Introduction from The Viral Texts Project
This site presents data, visualizations, interactive exhibits, and both computational and literary publications drawn from the Viral Texts project, which seeks to develop theoretical models that will help scholars better understand what qualities—both textual and thematic—helped particular news stories, short fiction, and poetry “go viral” in nineteenth-century newspapers and magazines. During this period, texts published in newspapers and magazines were not typically protected as intellectual property, and so literary texts as well as other non-fiction prose texts circulated promiscuously among newspapers as editors freely reprinted materials borrowed from other venues. In the Viral Texts project, we ask: What texts were reprinted and why? How did ideas—literary, political, scientific, economic, religious—circulate in the public sphere and achieve critical force among audiences? By employing and developing computational linguistics tools to analyze the large textual databases of nineteenth-century newspapers newly available to scholars, this project will generate new knowledge of the nineteenth-century print public sphere.
Who:
Primary Investigators
- Ryan Cordell, Associate Professor of Information Sciences and English, University of Illinois Urbana-Champaign
- David A. Smith, Associate Professor of Computer Science, Northeastern University
Graduate Researchers
- Avery Blankenship, Ph.D. candidate, English Department, Northeastern University
- Daniel Evans, Ph.D. student, UIUC iSchool
- Jess Frye, Ph.D. student, UIUC iSchool
- Matthew Kollmer, Ph.D. student, UIUC iSchool
- Abraham Frank, MSLIS student, UIUC iSchool
- Amanda Riggenbach, MSLIS student, UIUC iSchool
Project Alumni
- Elizabeth Maddock Dillon, Professor of English, Northeastern University
- Jay Park; (then) Ph.D. Student, UIUC iSchool; (now) Assistant Professor, Nanyang Technological University
- Jonathan D. Fitzgerald; (then) Ph.D. student, English Department, Northeastern University; (now) Assistant Professor of Humanities, Regis College
- Thanasis Kinias; (then) Ph.D. candidate, History Department, Northeastern University; (now) Assistant Professor of Instruction, History Department, University of Texas
- Abby Mullen; (then) Ph.D. student, History Department, Northeastern University; (now) Research Faculty, Roy Rosenzweig Center for History and New Media, George Mason University
- Shaobin Xu, Ph.D. student, College of Computer Science, Northeastern University
- Kevin Smith; (then) Ph.D. student, English Department, Northeastern University; (now) Assistant Professor, English Department, University of Virginia
- Peter Roby, (then) MA student, English Department, Northeastern University
- Matthew Wilkinson, (then) Ph.D. student, History Department, Northeastern University
Outside Collaborators
- Lou Foster, Nick Laiacona, and Kristin Jensen, Performant Software Solutions
Where:
The Viral Texts project was made at Northeastern University in collaboration with the University of Illinois Urbana-Champaign and its NULab for texts, maps, and networks. Its funded by grants from the organizations Mellon Foundation, the National Endowment for the Humanities, and the American Council of Learned Societies.
When:
The project was planned between 2012-2014 before it gained outside support in 2015-2016. The project ended in 2016.
What:
The project collaborates with different scholars to understand and gather newspaper data that they can then use to map anti-black publications and how virality started with such topics.
Detailed description of scope of project
The scope of the project was to collect and place newspaper articles using similar or the exact same language into clusters that show what and where phrases went viral.
Establish importance/significance of project to area of study AND DH
What the project aims to find is relevant to modern politics and even historians that look back to slavery and segregation. To gather physical media from newspapers could easily change the general public’s access to old texts and what it could mean for future projects.
Unfortunately, they soon realized that collecting such big data does not work. The effort needed to go in and analyze and compute the data was not achievable.