Writing through Big Data: New Challenges and Possibilities for Data-Driven Arguments.

As multimodal writing continues to shift and expand in the era of Big Data, writing studies must confront the new challenges and possibilities emerging from data mining, data visualization, and data-driven arguments. Often collected under the broad banner of data literacy, students’ experiences of data visualization and data-driven arguments are far more diverse than the phrase data literacy suggests. Whether it is the quantitative rhetoric of “likes” in entertainment media, the mapping of social sentiment on cable news, the use of statistical predictions in political elections, or the pervasiveness of the algorithmic phrase “this is trending,” datadriven arguments and their accompanying visualizations are now a prevalent form of multimodal writing. Students need to understand how to read data-driven arguments, and, of equal importance, produce such arguments themselves. In Writing through Big Data, a newly developed writing course, students confront Big Data’s political and ethical concerns head-on (surveillance, privacy, and algorithmic filtering) by collecting social network data and producing their own data-driven arguments. As multimodal writing continues to shift and expand in the era of Big Data, writing studies must confront the new challenges and possibilities emerging from data mining, data visualization, and data-driven arguments. Often these challenges and possibilities are collected under the broader banner of data literacy, but for students, their everyday experience of data visualization and data-driven arguments is far more diverse than the phrase data literacy suggests. Whether it is the quantitative rhetoric of “likes” in entertainment media, the mapping of social sentiment on cable news, the use of statistical predictions in political elections, or the pervasiveness of the algorithmic phrase “this is trending,” data-driven arguments and their accompanying visualizations are now a prevalent form of multimodal writing. As the era of Big Data continues to reshape the invention, delivery, and discovery of digital content, which now includes the Internet of Things and the vast expansion of sensor-driven technologies, visualized streams of data will continue to increase the amount of automated decision making in everything from business management to refrigerators that sense when to order another carton of milk. The collecting, processing, and visualizing of data—the creation of data-driven arguments—should be understood as both statistical and rhetorical in nature. For classrooms centered on multimodal forms of writing, this means that students need to learn how to read data-driven arguments, and, of equal importance, how to produce such arguments themselves. As students read and produce data-driven arguments, Big Data’s larger political and ethical concerns come into frame. A recent collection of articles for Kairos, titled “Writing in an Age of Surveillance, Privacy, & Net Neutrality,” calls for interventions that address the “natural language systems that code and collect billions of posts, and tracking systems that follow our every click. ” As the authors explain, these writing systems “have fundamentally changed the spaces and places in which we compose, create, interact, research, and teach. ” As a response to such concerns, this article describes a newly developed writing course called “Writing through Big Data”{1} (WtBD). In WtBD students confront the political and ethical concerns of Big Data head-on by collecting writing data themselves and producing their own data-driven arguments. In Writing through Big Data social network trends are the primary object of study. In the context of this course, writing is defined under its broader conception that includes text, image, audio, video and any other digital artifact used to make or remix meaning. Social network trends are popular and sometimes viral topics that circulate among social networks such as Facebook, Twitter, YouTube, Reddit, Tumblr, Instagram, Snapchat, and many others. Specifically, for this course, students investigate trends occurring within Twitter’s network and visualize trend data to study the way writing moves and changes over time. Weekly workshops teach students how to collect and visualize writing data to produce multimodal, data-driven arguments about trends, and all of the work completed by students during CF 37: Writing through Big Data by Aaron Beveridge http://compositionforum.com/issue/37/big-data.php[11/29/2017 11:19:37 AM] the semester builds toward the final project in which students deliver their semester-long trend research in an oral slideware presentation. Through the use of online discussion forums, the trending topics and digital artifacts chosen by students provide thematic content for the course, allowing collaborative invention to emerge from a combination of technical practice, visual design, and engaged discussion. Two key reciprocal concepts undergird the rationale for Writing through Big Data. First and foremost, WtBD is a rhetoric and writing studies course where students research and write about networked writing. As Doug Eyman defines it in Digital Rhetoric: Theory, Method, Practice, networked writing functions as an “an ecology of circulation,” where digital networks provide a “framework that situates digital circulation within specific ecologies and economies of production: while circulation ecologies represent the places, spaces, movements, and complex interactions of digital texts as they are produced, reproduced, exchanged, or used” (84). Networked writing has taken many various forms since the invention of the Internet, and many new forms will no doubt continue to emerge. Due to the immense growth in worldwide users, and the influence that social networks have on culture, politics, economics, journalism, and entertainment, social networks provide a valuable resource for studying networked writing. By researching and writing about trending topics and digital artifacts they find meaningful, students develop a better understanding of their own networked writing practices. While social network trends provide a macroscopic view of large-scale patterns in networked writing, they also provide a way of understanding the networks themselves. As Sidney Dobrin explains regarding networked writing in Postcomposition, “writing-as-system interrelates with networks rather than suggest writing is itself an identifiable network” (181). According to Dobrin's understanding of “writing-as-system,” there is an indistinguishable reciprocity between writing and system—writing is structured by the technologies, systems, and networks that enable its production and circulation, but writing also restructures those same systems and networks as it saturates and permeates them: “Network emerges from writing and depends on its saturation, its fluctuation, and its mass. Without being saturated by and within writing, the network would neither emerge nor evolve, nor would the connections between the nodes and knots serve the network to any degree of circulation” (184). With #hashtags providing an organic reference tag functionality, trends often arise from the connections, topics, and digital artifacts already available within a network, but writing also moves and changes those systems by circulating new ideas and texts and forging new connections. While complex descriptions of networked writing may seem theoretically difficult for undergraduate students to grasp, trending topics and highly-circulated digital artifacts provide a non-threatening and familiar starting point for introducing more complex descriptions of networked writing. In addition to networked writing, the second key concept already mentioned is data literacy. Data literacy, as an offshoot of digital literacy, focuses specifically on the tools, techniques, and rhetorical practices of data-driven arguments. WtBD fosters data literacy by tuning students into the pervasive and ubiquitous uses of data visualization in academia, politics, journalism, and entertainment media as well as its rhetorical dimensions. In “Rhetorical Numbers: A Case for Quantitative Writing in the Composition Classroom,” Joanna Wolfe explains that “there is a paradox in that on one hand our culture tends to represent statistical evidence as a type of 'fact' and therefore immune to the arts of rhetoric, but on the other hand we are deeply aware and suspicious of the ability of statistics to be 'cooked,' 'massaged,' 'spun,' or otherwise manipulated. ” In other words, Wolfe argues, "Treating numbers as inherently truthful or inherently deceptive is equally naive” (453). In WtBD, students arrive at a rhetorical awareness of data visualization by collecting data on networked writing (trends) and by forming arguments about that data through infographics and visualizations they create themselves. For Writing through Big Data, data literacy is as much about finding productive intersections of rhetoric and statistical reasoning as it is about bridging the digital divide, increasing the availability and accessibility of data-intensive tools and methods, and putting all of this to work within topics and arguments that students find meaningful. Students spend roughly the first third of the course determining which trend they want to research throughout the rest of the semester. While trends are complex in how they emerge and how they may be observed within various networks, trends are often understood simply as popular topics or digital artifacts (such as memes or viral videos) that are written about or shared by a large number of users within a network. Trends may also arise from particular social or political events and then transform into broader issues or concepts that users continue to discuss. Often, these broader concepts become categorical tags that are used to create connections among other newer topics or events (associative connections made through the use of #hashtags or common words/phras