Analysis, were of great aid in understanding the social positioning in which even the most objective media sources are continuously engaged. In our analysis, we found that CNN.com, generally, portrayed protesters negatively, but that within this trend, CNN.com’s representation of the protesters improved over time. Moreover, our final realization is that even the most moderate of mainstream news sources strives to categorize people in their reporting. According to Stuart Hall, we want people to fit within the given symbolic order. As humans, we desire for people to belong to a category in order to make sense of our social surroundings. In the case of the protesters of the Occupy Wall Street movement, they were routinely categorized as part of a powerless, volatile out-group, while the police and other authorities were regarded as a powerful, respected in-group.
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