Composition Forum 37 - Writing through Big Data: New Challenges and Possibilities for Data-Driven Arguments

Oct 15, 2026

Uploaded by jay

Writing through Big Data examines how multimodal writing evolves in the era of data mining, data visualization, and data-driven arguments. It argues that students’ real experiences of data-driven rhetoric are far broader than the label “data literacy” suggests, ranging from social-media “likes” to sentiment mapping, election prediction, and trending algorithms. The course addresses surveillance, privacy, and algorithmic filtering by having students collect network data and produce their own multimodal data-driven arguments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Composition Forum 37, Fall 2017

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

Course focus and definitions

Political and ethical concerns in Big Data

Social network trends and project structure

Current Issue From the Editors Weblog Editorial Board Editorial Policy Submissions Archives Accessibility Search Composition Forum 37, Fall 2017 Writing through Big Data: New Challenges and Possibilities for Data-Driven Arguments Aaron Beveridge Abstract: 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 con

FAQs

What challenges does the article say writing studies must address in the Big Data era?

It highlights new challenges and possibilities tied to data mining, data visualization, and data-driven arguments, especially how these practices shape students’ experiences with multimodal writing.

How does the course “Writing through Big Data” respond to political and ethical concerns of Big Data?

It frames surveillance, privacy, and algorithmic filtering as central issues, having students collect social network data and create their own data-driven arguments.

What is the main object of study in the course, and how do students work with it?

Students study social network trends and, within Twitter’s network, collect and visualize trend data to produce multimodal, data-driven arguments over the semester.

Oct 15, 2026

Uploaded by jay