Characterizing Stage-Aware Writing Assistance in Collaborative Document Authoring

TL;DR

This paper investigates the stages of document authoring in collaborative writing environments, analyzing user habits, document lifespans, and interaction logs to enable intelligent, stage-aware writing assistance.

Contribution

It introduces a comprehensive analysis of writing stages through surveys, qualitative studies, and log data, and demonstrates the feasibility of predicting stages for tailored support.

Findings

Writers progress through distinct conceptual phases.

Writing stages are linked to document lifespan.

Interaction logs can predict document stages.

Abstract

Writing is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement). Despite past research in understanding writing, Web-scale consumer and enterprise collaborative digital writing environments are yet to greatly benefit from intelligent systems that understand the stages of document evolution, providing opportune assistance based on authors' situated actions and context. In this paper, we present three studies that explore temporal stages of document authoring. We first survey information workers at a large technology company about their writing habits and preferences, concluding that writers do in fact conceptually progress through several distinct phases while authoring documents. We also explore, qualitatively, how writing stages are linked to document lifespan. We supplement these qualitative findings with an analysis of the longitudinal user interaction logs of a popular digital writing platform over several million documents. Finally, as a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document. Our results support the benefit of tools tailored to writing stages, identify primary tasks associated with these stages, and show that it is possible to predict stages from anonymous interaction logs. Together, these results argue for the benefit and feasibility of more tailored digital writing assistance.