Toniesha L. Taylor
Like many African American people, I grew up in a house where language and the tool of signifying were taught and learned in the comfort and protection of the family. Everyone in my family could signify, and they were excellent at doing so. I recall my father explaining the power of mastering words and what it means to be able to slip effortlessly between street English and the King’s English. He explained that I should learn to do this well so that, no matter the audience, I would always be at home. “You can command respect when you talk and be talked to, and folks don’t have to change,” he said. I took this to heart. I learned. Years after my father’s passing, I still often think of moments like this because they have informed my speaking and my academic work. In this way, language and signifying are the tools of relationship and relational belonging. Signifying marks belonging in the diaspora.1
The grammar of Blackness is such that it requires invention and reinvention through language to survive the tandem threats of surveillance and government violence. Many Black people throughout the diaspora live in nation-states with a history of slavery as the primary genesis of Black oppression, where laws and policies were put in place to ensure subjugation through language, shared public and private spaces, and the ownership of bodies. In the United States, these laws and policies were particularly harsh. Enslaved Africans were not allowed to gather without a white male present; they were forbidden to learn to read or use their native languages. It is through language that enslaved and later freed Black people found and find liberation. In his seminal book The Signifying Monkey, Henry Louis Gates speaks of how Black people use language to create codes and meanings that are socially, culturally, and intellectually significant and tangible to describe their lived experiences. He discusses how signifying functions as a rhetorical tool and grammar that allows Black people who learn to use it efficiently an entry into Blackness and a shared cultural space. In the present cultural moment in which memes, tweets, and GIFs rule social meaning-making, the power of signifying within the Black diaspora is particularly potent and visible. Ambiguous grammars of Blackness remain elusive to those who have not had experiences of living Black in social spaces that are guided by the even more powerful grammars of white supremacy, which require secret grammars of Black signifying. White people, businesses, governments, and organizations still attempt to coopt Blackness and grammar to sell products, share experiences, or tantalize publics.
Signifying as a rhetorical device in social media spaces like Twitter is often about invention, command of antecedents like links, and temporal moments. The ability of users to harness the grammars of Blackness that exist offline or IRL (in real life) is crucial to demonstrating the rules of signifying on Black Twitter. Black tweeters (or, to borrow a phrase from MSNBC host Joy-Ann Reid, tweeples) function as the rule makers in social media environs governed by the rules of Black grammar in a linguistic economy. To demonstrate this, I collected instances of and analyzed two hashtags: #RaceTogether and #NewStarbucksDrink.
The Day Starbucks Decided Race Existed: History and Context
Starbucks CEO Howard Schultz created the Race Together campaign, which launched on March 16, 2015, to encourage a much-needed social dialogue around race in American life, to be led by store baristas. The hashtag #RaceTogether was launched through the company’s social media platforms on the same day to correlate with the discussion in online spaces.2 The campaign’s hoped-for results, as articulated by Schultz, were a clearer understanding of race relations and greater acceptance of people with varying backgrounds. What Schultz got instead was something he did not bargain for. Twitter-savvy Black intellectuals whose academic areas of study are race relations, critical cultural studies, communication, sociology, history, and art, as well as journalists and activists, responded to the call with critique. Most notably, Jay Smooth and Nancy Giles joined Chris Hayes on his MSNBC show to discuss their criticisms of the campaign. Smooth noted that, although its goals were laudable, the campaign seemed underdeveloped: holding baristas responsible for having credible conversations about race without any training was a recipe for social disaster. Smooth was not the only one that felt this way. The Twittersphere responded on March 16, 2015, with the hashtag #NewStarbucksDrink as a critical response to the company’s Race Together campaign. The two hashtags #RaceTogether and #NewStarbucksDrink trended on Twitter for more than forty-eight hours, and regional, national, and international news outlets covered the controversy. According to NewsBank, 564 news stories appeared in four global regions—536 in the United States, 2 in Africa, 3 in Asia, and 23 in Europe—in March 2015.
Black Twitter, as a collective of users, is transnational and diverse in age, genders, sexualities, politics, class, ethnicities, and religious practices. After the global diversity of Black Twitter met the local attempt by the global coffee brand Starbucks to talk about race using the hashtag #RaceTogether, very little went right for the company. Anti-Black discourses are perverse and embedded in the everyday languages of all colonizing world powers. The countries where Black people live are infused not only with anti-Black discourses but also specifically with anti-Black discourses grounded in white supremacy. The conversations of colonization are pervasive and inextricably linked to capitalism in ways that mask the realities of anti-Black discourses within advertising. This has been noted explicitly by scholars examining how global companies have responded to critics who have called out insensitive or flat-out racist products or product campaigns.
The shared experiences of thriving under systems of oppression constitute the secret power of Blackness. Thriving occurs in several ways but perhaps most impressively through the grammars of Blackness. For the first time in modernity, we can witness the grammar of Blackness at work by everyday Black people throughout the diaspora and in real time. Black Twitter users provide a window into our collective Black present future while thriving in our present past.3
This chapter focuses on the Black grammar of signifying in three areas: (1) schooling as comic resistance, (2) loud talking, and (3) allegory as resistance through tweets in the #RaceTogether campaign. The resistance response hashtag #NewStarbucksDrink functions as a critical response using loud talking and schooling as comic resistance. Distant reading as rhetorical analysis understands the two hashtags as a call-and-response dialectic familiar in Africana discourse, demonstrating the Black grammar of shade (in two parts) as a form of signifying.
It Ain’t Slang, It’s Blackness: Coordinated Management of Meaning and Signifying Race
As humans, our ability to understand the world comes from socialization and social interaction. Our earliest experiences of socialization come through primary interpersonal relationships with parental/primary caregivers, siblings, cousins, grandparents, aunts, and uncles. Then, we develop secondary interpersonal relationships with friends, classmates, coworkers, and future romantic partners. Socialization also takes place through mediated communication, including television, radio, print media, internet, and social media. Through these interpersonal and meditated communicative interactions, we become socially constructed and engage in the active social construction and meaning-making that constitute who we are and how our world works. Our meaning-making process is not general; as W. Barnett Pearce and Sharon M. Rossi note, “It is particular to specific parents, family, clan, nation, culture, etc.”4 To these, we should add mediated experiences. The theory of the coordinated management of meaning (CMM) positions communication at the center of human interaction and socialization. It asserts that humans are socialized into social structures through a combination of particular verbal and nonverbal communicative practices.5 The particularity of these moments intersects as connections to others within the hierarchy of socialized spaces become more concretized over time.
As social actors and social participants, all of us are always connected to a socially constructed reality dependent on our interpersonal relationships with others. Pearce argues that CMM allows for a way to understand and theorize the rules and grammars of our particular connections.6 In other words, we are not islands. We learn, understand, and make meaning in connection to and collaboration with others. The development of CMM itself has proved the collaborative process of communicative rulemaking and relational grammars. Pearce and his many collaborators over the years, including Vernon E. Cronen, have developed CMM into a theory applicable to a variety of social spaces and human interactions. Cronen and Pearce’s articulations of CMM as a process to engage the hierarchies of socially constructed interactions and interpersonally created meaning emphasize two primary concepts: “constitutive” and “regulatory” rules. In his apt summary of CMM, Randell Rose describes constitutive rules as “employed by social actors to link the different levels of their meaning hierarchies with each other and with the unfolding action that is occurring.”7 He defines regulative rules in CMM as “meaning structures that have a temporal quality to them and relate to how individuals manage the unfolding sequence of actions in a social episode.”8 Each of these rules lives within the narrative structure.
CMM theory argues that we all construct narratives of meaning to connect and be connected to social worlds. These narratives provide perspective, depth, scope, temporal reality, material location, physical orientation, and emotional connections to others. We tell the story of who we are and how we came to be through varying entry points based on the audience. Because our relationships are not all the same—for example, friends are different from coworkers, coworkers are different from romantic partners—we differentiate our entrance to our narrative. The constitutive rule tells us that difference matters in how relationships occur. The same rule is used to create the coordinated management construction of coherent meaning out of relational context and cultural narrative. In other words, we need to make sense of different relationships and develop rules to engage others, so we categorize like relationships by asking questions. As small children, we ask if all women are our mother or all men are our father. Until we learn to distinguish between all men and the man that is our father, we see them all as the same. Once we learn the difference, we then ask relational questions to make sense of the events and constructions around us. We learn through relationships with our father and other fathers who fathers are. We determine through connection to our mother and to other mothers what mothers do. It is through continued connection and interaction that the constitutive rules are written.
Gates’s The Signifying Monkey outlines the Black grammar of signifying in ways not previously detailed. Essays and books by Jack Daniel, Geneva Smitherman, and others focus on the holistic of Black grammar, in which signifying is one of many elements.9 In the twenty-fifth-anniversary revised edition of The Signifying Monkey, Gates further refines his reading of meaning as a central tenet of Black grammar, making the argument that to use the syntax of signifying is to signal one’s belonging to the cultural groups of the Black diaspora.
Method of Application to the Dataset
The application of traditional rhetorical methods and content analysis in a mixed-method approach to human communication is not new. What is new is the extent to which scholars are using the tools and methods of big data to extend traditional rhetorical approaches. Using open-source tools to collect the tweets available on Twitter during and after the Race Together campaign requires a sort of academic dexterity. This means that making the best use of such tools often requires scholars interested in the intersection of popular culture, media, and race to be active participants in the cultures studied in order to know when to collect a trending topic. In other words, you have to be on Twitter to know when to collect tweets. Not all hashtags trend, and not all topics are of sufficient interest to capture the Black diasporic imagination. Big data rhetorical analysis, coupled with African American Rhetoric, borrows from the methodological practices common to content analysis while focusing on the qualitative expectations of rhetorical analysis.
I engaged in live data collection from the Twitter platform from March 2015 through August 2015 to garner significant findings and define unique data points for examining the use of both the #RaceTogether and #NewStarbucksDrink hashtags by Twitter users. The Streaming API is the most widely used dataset for research on Twitter.10 As Devin Gaffney and Cornelius Puschmann note, “The Streaming API is push-based—that is, the data is constantly flowing from the requested URL (the endpoint), and it is up to the researcher to develop or employ tools that maintain a persistent connection to this stream . . . while processing it.”11 The two hashtags were initially collected using the open-access software tool by Hawksey called TAGS. The data obtained with TAGS uses Twitter’s API, which enabled a more in-depth analysis in real time rather than an archived dataset available for purchase. For rhetorical scholars interested in big data, TAGS provides a method for collecting comments, discourse, and opinions on topics of public interest. The benefit of using TAGS as a collection tool is the ability to set parameters for the collection of tweets. Each of the sheets created to collect tweets was configured to complete an auto-collection of tweets available every one to thirty minutes during the first fifteen days of the collection. TAGS, however, does have its limits. As noted earlier, one must be dexterous to do this work. In 2015, TAGS did not allow for past data collections; if I had begun my collection on March 20, 2015, I would not have been able to capture the tweets from the beginning of the hashtag when it was trending. Although TAGS currently can be set to search for tweets seven days in the past, it is still limited in its ability to collect tweets sent before the collection start date. Thus, TAGS is best as a present and future collection tool.
The dataset for this analysis was collected from two hashtags directly related to the Starbucks Race Together campaign: #RaceTogether and #NewStarbucksDrink. All grammatical variations of the hashtag were collected (i.e., #racetogether and #newstarbucksdrink). However, versions of the hashtag where spelling errors occurred within the hashtag were not collected because data collection was automated and not done manually. The total number of tweets collected in two TAGS sheets was 128,277. Data collection for the hashtag #RaceTogether began on March 17, 2015, and continued to March 31, 2015, yielding the automated collection of 63,264 tweets. The hashtag #NewStarbucksDrink data were collected on March 17, 2015, for a twenty-four-hour period; 10,757 tweets were collected. Given the overwhelming response to Starbucks’ announcement that baristas would engage in conversations about race, it seemed appropriate to focus on the reactions on Twitter within the first forty-eight hours of the announcement. Within the initial forty-eight hours, the #RaceTogether dataset included 21,000 tweets, and the #NewStarbucksDrink dataset included 10,749 tweets. This chapter focuses on the 31,749 generated by both hashtags from March 17, 2015, through March 18, 2015.
In addition to using TAGS, I used Voyant Tools for text analysis of the corpus created with the two hashtags. The most basic frequency analysis typically used in content analysis work requires a hand count of the most frequent terms used. Voyant Tools provides frequency analysis within the individual hashtag datasets and comparative analysis. Additional tools, such as relational scatterplots and relational frequency analysis of terms, were used to determine the number of unique words in the dataset. Unique words and unique word pairs are particularly crucial in the analysis of signifying in Black English, as noted by Geneva Smitherman in her groundbreaking text, Talkin and Testifyin. The visualizations possible with Voyant Tools provide an opportunity for scholars to share their datasets without violating the privacy standards set forth by Twitter. For this chapter, only the text of the tweets was used. Other identifying information found in the TAGS sheet attached to each tweet was excluded so as not to reduce the text analysis capabilities of Voyant Tools.
“Somebody on Here Said Chai Felicia”: Signifyin’ in Big Data
In December 2016, I attended the 30th Biannual Symposium on African American Culture & Philosophy at Purdue University, where Marisa Parham of Amherst College delivered a lunch plenary titled “Decolonizing the Digital.” In her speech, Parham stated, “Bots don’t signify.” She explained that when they do, Black folks will be in trouble. How? Why? What? (Imagine these single-word questions posed in the voices of Heben Nigatu and Tracy Clayton as heard on their podcast, Another Round). My mind was blown. I began to think about how Black languages and the grammar of Blackness, as defined by Hortense Spillers, are grounded in the lived experiences of Black identities throughout the diaspora.12 I also began to think critically about what my father said about how he learned and how he taught me to signify, to speak, to relate. The attendant ideas of creation and commodification of nature became equally essential to consider. I began to wonder, “If signifying is a learned practice, then what limits the learning of bots?” This is a question far in advance of where we are in the current cultural moment and so light-years in advance of where the cultural moment was in 2015. In looking back on the datasets provided in #RaceTogether and #NewStarbucksDrink,13 it became clear that, before bots learned to signify, white people would have to learn to talk about race without making every person of color cringe, roll their eyes, or stare into their Starbucks coffee cups and then begin a complicated cultural and economic calculus before throwing their drinks on people, walking away in disgust, or both. It is this calculus and cultural negotiation that led to perhaps the funniest and most educational moment of signifying on Twitter in 2015.
Think back to the racial and larger cultural moment of 2015. There had been more than twenty-five significant killings or deaths of unarmed Black people at the hands of the police or vigilante civilians and not one conviction. The nation of birth of the president of the United States was still being questioned, despite his having provided ample proof that he was born in this country. A lot was happening, and in an attempt to address them, Starbucks, a socially aware coffee company, decided it could help. Its audience/consumers, specifically its Black consumers/audience, did not seem to agree. Perhaps what is most interesting and rhetorically evident about the use of the hashtag #NewStarbucksDrink is how the audience of speakers and readers needed the attendant and intersectional knowledges of being raced as Black in America, being members of at least the middle class (as evidenced by knowledge of the ordering parlance and cost of Starbucks drinks), being educated (evidenced in the puns that turned on literary and historical knowledges), and being aware of gender (evidenced in the gendered ways in which signifyin’ featured around “talk” as gendered unpaid labor). Each of these moments required intimate knowledge of the rules of signifyin’ and of what Goodman and Light indicate are central elements to the analysis of big data Twitter-based research.14
Loud talking is not always about volume. It is defined as a form of signifyin’ where person A speaking to person B comments about person C without mentioning their name or speaking directly to them.15 Loud talking turns on the understanding that everyone in the conversation understands that person A is calling into question some decision made by person C. Persons A and B follow this declaration as something that person C should know better than to do and should be publicly corrected because the results of the determination are in public view or occur within an open space. One example of loud talking is the tweet from user@CarolHenny that is incorporated into the title of this chapter: “WHITE PRIVILEGE AMERICANA EXTRA WHIP #NewStarbucksDrink.” The phrase “White Privilege Americana Extra Whip” appears in all caps, so it is possible to read it as shouted or loud in volume. However, as mentioned, loud talking is not always about volume. Person A (user@CarolHenny) is not screaming her comments about person C (Starbucks) at person B (Twitter users who consider themselves members of the Black Twitter community). Person A is speaking loud enough for person C to hear and, in fact, is prepared for the continuation of the conversation when Person A can directly correct the behavior in question, usually through comments that reflect the idea that person C is only responding to the loud talk because they know they were wrong. In this way, signifyin’ by audience/speakers on #NewStarbucksDrink gave the audience/speakers a means to engage the Starbucks brand in public schooling and corrective loud talking.
In other examples of the use of the #NewStarbucksDrink, Person A—signifying all users who wrote original tweets—used the parlance of ordering flavored drinks, thereby demonstrating their belonging to both the Starbucks customer/brand community and to the Black community through stylings of statements that brought forth the understanding of what it means to be raced Black in the United States. Person B represents the audience/speakers who retweeted Person A in the form of agreement, thus adding to the spreadability of the signified.16
- @katieg1975 “12 Years a Barista #NewStarbucksDrink” (appearing twenty-four times)
- RT @that_crazy_jen: Police Brew-tali-Tea #NewStarbucksDrink (appearing fifty-three times)
- RT @JenJamesBeauty: “No Justice, No Tea” #newstarbucksdrink (appearing twenty-five times)
These retweets appeared in the dataset as original and retweeted (RT) examples. Decoding this moment of signifyin’ required an understanding of the historical processes of race and structural racism present in the daily lives of Black peoples. The tweets were a response to what the cross section of Black Twitter users who are Starbucks customers felt was a false or, at best, a commercialized attempt to extort labor from Black communities. The number of times a tweet was retweeted allows for an understanding of its amplification in the ways Sam Ford, Joshua Green, and Henry Jenkins discuss the term in their book Spreadable Media.17 That the hashtag #NewStarbucksDrink trended in the United States and Canada demonstrates both the ubiquity of white supremacy as grammar but also the use of signifyin’ as a grammar of Blackness designed as a required response.
Some were not satisfied with this form of rhetorical corrective, however. For those who preferred the direct schooling approach of signifyin’, the combination of the hashtags #RaceTogether and #NewStarbucksDrink provided a means by which to speak directly to Starbucks. One user tweeted “Separate But Equal (Or Splenda) #NewStarbucksDrink #RaceTogether.” This tweet presented the direct schooling approach using the language of the past to call out a race-related policy while calling on the contemporary reader to think through how the conversation on race would go over coffee. One can see the possibility for misunderstanding brought forward by the “Equal (Or Splenda)” moment.
By talking directly on the company-branded hashtag #RaceTogether, users of the schooling approach took the opportunity to reflect on the public call of Starbucks CEO Howard Schultz for partner employees (baristas) to engage in a conversation about race with customers by writing these words on their cups: “Race Together.” Schultz furthered his goal of creating a discussion about race by launching the Race Together campaign on social media and the company website. This visualization of the #RaceTogether hashtag increased its visibility and provides a unique window into the ways diverse customers responded.18 It was not only Black American customers who found the campaign problematic. Twenty percent of the tweets in the dataset were from customers who expressed feelings of exasperation at being called racist because they were white. This response by white and white-presenting customers elicited responses such as the following:
- “RT @IjeomaOluo: Wait—is #RaceTogether really just people talking about race over a cup of white privilege?”
- RT @VodkaPundit: I’ll have a half double decaffeinated half-caf, with a twist of white privilege.
Both of these tweets represent the views of those customers and Twitter users who questioned the goals and sincerity of Schultz and others. The questions, criticism, and signifyin’ ultimately led Starbucks to abandon the campaign by the end of the month.
Analysis of #RaceTogether denotes that Schultz was correct in his assessment that there needed to be room in American public discourse to talk about race. With the benefit of time and open-source tools that enabled distant reading, I found that customers who presented as Black or brown presumed that, by being asked to discuss race with baristas, they would have to engage in more unpaid labor, and they were just not interested in having those conversations without context, warning, or coffee. For white customers, unaccustomed and unprepared to have conversations about race, the idea seemed only to stir feelings of resentment. Pointing to the #NewStarbucksDrink hashtag that they rightly coded as criticism, white customers took to the #RaceTogether hashtag to vent frustrations at being perceived as racist only because they were unable to have a knowledgeable conversation. Ultimately, a conversation on race is only as productive as the participants are informed and prepared to hold it.
“Stop Resisting, or You’ll Get Extra Whips Mocha Grande #NewStarbucksDrink”: How It All Ended
Evidently, the goal of Schultz to have a conversation on race in 2015 was not well received in all quarters. As shown through this analysis, many customers and Twitter users who spoke out about the #RaceTogether campaign used signifying and shade to respond to the company within a culturally specific discourse. What is unique about this moment is the way that Black Twitter users came to critique the company using the sophisticated combination of Starbucks ordering language and culture along with signifyin’ focused on (1) schooling as comic resistance, (2) loud talking, and (3) allegory as resistance. The success of Black Twitter users in schooling Starbucks is evidenced in the length of time both hashtags trended and their global reach. One could argue that, even though the company ended the campaign within the first month, the later partnership with Black cultural figures like musician and actor Common may have been an attempt to respond to the schooling that happened in both hashtags.
As is the case with rhetorical work focused on critical race studies and culture, there are always new moments, and the incident at a Philadelphia Starbucks is the perfect example. In April 2018, one of the store managers called the police on two African American patrons whose only offense seemed to be waiting in the store for a third person to join them, without ordering coffee. The two African American businessmen were taken into custody by the police amid the vocal resistance of other patrons. The patrons who expressed their disagreement with the arrest were of varied ethnic backgrounds. In response to this police intervention, many of the community members came to this Starbucks location and to locations around the country to protest Starbucks management. The arrest of Black patrons for doing normal Starbucks things, including “waiting” and “requesting to use the bathroom,” was noted by everyone from Twitter users to cultural critics to MSNBC hosts Joy-Ann Reid and Stephanie Rhule. The company’s quick response on social and traditional media, as well as a company-wide one-day closure for training on racial bias, may have been facilitated by the now-former CEO’s 2015 foray into race and communication. The incident and the company’s response speak to the questions, concerns, and undercurrents in the responses to the #RaceTogether and #NewStarbucksDrink hashtags.
Clearly, in 2015 Schultz and the executives at Starbucks had not thought through the level of work that they were asking baristas and local management to undertake. By 2018, it wasn’t clear they had gained understanding of the work and space required to hold meaningful conversations on race. The critics of #RaceTogether, who through their tweets called for more extensive training of baristas and managers before engaging in dialogue about race over coffee, were correct. Future research could consider both the 2015 campaign and the corporate response to the 2018 event, as well as the racism evidenced in the #RaceTogether hashtag.
Gates discusses relational belonging in The Signifying Monkey as the way that people signify their relationship to and belonging in Black communities.
“Race Together,” Starbucks’ Stories and News, March 17, 2015, https://news.starbucks.com/news/race-together-conversation-has-the-power-to-change-hearts-and-minds.
Taylor “We Speak, We Make, We Tinker.”
Pearce and Rossi, “The Problematic Practice of ‘Feminism.’”
Pearce, Interpersonal Communication; Pearce, “The Coordinated Management of Meaning.”
Pearce, Communication and the Human Condition.
Rose, “A Proposal,” 178.
Rose, “A Proposal,” 178.
See Smitherman, Talkin and Testifyin, and Daniel and Smitherman, “How I Got Over.”
Gaffney and Puschmann, “Data Collection on Twitter,” 56.
Gaffney and Puschmann, “Data Collection on Twitter,” 56.
Spillers, “Mama’s Baby, Papa’s Maybe.”
Goodman and Light, “Coding Twitter.”
Gates, The Signifying Monkey.
Ford, Green, and Jenkins, Spreadable Media.
Ford, Green, and Jenkins, Spreadable Media.
Daniel, Jack L., and Geneva Smitherman. “How I Got Over: Communication Dynamics in the Black Community.” Quarterly Journal of Speech 62, no. 1 (2009): 26–39.
Ford, Sam, Joshua Green, and Henry Jenkins. Spreadable Media: Creating Value and Meaning in a Networked Culture. New York: NYU Press, 2018.
Gaffney, Devin, and Cornelius Puschmann. “Data Collection on Twitter.” In Twitter and Society, edited by Katrin Weller, Axel Bruns, Jean Burgess, Merja Mahrt, and Cornelius Puschmann, 55–68. New York: Peter Lang, 2014.
Gates Jr., Henry Louis. The Signifying Monkey: A Theory of Afro-American Literary Criticism. Oxford: Oxford University Press, 2014.
Goodman, Noah, and Daniel Light. “Coding Twitter, Lessons from a Content Analysis of Informal Science.” EDC Center for Children Technology, 2016.
Pearce, W. Barnett. Communication and the Human Condition. Carbondale: Southern Illinois University Press, 1989.
Pearce, W. Barnett. “The Coordinated Management of Meaning (CMM).” In Theorizing about Intercultural Communication, edited by William B. Gudykunst, 35–54. Thousand Oaks, Calif.: Sage, 2005.
Pearce, W. Barnett. Interpersonal Communication: Making Social Worlds. New York: HarperCollins, 1994.
Pearce, W. Barnett, and Sharon M. Rossi. “The Problematic Practices of ‘Feminism’: An Interpretive and Critical Analysis.” Communications Quarterly 32, no. 4 (1984): 277–86.
#RaceTogether. http://voyant-tools.org/?corpus=30031a1f1f0f1504c23a8682fdf3862a&stopList=keywords-de2803c285ed47530ca79fe076d93b90&whiteList=&docId=bcb6b9b85755a11e017ddfcc8fa1545b&view=Cirrus. Word Cloud. Voyant Tools.
Rose, Randall A. “A Proposal for Integrating Structuration Theory with Coordinated Management of Meaning Theory.” Communication Studies 57, no. 2 (2006): 173–96.
Sinclair, Stéfan, Geoffrey Rockwell, and the Voyant Tools Team. Voyant Tools (web application). 2012.
Smitherman, Geneva. Talkin and Testifyin: The Language of Black America, rev. ed. Detroit: Wayne State University Press, 1986.
Spillers, Hortense J. “Mama’s Baby, Papa’s Maybe: An American Gramma Book.” Diacritics 17, no. 2 (1987): 65–81.
Starbucks’ Stories and News. “Race Together: Conversation Has the Power to Change Hearts and Minds.” Last modified March 17, 2015, https://news.starbucks.com/news/race-together-conversation-has-the-power-to-change-hearts-and-minds.
Taylor, Toniesha L. “We Speak, We Make, We Tinker: Afrofuturism as Applied Digital Humanities.” In The Black Speculative Arts Movement: Black Futurity, Art+Design, edited by Reynaldo Anderson and Clinton Fluker, 55–58. Lanham, Md.: Lexington Books, 2019.