This article demonstrates the value of sentiment analysis for contextualizing audiences in Rhetoric of Health and Medicine (RHM) by comparing vaccine related newspaper comments to non-vaccine related comments in the New York Times from 2017–2023 (n = 22,330,999). Our results show that while all comments skew negative, following a similar trend line, after the emergence of COVID-19, vaccine related comments decouple from the negative trend of baseline non-vaccine comments, becoming more negative and volatile. These results raise additional questions about the nature of the negativity for vaccine related comments, and we provide a properly sampled dataset for follow-up research to encourage iterative investigation into the public response to vaccine policy. In addition to these findings, this article calls for broader engagement with Natural Language Processing (NLP) and data science in RHM.

With respect to AI writing technologies (AIWT), we pose three foundational questions about academic dishonesty. First, do writing instructors and students perceive differences between AI agents and human agents in classroom scenarios? Second, to what extent are writing instructor and student perceptions are aligned? Third, what types of writing scenarios are perceived as academic dishonesty? Answering these questions provides a baseline of comparison not only for future studies of AIWT collaboration but also contextualizes perceptions of human-to-human collaboration. We report on a large-scale experimental survey study that answers these questions using item response theory (IRT). Our findings demonstrate that while there are differences between AI and human agents of collaborations, writing instructors and students are generally aligned in their perceptions. Using a Rasch model, we find that academic dishonesty operates along a spectrum of textual production. Regardless of whether the collaborating agent is human or AI, the more an agent produces text, the more this collaboration is perceived as academic dishonesty. Conversely, the less text that is produced, the less this scenario is perceived as academically dishonest. In our discussion, we provide a data-driven heuristic to guide instructors and administrators.

It remains unclear if perceptions of academic dishonesty concerning artificial intelligence writing technologies (AIWTs) present new challenges or if they reflect prior, non-AI concerns. To structure this problem, we used a randomized control survey experiment. We compared student ( n = 603) and instructor ( n = 312) attitudes toward dishonesty in collaborations involving humans versus AIWT in 10 writing-related scenarios. Results suggest similar perception patterns among students and instructors, with both populations expressing significant differences in perceived dishonesty between AI and human collaborators in some scenarios. This experiment structures the problem of AI writing and academic dishonesty for future research in this emerging field.

Creating new emojis is predicated on a system of technical writing that lobbies for new emojis to the Unicode Consortium. Emojination, an activist collective working for cultural inclusivity, helps everyday people write proposals for inclusive and culturally sensitive emojis. Through a case study of Emojination, this article describes ways that Tactical Technical Communication can work toward cultural inclusivity within regulatory frameworks.

Identifying the effects of online templates, such as empty state pages (ESPs), sheds light on the user writing habits and best practices for user design. By using assemblage theory and extending previous studies of ESPs to grant proposal writing on the crowded-funded website Experiment.com, this large-scale study (n = 778) finds that required fields are more likely to be filled to the character limit than optional fields.

This article advocates for web scraping as an effective method to augment and enhance technical and professional communication (TPC) research practices. Web scraping is used to create consistently structured and well-sampled data sets about domains, communities, demographics, and topics of interest to TPC scholars. After providing an extended description of web scraping, the authors identify technical considerations of the method and provide practitioner narratives. They then describe an overview of project-oriented web scraping. Finally, they discuss implications for the concept as a sustainable approach to developing web scraping methods for TPC research.

This article uses a machine learning algorithm to demonstrate a proof-of-concept case for moderating and managing online comments as a form of content moderation, which is an emerging area of interest for technical and professional communication (TPC) researchers. The algorithm sorts comments by topical similarity to a reference comment/article rather than display comments by linear time and popularity. This approach has the practical benefit of enabling TPC researchers to reconceptualize content display systems in dynamic ways.

Providing contextualized, effective writing instruction for engineering students is an important and challenging objective. This article presents a needs analysis conducted in a large engineering college and introduces the faculty development program that was created based on that analysis. The authors advocate for sustained interdisciplinary collaboration to promote contextualized adoption and adaptation of best practices and testing of scalable strategies.

Chronos and kairos are often understood as separate from one another in discussions of rhetorical temporality. For online and other highly mediated contexts, however, chronos and kairos can be understood as deeply related and intertwined. Via the concept of transduction, this article introduces machine time, which describes rhetorical time across a broad range of digital contexts, including online discussion forums and computer code.

This article offers a methodology for conducting large-scale audience analysis called “big data audience analysis” (BDAA). BDAA uses distant reading and thin description to examine a large corpus of text data from online audiences. In this article, that corpus is approximately 450,000 online reader comments. We analyze this corpus through sentiment analysis, statistical analysis, and geolocation to identify trends and patterns in large datasets. BDAA can better prepare TPC researchers for large-scale audience studies.

This article examines how empty state pages (ESPs) constrain user-generated communication through the ethical lens of Bourdieu’s habitus. The authors define ESPs as interactive instructional templates that prompt users to input information to participate in an online network. Through a case study analyzing ~450,000 online comments from The New York Times, the authors find a direct connection between ESP elements, such as the character limit for comments, and online writers’ cultivated habitus.

This article investigates the strategies web-writers develop when their audiences respond to them via textual participation. Focusing on three web-writers who want to “continue the conversation,” this article identifies five major strategies to accomplish this aim: (a) editing after production, (b) quotation, (c) question posing, (d) naming secondary writers, and (e) textual listening. Using the lens of writer-audience tension, I find that due to these web-writers’ perceptions of audience, one that is partially externalized via the website’s template, the term audience itself may not be a discrete concept, but a fluid, evolving, and recursive one, in other words, ongoing. These perceptions of audience reflect the unending nature of online texts and are exemplified by these five strategies.