“Not So Black and White”

Abstract

Although Twitter may not be representative of the overall population, information gathered from Twitter can assist our understanding of what people think about certain social issues, particularly race. With this in mind, using Twitter as the main source for data collection, this study tracks the #Ferguson hashtag in the 4 days following the killing of Mike Brown in Ferguson, Missouri, to examine how race was discussed in the aftermath of the event. A key question guiding this article is how do the tweets captured resonate with broader understandings about race in America?

Keywords

Twitter Race Mike Brown Ferguson Racism

Introduction

Social media has affected the way we talk about race, raising public attention to race issues. For example, on December 20, 2013, then public relations director of Interactive Corp (whose brands include the likes of UrbanSpoon) Justine Sacco was boarding a plane headed to South Africa. Before boarding, she sent a tweet out to her followers informing them of her travel plans. The tweet read “Going to Africa. Hope I don’t get AIDS. Just kidding. I’m white!” After sending this, she turned off her phone and settled into the 11-hr flight between London and South Africa. As the flight progressed, her tweet was picked up by Internet media outlets (such as Gawker and Buzzfeed), reaching a wider audience than she imagined. As Vingiano explains, 3 hr after her tweet, Justine Sacco began to trend in South Africa, and later, worldwide (Vingiano, 2013) with the hashtag #HasJustineLanded tying the story together. As one Twitter user described, “I don’t think America has watched a landing this closely since Apollo 13 re-entered the earth’s atmosphere in 1970. #HasJustineLandedYet” (Vingiano, 2013). The instant attention Justine Sacco’s insensitive tweet received catapulted the scenario to a viral event, where Twitter users were collectively saying “this is not cool.”

According to Twitter analytics website Topsy.com, after Justine sent out the tweet, her name had been tweeted more than 30,000 times and the hashtag #HasJustineLanded almost 100,000 (Vingiano, 2013) between December 20 and 21, 2013. The Twitter response to the tweet is interesting, as users were able to “raise awareness on a given topic, show[ed] new ways of viewing the world, and expos[ed] truths” (Nahon & Hemsley, 2013, p. 9) about the expected norms of behavior in the online and offline world. In response to her racially charged tweet, Justine Sacco’s employer quickly terminated her contract.

A question that arises is why did Justine Sacco feel it was appropriate to tweet what she did? Some argue that people are more open to using insensitive language online because of the anonymity and lack of accountability provided by the use of social media—a phenomenon John Suler calls the online disinhibition effect (Suler, 2004). On one hand, people share very personal things about themselves and reveal secret emotions, fears, and wishes—something Suler refers to as benign disinhibition (Suler, 2004). On the other hand, we sometimes witness (online) rude language, harsh criticisms, anger, hatred, and even threats—something Suler calls toxic disinhibition (Suler, 2004). With the rise of different social networking websites, “new modes of communication mean it is easier than ever to find and capture this type of language” (Bartlett, Reffin, Rumball, & Williamson, 2014, p. 5). Social media sites like Twitter allow us to view toxic disinhibition like we have never been able to do before.

Twitter, for example, gives anyone with something to say a “digital soapbox” where he or she can instantly express thoughts, values, and opinions on a variety of issues in 140 characters or less. Although most Twitter users will tweet about news stories (see Tao, Hauff, Abel, & Houben, 2014), some users may take to Twitter to espouse hateful sentiment. Before Twitter gained popularity, reading through the comments section of online newspaper websites revealed a level of intolerance users had in response to a number of social issues. For example, in a recent study analyzing hate speech in newspaper website comment sections, Erjavec and Kovacic found that of the 362 news items analyzed, hate speech was found under (almost) every news item, particularly as it related to politics, racism, homophobia, and religion (Erjavec & Kovcic, 2014). Although the use of hateful language online is not a new phenomenon (see Foxman & Wolf, 2013), what is new is the ability for users to strategically track and monitor its use. For example, a number of recent projects have used Twitter to generate an understanding of how often certain racist terms are used online. For example, in a 2014 study by U.K.-based Think Tank DEMOS (titled Anti Social Media), researchers found that over a 9-day study period, there were approximately 10,000 uses per day of racist and ethnic slur terms in English (see Bartlett et al., 2014). Similarly, a 2015 study of racist tweets in Canada found that users would quite openly use racist language in the context of real-time situations (see Chaudhry, 2015). Due to Twitter’s “free speech” ideal (see Greenhouse, 2013), the company does not filter out terms that are racist in nature, and as a result, users can track and monitor racist language.

Examining tweets allows researchers to understand what Twitter users are saying about any given topic. Scholars, advertisers, and political activists see massive online social networks, such as Twitter, as a “representation of social interactions that can be used to study the propagation of ideas and social bond dynamics” (Huberman, 2009, para. 16). This assumption, however, is problematic as a recent survey conducted by Pew Research found that Twitter reaction to certain events (specifically political events) is often at odds with overall public opinion (Pew Research, 2013). Although this finding suggests that Twitter users are not representative of the general public, gathering data from Twitter can still reveal interesting real-time insights related to current events, particularly around difficult topics such as race.

With this in mind, using Twitter as the main source for data collection, this study tracks the #Ferguson hashtag in the 4 days following the killing of Mike Brown in Ferguson, Missouri, to examine how race was discussed in the aftermath of the event. A key question guiding this article is how do the tweets captured resonate with broader understandings about race in America?

Method

Twitter has become a powerhouse in sharing news information (both locally and globally). Users are able to search for keywords or hashtags related to an event, moment, or experience, allowing them to feel like they are experiencing the event in real time. As Murthy explains, “part of Twitter’s seductive power is the perceived ability of users to be important contributors to an event” (Murthy, 2013, p. 33). One of the ways Twitter allows users to search for data is through a Streaming API. The Streaming API is the most used data source for Twitter research as large-scale quantitative data are based on raw data collected through this source (Gaffney & Puschman, 2014). The Streaming API is a unique way of gathering data as it is “push” based, meaning “data is constantly flowing from the requested URL (the end point) and it is up to the researcher to develop or employ tools that maintain a persistent connection to this stream of data while simultaneously processing it” (Gaffney & Puschman, 2014, p. 56).

One of the main obstacles in collecting streaming data from Twitter is the structure of Twitter itself. As Gaffney and Puschman explain, the Streaming API permits access to data in three bandwidths: “spritzer,” “gardenhose,” and “firehose,” which deliver 1%, 10%, and 100% of all tweets on a given topic posted on the system, respectively (Gaffney & Puschman, 2014). According to Gafney and Puschman, regular accounts on Twitter have access to spritzer data; the gardenhose is granted occasionally to users with definable and compelling reasons for increased access, while the firehose is only available as a component of a business relationship with Twitter directly (Gaffney & Puschman, 2014).

Users can pay third-party service providers (such as Sifter) to gain more access to Twitter data—especially historical data. Sifter provides users with all undeleted tweets for a user-specified date range and keyword search. For this project, Sifter was used as the main data collection site searching for all tweets using the hashtag #Ferguson between August 9, 2014 (the day of Mike Brown’s death) and August 13, 2014. Once the tweets were captured, DiscoverText¹ was used as the data analysis tool. As a result, a total of 1,062,000 tweets mentioning the keyword #Ferguson were used on Twitter between August 9 and August 13, 2014.

One of the biggest challenges in using Twitter data is the large amount of data collected. As Chief Innovation Officer of FACE (a global strategic insight agency), Francesco D’Oraztio points out, “social [media] data is not quantitative data, rather qualitative data on a quantitative scale” (D’Orazio, 2013, para. 10). As a result, a manageable approach to this data set is to develop systematic sampling methods. One of the strengths in using DiscoverText is the ability to create user-defined sample sets. Although there were 1,062,000 tweets mentioning the keyword #Ferguson, only 232,286 were unique, meaning they were not duplicates. Although 232, 286 is still a large amount to read through, Bruns and Stieglitz suggest that researchers can still examine themes between tweets (Bruns & Stieglitz, 2014)

One way of doing this is to identify key actors and concepts to search within the tweet sample set. As this project was interested in examining how race was being discussed on Twitter in the aftermath of Mike Brown’s death, the hashtag #Ferguson served as the key actor, while the terms Black and White served as the key concepts. In total, 18,862 tweets using the #Ferguson hashtag used the terms Black and White during the timeframe these tweets were collected. Using DiscoverText, a random sample of 1,200 tweets was created, read, and coded by the researcher from the above sample size and organized into race-based themes (discussed below). In addition, race of the Twitter user was also captured in the sample set from this study. One of the strengths in using DiscoverText to analyze tweets is the ease with which it permits access to Twitter user information, including (but not limited to) user name and profile picture. As a result, the perceived race of the Twitter user can easily be obtained by accessing the user profile (presuming the user is using a real profile picture). The race of the Twitter user was determined by searching the user’s profile, and if the image was confirmed to be of him or her, they were coded as Black, White, or Other. Profiles that contained the standard Twitter egg image, or that used an alternative image, were coded as no picture and alternative picture, respectively.

#Ferguson

On August 9, 2014, Mike Brown, a young Black male, was shot and killed in a suburban street during daylight hours in Ferguson, Missouri. Missouri has long been one of America’s most segregated areas, and “there remains a high wall between black residents—who overwhelmingly have lower incomes—and the white power structure that dominates City Councils and police departments” (“The Death of Michael Brown,” 2014). The shooter, Darren Wilson, a White police officer, drove up to Brown and ordered him to move off the street and on to the sidewalk. A struggle took place between Brown and Wilson, resulting in shots being fired within the vehicle, followed by a short pursuit where Wilson fired his gun six more times. According to news reports, Brown died 35 feet from the police cruiser (see Lind, Lopez, Williams, & Taub, 2014). After the shooting, protestors voiced concerns over the disputed circumstances of the shooting and the ongoing investigation. Much like the killing of Trayvon Martin in 2012, the Mike Brown incident recalled echoes of police bias and discriminatory treatment toward Black people in America. In the days that followed, protests and civil disorder broke out throughout America, creating stronger unrest between Black residents and the police of Ferguson. This resulted in polarizing viewpoints regarding race and police bias in America. Nowhere was this polarization more prevalent than on Twitter, where users turned to the medium to raise awareness and bring attention to the perceived injustice of the Mike Brown shooting. The hashtag #Ferguson emerged as a common thread tying conversations together on Twitter, creating an interesting space to examine how the tweets resonated with discussions about racist policing practices in America.

To understand processes of race making within policing, it is important to consider the historical frame of reference within the discipline itself that shapes this situation. As Mike Rowe reminds us, “the discipline of criminology emerged in Europe in the mid-nineteenth century when ‘scientific’ race-thinking was ascendant” (Rowe, 2012, p. 9). This form of thinking was historically rampant within the criminal justice system of North America. As Rowe explains, consider how the work of Cesare Lombroso, an Italian anthropologist widely regarded as the founder of modern criminology, was significant in U.S. crime policy development in the first decades of the 20th century (Rowe, 2012, p. 9). In Lombroso’s terms, criminal offending among the “higher” European races was atavistic; among the “lesser” races, however, crime was a normal result of biological character (see Rowe, 2012). As Rowe concludes, although scientific racism has largely been discredited, elements of biological determinism resound[ed] within criminological thought and influenced a large variety of criminal justice law, policy, and practice in the United States (Rowe, 2012).

Within the context of race and the criminal justice system, racialization serves the purpose of defining who the law maker is and who the law breaker is. Often times, this occurs along racialized lines, where the White police officer stops the racialized suspect, due to an (un)conscious bias that equates skin color with crime. As Carl James points out, from most accounts, race is a major determinant of an individual’s chances of being stopped and questioned by police (James, 2002):

Racial identity takes on particular meanings that inform discriminatory judgments by police officers. The decision to stop, question, and search [racialized] individuals is a reflection of the racialized ways in which police carry out routine checks of people they imagine to be potential lawbreakers. (James, 2002, p. 296)

Through this relationship, we see how the process of police discretion is an exercise in assigning negative meaning to race. As Rowe explains, though some argue that race is not a concept real in itself, it is a concept that has real-life consequences (Rowe, 2012).

The use of police discretion is a basic investigative tool for the police to conduct their work. Law enforcement officials argue that they need to use [these] tactics to identify criminals (see Bowling & Phillips, 2003) and manage the potential for crime to occur. As Satzewich and Shaffir point out, police work necessarily involves attentiveness to particular signals and unusual fits (Satzewich & Shaffir, 2009). As a 2001 United Nations Research Institute for Social Development report explains, police see themselves as the “thin blue line” on the front line of a war against crime (Bowling, Phillips, Campbell, & Docking, 2004): “Policing, under a military model, requires selective enforcement in favor of the dominant group and the criminalization of minority activities” (Bowling et al., 2004, p. 5). Unfortunately, through the process of discretion and race making, racialized minorities are often the first to be selected for criminal activity.

This opinion of racist discretionary practices was prevalent in the Los Angeles Police Department (LAPD) during the early 1990s, culminating in the infamous videotaped Rodney King beating. Although the fallout from the Rodney King incident challenged the perception of fair administration of justice in the United States (a sentiment that re-emerged during the aftermath of the Mike Brown shooting), it also unveiled what a large number of racialized minorities felt about the police. The 1991 Christopher Commission concluded that “there are a significant number of officers in the LAPD who repetitively use excessive force against the public which is aggravated by racism and bias within the LAPD” (Bowling et al., 2004, p. 7). Similar results were found in 1999 in the New York Police Department (NYPD) by Fagan and Kiss, where records indicated that the NYPD were stopping Blacks and Hispanics more often than Whites (Fagan & Kiss, 2007). In Ferguson, Missouri, where Mike Brown was shot, a New York Times article found that Blacks account for 86% of the traffic stops and 93% of the arrests after those stops (“The Death of Michael Brown,” 2014). Not surprisingly, it is under the context of the perceived unequal distribution of justice from Whites on Blacks that provides a useful framework to understand online responses to the death of Mike Brown.

#Ferguson—The Online Reaction

As highlighted in a recent Pew Research study, the shooting death of Mike Brown became a national news story on mainstream and social media (Pew Research, 2013). On Twitter, conversations about #Ferguson started soon after Brown’s death, while mainstream media did not start to fully cover the story until 2 days after the event (Pew Research, 2013, para. 14). The Ferguson story generated a lot of Twitter activity. More than 10.6 million tweets were posted about Ferguson from the day the shooting occurred through the 8 days that followed. Of those 10.6 million tweets, 8.3 million used the hashtag #Ferguson (Pew Research, 2013, para. 15). It is important to remember that analysis of Twitter is not meant to serve as a means to represent the national U.S. adult population (see Pew Research, 2013). Having said this, however, interesting insight about race can be gleaned from the data. In the aftermath of the Mike Brown shooting, debates around racial profiling, police bias, and systematic oppression toward the Black community quickly started to surface online as users reacted to the shooting. The hashtag #Ferguson quickly became a rallying point for users to connect to a trending event and share their viewpoint.

As Figure 1 points out, based on the sample size from this study, Black³ Twitter users tweeted most often using the #Ferguson hashtag.

Figure 1.

Race of Twitter User².

Although having a sense of the racial background of the Twitter user gives us (limited) insight on who was tweeting about #Ferguson, further information is available when comparing Twitter user race data with how race was being discussed during this timeframe. Although Figure 2 is specific to the race of the Twitter user and the context of the tweet, Table 1 provides a broader look at how the key search terms Black and White were used. Further examination of these tweets revealed interesting findings about how the #Ferguson hashtag connected to broader debates about race in America. Although all categories below provide useful information to consider, this article will only focus on the following categories: structural racism, “race talk,” institutional racism, interpersonal racism, and individual racism.

Figure 2.

Race of Twitter User broken down by type of racism being tweeted about.

Table 1.

Context of tweet related to race and racism.

Category of tweet	Keyword: Black	Keyword: White	Total	%
Structural racism	213	195	408	34
Race TALK	146	155	301	25
Institutional racism	90	79	169	14
Media reports—Mainstream Media and non-Mainstream Media	84	36	120	10
Interpersonal racism	26	46	72	6
Tweet in response to ongoing violence	20	39	59	5
Individual racism	13	43	56	5
Not related to #Ferguson	8	7	15	1
Total	600	600	1,200	100

Structural Racism

Structural racism was the most common theme in the tweets collected for this study. Structural racism is the normalization of an array of dynamics—historical, cultural, institutional, and interpersonal—that routinely advantage Whites while producing cumulative and chronic adverse outcomes for people of color. Structural racism states that “inequalities are rooted in system-wide operation of a society that excludes a substantial number of members from particular groups from participation in major social institutions” (Henry & Tator, 2010, p. 352). In the tweets collected for this study, discussions about structural racism manifested themselves in the following five ways:

“Being Black”: Tweets under this category reflected from mostly Black user’s musings on the difficulties and dangers of being Black in America, for example, @SouthJeiwshWman: #ferguson No one is safe while these tragic deaths of young black males continue. Being black, male, & young is not a death penalty offense (9:36 a.m., August 11, 2014).

“Double standards”: A common thread tying tweets under this category together was the perceived double standard society has toward Black people in comparison with White people. When broken down by race, Black Twitter users and White Twitter users were fairly equal when discussing this issue, for example, @thedahlimmama: So MANY white Bundy Ranchers pt rifles at cops and NOTHING!Black teen unarmed,hands raised? Shot multiple times! #SMDH #Ferguson #UniteBlue (6:48 p.m., August 12, 2014).

Racism: According to Henry and Tator, racism is a system in which one group of people exercise power over another on the basis of skin color (Henry & Tator, 2010). Tweets under this category, mostly from Black Twitter users, reflect this definition, for example, @Bobby_Fleck: Unarmed black kids being murdered DOES NOT HELP HUMANITY when will racism stop? #Ferguson (11:14 p.m., August 12, 2014).

Societal bias: Tweets under this category suggest there is a general prejudice toward Black people in America and the situation in Ferguson is symptomatic of larger structural issues. Black and White Twitter users tweeted roughly the same amount under this category, for example, @blackpuma12: White ppl dont know what its like to have psychopaths cloaked in racism & entitlement have authority to strip their civil rights #Ferguson (5:24 p.m., August 12, 2014).

White privilege: Under this category, tweets resonated with the basic premise of White privilege, namely, privileges associated with Whiteness. Interestingly, there was little variation between Black and White Twitter users coded under this category, for example, @ronfein: One aspect of white privilege in America: not needing to worry that you or your kids will be shot dead by police. #Ferguson (7:30 p.m., August 12, 2014).

Race Talk

The next theme is categorized as “race talk.” Essentially, tweets under this category are framed in a way where race is central to its content. These tweets either confirmed or resisted the notion that race had something to do with the Ferguson situation. As Figure 3 highlights, a number of tweets from this category took a neutral approach in suggesting race was a contributing factor, followed by others tweeting that Ferguson was a race issue that required immediate government intervention.

Figure 3.

Type of “race talk” organized by race of Twitter user.

In terms of a breakdown between race of Twitter user and the above categories of “race talk,” interesting sub-themes emerged. For example, Whites were more likely to suggest the shooting of Mike Brown had nothing to do with race, while Blacks were more likely to suggest the subsequent riots in response to the shooting were justified, as this tweet highlights: @luvnhappiness08: Citizens of #ferguson hv every right to be outraged. They are tired. Black people are humans. We deserve the same respect allotted to others (8:12 a.m., August 13, 2014).

The “owning it” category also provides interesting insight. White Twitter users felt there was a level of ownership they needed to take to rectify the unfairness connected to systemic bias within America (i.e., @AmyStephen: No one wants to hear about “privilege” but it’s time us white people open our eyes and see it. http://thoughtcatalog.com/macy-sto-domingo/2014/04/18-things-white-people-seem-to-not-understand-because-white-privilege/ … #Ferguson #Peace [8:35 p.m., August 11, 2014]), while some Black users felt the Black community requires concentrated efforts to create sustained change (i.e., @just_Kellen: I long for the day when black orgs and communities carryout sustainment plans instead of reacting and forgetting. #blackissues #Ferguson [2:37 p.m., August 12, 2014]). What is interesting about this category of tweets is the awareness of race as a key issue in identifying and resolving key structural issues within society.

Institutional Racism

Institutional racism refers to unequal impacts and outcomes based on race produced by key societal institutions. As Henry and Tator outline, “institutional racism is manifested in the policies, practices, and procedures of institutions, which may, directly or indirectly, consciously or unwittingly, promote, sustain, or entrench differential advantage or privilege for people of certain races” (Henry & Tator, 2010, p. 44). When an individual acts in the context of an institution, these actions perpetuate racial inequalities at a broader level. For example, when a police officer treats a member of the public with racial bias, this action is institutional racism as the police officer is acting as a representative of a law enforcement institution. Not surprisingly, police bias was a major focal point for many Twitter users, with Black users more likely suggesting the incident in Ferguson is a result of a bias police system (Figure 4).

Figure 4.

Perception of police bias, broken down by race of Twitter user.

This perception from Black people, however, might not be unfounded. Recent reports highlight that though Ferguson has a high number of Black residents, there are very few in positions of influence. For example, by 2010, Ferguson was 29% White and 69% Black, but had very little political influence. The mayor and the police chief are White, as are five of the six City Council members (“The Death of Michael Brown,” 2014). The disparity is even greater within the police department, where only three of the 53 police officers are Black (“The Death of Michael Brown,” 2014). This fact was not lost on many of the Black Twitter users, as a number of times the above statistics were tweeted out, resulting in many users claiming the shooting had everything to do with institutional racism:

@PureBlackNubian: Feeling so angry about the situation #ferguson a young black man killed, another life wasted, wiped-out because of trigger-happy cops. (3:38 a.m., August 13, 2014)

@Ms_Kitty_Kat: The police killed a Young Black Man!! This is not how you rectify/apologize for Your wrongdoings. #Ferguson #JusticeforMikeBrown. (5:38 p.m., August 13, 2014)

@BlaQQtino: It hurts to see this same storyline play out ever year! Cop kills black male! #Ferguson #PoliceOurOwn it’s becoming routine! (7:22 p.m., August 13, 2014)

Individual and Interpersonal Racism

Although the aforementioned tweets highlight macro-level understandings of racism, individual and interpersonal racism provides us with a micro-level lens to consider the #Ferguson tweets from. Individual racism includes personal and private attitudes about race influenced by the dominant culture. As Henry and Tator explain, “individual racism involves both the attitudes held by an individual and the overt behavior prompted by those attitudes” (Henry & Tator, 2010, p. 42). Interpersonal racism is a manifestation of individual racism, where people publicly express racial prejudice, hate, bias, and bigotry between individuals. Once private beliefs come into interaction with others, the racism is now in the interpersonal realm. Individual and interpersonal racism made up about 11% of all tweets collected for this study (see Table 1). As Table 2 below highlights, White users and those with an alternative Twitter image were most often expressing forms of individual and interpersonal racism using the #Ferguson hashtag.

Table 2.

Race of Twitter user expressing forms of individual or interpersonal racism.

Race of twitter user	Amount
Alternative image	51
Black	23
No picture	9
Other	7
White	38
Grand total	128

Individual and interpersonal racism manifested itself in the tweets collected for this study in four different ways (Figure 5):

1. “White resistance”: Under this category, mostly White users expressed sentiment that Black people are to blame for the events in Ferguson. Twitter users would often mention previous crimes where a White person was assaulted by a Black suspect, focusing on the “fact” that White people did not respond to violent crimes in the same way (i.e., @DominicFabiscus: Hey #Ferguson ! Tony Stewart murdered a teenager this weekend, and you dont see white people looting Pottery Barn and Starbucks! [4:41 a.m., August 11, 2014]). Users under the “White resistance” category also suggested the white race was the true victim, as suggested by the following tweet: @sassyalice: .@SillyLiberals @KingLouisIII @SweetSlimJones_ Token blacks? Wow, you really can help yourself can you. White is a minority in #Ferguson (12:04 p.m., August 13, 2014).

2. Black crime: Similar to the “White resistance” category, tweets from this category reflected a sense from Twitter users that “Black on Black” crime should be the protestors’ main focus, rather than worrying about one White person killing a Black person. For some users, the suggestion was Black people should focus their attention on “their own” rather than trying to racialize the issue: @TXWade: Where’s outrage for black on black crime/murder in Chicago? Liberals rely heavily on racial divide. And of course stupid people. #Ferguson (3:19 p.m., August 13, 2014).

3. Subtle racism—non-Black user: Tweets under this category reflect the essence of individual racism as most tweets under this category revealed the person’s personal attitudes and beliefs about Blacks. As these sentiments were shared via Twitter, the definition of interpersonal racism also applies due to the public nature of Twitter and the expression of racial prejudice. The following tweets are examples of this category:

@Bear_BQ: the black community has lived their lives idolizing thugs, gang life, rappers and living it large, why not get a job #MakeChange #Ferguson. (10:09 p.m., August 12, 2014)

@FederalSpyGuy: #Ferguson black people are blaming EVERYONE and EVERYTHING ELSE for the behavior of *their citizenry Just an excuse to run wild (1:16 p.m., August 12, 2014)

@HJett: If the black people in #Ferguson had jobs they’d be to busy to riot. Just a thought. (7:20 a.m., 12 August 12, 2014)

4. Subtle racism toward White: Drawing upon the same context as above, tweets under this category reflect non-White users’ prejudice and bias toward Whites, as the following set of tweets showcases:

@candiie_taker: Fuck the police. Fuck the white man. #BLACKPOWER #FERGUSON. (9:20 p.m., August 12, 2014)

@playboystudG4L: @CharlieB4Real And your ancestors wasted a nut busting out your family tree white devil. Pale pasty devil burn in the sun!!! Haha #Ferguson. (1:07 p.m., August 11, 2014)

@AnIcecoldBrew: I’m sorry but I can’t agree with anyone who’s white saying anything on this situation #Ferguson. (10:54 p.m., -August 10, 2014)

Figure 5.

Expressions of individual and interpersonal racism.

#Ferguson—Trending for Social Change

As the above tweets highlight, discussions surrounding race and racism manifested themselves in a variety of forms in the days following the shooting death of Mike Brown in Ferguson, Missouri. The tweets reflect broader debates surrounding structural racism, institutional racism, interpersonal racism, and individual racism. At the time of writing, the Ferguson shooting grand jury, after months of deliberation, decided to not indict Officer Darren Wilson in the death of Mike Brown, leading to further protests and outrage both offline and online—at an unprecedented rate. Although a number of riots took place around the Ferguson area after the verdict, on Twitter, the activity was even more overwhelming. There were over 3.5 million tweets with the #Ferguson hashtag used within the span of 2 hr (Twitter Reverb, 2014),). In fact, according to @TrendieCA (a Twitter account that tweets the top 10 trends at the top of the hour), in the hours after the Ferguson decision, the top five (out of 10) Twitter trends in North America were related to #Ferguson.

Under this context, the shooting of Mike Brown constitutes what Lynn Chancer defines as legal cases that become social causes (Chancer, 2005). According to Chancer, “a highly profiled legal case involving violence often become symbolic of perceived social problems in a given time and place and often engage a broad range of participants” (Chancer, 2005, p. 6). The case of Mike Brown surely fit this description, as it received a large amount of attention from a broad spectrum of participants. As a result, cases such as this one become symbolic, as they “merge legal cases and social causes” (Chancer, 2005, p. 7), thereby creating polarizing discussions about blame, culpability, and appropriate forms of retribution. As cases and causes become enmeshed, “controversies [emerge] about whether responsibility for a given crime rests with individuals or social forces, and whether apparent perpetrators were actually victimized, or apparent victims were culpable to some extent” (Chancer, 2005, p. 7).

As Chancer explains, crimes that become “culturally and politically symbolic” prompt broad public commentary and protest (Chancer, 2005), as is evident in the aftermath of Mike Brown’s death. [High-profile cases], according to Chancer, can be seen, in retrospect, to reveal much about a bygone era’s anxieties or fears, and manifest disparities between a period’s idealized expectations and its troubling realities (Chancer, 2005). In the case of Mike Brown and the acquittal of Darren Wilson, the troubling reality is that young Black men who are shot and killed (at the hands of a non-Black assailant) are perceived as disposable in a supposed “post-racial” America, and are a troubling reminder that institutionally, #BlackLivesDontMatter. The fact that the #Ferguson hashtag continues to trend on Twitter, however, highlights the importance of users’ interest in the case as an opportunity to create momentum for social change.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biography

Irfan Chaudhry is currently a criminology instructor at MacEwan University, Department of Sociology and a PhD Candidate (provisional) with the Department of Sociology, University of Alberta.

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