I research the ways that writers contend with interactive and participatory audiences on the Internet, including via user design. I teach technical writing, including search engine optimization, data visualization, visual rhetoric, interviewing techniques, document design, and digital rhetoric. I value students as holistic people and not a set of skills.
I study the way writers contend with participatory audiences in the context of Web 2.0. I specialize in qualitative research methods; in particular, case study methodologies. I also draw on quantitative methods, such as web-scraping and computational analysis, to study online comments and participatory audiences.
- Writing Studies
- Audience Theory
- Web 2.0
- New Media
- Online Participatory Cultures
- User design
- Online ethics
- Search Engine Optimization
- BTW 250
- BTW 490
- ENGL 582
- ENGL 380
- ENGL 482
Gallagher, J. R. (2020). Update Culture and the Afterlife of Digital Writing. Utah State University Press. http://www.jstor.org/stable/j.ctvvh85pr
Gallagher, J. R., & DeVoss, D. N. (Eds.) (2019). Explanation Points: Publishing in Rhetoric and Composition. Utah State University Press.
Zhu, J., Wickes, E., & Gallagher, J. R. (Accepted/In press). A Machine Learning Algorithm for Sorting Online Comments via Topic Modeling. Communication Design Quarterly. https://doi.org/10.1145/3453460.3453462
Gallagher, J., Turnipseed, N., Yoritomo, J. Y., Elliott, C. M., Cooper, S. L., Popovics, J. S., Prior, P. A., & Zilles, J. (2020). A collaborative longitudinal design for supporting writing pedagogies of STEM faculty. Technical Communication Quarterly, 29(4), 411-426. https://doi.org/10.1080/10572252.2020.1713405
Gallagher, J. R., & DeVoss, D. N. (Eds.) (2020). Data Visualization in Composition Studies. Kairos, 25(1).
Gallagher, J. R. (2020). Machine Time: Unifying Chronos and Kairos in an Era of Ubiquitous Technologies. Rhetoric Review, 39(4), 522-535. https://doi.org/10.1080/07350198.2020.1805573
Gallagher, J. R., Chen, Y., Wagner, K., Wang, X., Zeng, J., & Kong, A. L. (2020). Peering into the Internet Abyss: Using Big Data Audience Analysis to Understand Online Comments. Technical Communication Quarterly, 29(2), 155-173. https://doi.org/10.1080/10572252.2019.1634766