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"Written communication Data processing."
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Observing writing : insights from keystroke logging and handwriting
\"Observing Writing: Insights from Keystroke Logging and Handwriting is a timely volume appearing twelve years after the Studies in Writing volume Computer Keystroke Logging and Writing (Sullivan & Lindgren, 2006). The 2006 volume provided the reader with a fundamental account of keystroke logging, a methodology in which a piece of software records every keystroke, cursor and mouse movement a writer undertakes during a writing session. This new volume highlights current theoretical and applied research questions in keystroke logging and handwriting research that observes writing. In this volume, contributors from a range of disciplines, including linguistics, psychology, neuroscience, modern languages, and education, present their research that considers the cognitive and socio-cultural complexities of writing texts in academic and professional settings\"-- Provided by publisher.
Book
Writing and Digital Media
This indispensible volume reviews outstanding European, American and Australian research in the cognitive, social and cultural implications of writing for digital media. It addresses writing modes and environments, writing and communication, digital tools for writing research, online educational environments, and social and philosophical aspects.
eBook
Observing Writing
Observing Writing shows how keystroke logging and handwriting logging provide windows onto the complex world of text production. This book contributes to the development of research questions, technical innovation, and user applications for writing observation tools.
eBook
Computer keystroke logging and writing: methods and applications
Computer keystroke logging is an exciting development in writing research methodology that allows a document's evolution to be logged and then replayed as if the document were being written for the first time. Computer keystroke logged data allows analysis of the revisions and pauses made by authors during the writing of texts. Computer Keystroke Logging and Writing: Methods and Applications is the first book to successfully collect a group of leading computer keystroke logging researchers into a single volume and provide an invaluable introduction and overview of this dynamic area of research.This volume provides the reader unfamiliar with writing research an introduction to the field and it provides the reader unfamiliar with the technique a sound background in keystroke logging technology and an understanding of its potential in writing research. In the core of the methods section, leading researchers demonstrate how keystroke logging can be used to analyze the writing process phenomena of the pause, the writing unit and the revision unit. These phenomena are illustrated with data from current keystroke logging research projects. The final section of the book explores a range of application possibilities for computer keystroke logging. These include how keystroke logging can be used to study how translators approach their work, how keystroke logging, alone or coupled with other techniques, can be used to examine theoretical proposals and models, and how keystroke logging can be used in pedagogical settings.
eBook
Multilingual text analysis : challenges, models, and approaches
Text analytics (TA) covers a very wide research area. Its overarching goal is to discover and present knowledge - facts, rules, and relationships - that is otherwise hidden in the textual content. The authors of this book guide us in a quest to attain this knowledge automatically, by applying various machine learning techniques. This book describes recent development in multilingual text analysis. It covers several specific examples of practical TA applications, including their problem statements, theoretical background, and implementation of the proposed solution. The reader can see which preprocessing techniques and text representation models were used, how the evaluation process was designed and implemented, and how these approaches can be adapted to multilingual domains.
Book
Large language models, social demography, and hegemony: comparing authorship in human and synthetic text
Large language models have become popular over a short period of time because they can generate text that resembles human writing across various domains and tasks. The popularity and breadth of use also put this technology in the position to fundamentally reshape how written language is perceived and evaluated. It is also the case that spoken language has long played a role in maintaining power and hegemony in society, especially through ideas of social identity and “correct” forms of language. But as human communication becomes even more reliant on text and writing, it is important to understand how these processes might shift and who is more likely to see their writing styles reflected back at them through modern AI. We therefore ask the following question: who does generative AI write like? To answer this, we compare writing style features in over 150,000 college admissions essays submitted to a large public university system and an engineering program at an elite private university with a corpus of over 25,000 essays generated with GPT-3.5 and GPT-4 to the same writing prompts. We find that human-authored essays exhibit more variability across various individual writing style features (e.g., verb usage) than AI-generated essays. Overall, we find that the AI-generated essays are most similar to essays authored by students who are males with higher levels of social privilege. These findings demonstrate critical misalignments between human and AI authorship characteristics, which may affect the evaluation of writing and calls for research on control strategies to improve alignment.
Journal Article
Deliberative Democracy in an Unequal World: A Text-As-Data Study of South India’s Village Assemblies
This paper opens the “black box” of real-world deliberation by using text-as-data methods on a corpus of transcripts from the constitutionally mandated gram sabhas, or village assemblies, of rural India. Drawing on normative theories of deliberation, we identify empirical standards for “good” deliberation based on one’s ability both to speak and to be heard, and use natural language processing methods to generate these measures. We first show that, even in the rural Indian context, these assemblies are not mere “talking shops,” but rather provide opportunities for citizens to challenge their elected officials, demand transparency, and provide information about local development needs. Second, we find that women are at a disadvantage relative to men; they are less likely to speak, set the agenda, and receive a relevant response from state officials. And finally, we show that quotas for women for village presidencies improve the likelihood that female citizens are heard.
Journal Article
Facebook post data: a primer for educational research
Facebook is widely used and researched. However, though the data generated by educational technology tools and social media platforms other than Facebook have been used for research purposes, very little research has used Facebook posts as a data source—with most studies relying on self-report studies. While it has historically been impractical (or impossible) to use Facebook as a data source, the CrowdTangle platform allows academic researchers to freely access the massive collection of posts on public Facebook pages and groups. In this paper, we first outline how interactions and textual features in these public Facebook data in concert with established methods from educational data mining and learning analytics can be used to scrutinize educational discourse and knowledge sharing at scale. We then provide a primer that offers considerations for researchers before collecting these data (i.e., conducting research ethically and framing the study). The tutorial also covers matters directly pertaining to using CrowdTangle: accessing the CrowdTangle platform, uploading or identifying pages (or groups), and downloading historical data and it includes code using the statistical software and programming language R. We conclude with ideas for future directions for using Facebook posts as data with a focus on how educational researchers can leverage the scale of the available data and the time periods for which data is available to study educational affairs (i.e., issues or topics) and individuals (i.e., people or organizations) and to scrutinize how Facebook itself is used.
Journal Article