Reading Time Prediction Model on Chinese Technical Documentation

This paper was presented at the Invited Panel session “Technical Communication in China”. There has been various research on the reading time and legibility of online texts with people's tendency to online materials. Text-related attributes like font size or letterspacing are commonly used variables in this field. The objective of this study is to investigate the influential factors on the reading time of Chinese technical documentation, and to build a Decision Tree model to predict its reading time. In the experiment, log data including information of over a million user visits from a cloud service provider's website are collected. User's visit time, stay time, visit step, visit device and many other data fields are recorded in a user session. In addition to user behavioral data from log files, data metrics concerning technical documentation itself are also collected. For all documents used in the experiment, their word counts, image counts, link counts and section counts are scraped using web crawlers. The linear correlation analysis is applied in order to explore the correlations between variables for predictions. The results show that a 75 percent accuracy is achieved using the Decision Tree model.