Reading Time Prediction Model on Chinese Technical Documentation

Zhijun Gao, Fan Li, Jingsong Yu
{"title":"Reading Time Prediction Model on Chinese Technical Documentation","authors":"Zhijun Gao, Fan Li, Jingsong Yu","doi":"10.1109/ProComm48883.2020.00046","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":311057,"journal":{"name":"2020 IEEE International Professional Communication Conference (ProComm)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Professional Communication Conference (ProComm)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ProComm48883.2020.00046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

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.
中文技术文献阅读时间预测模型
本文在“中国技术交流”特邀小组会议上发表。随着人们对网络材料的倾向,人们对网络文本的阅读时间和易读性进行了各种研究。与文本相关的属性,如字体大小或字母间距,是该字段中常用的变量。本研究的目的是探讨中文科技文献阅读时间的影响因素,并建立决策树模型来预测其阅读时间。在实验中,收集了一家云服务提供商网站上超过100万用户访问的日志数据。用户会话记录用户的访问时间、停留时间、访问步骤、访问设备等多个数据字段。除了来自日志文件的用户行为数据外,还收集有关技术文档本身的数据度量。对于实验中使用的所有文档,使用网络爬虫抓取它们的单词计数、图像计数、链接计数和章节计数。采用线性相关分析来探讨变量之间的相关性,以进行预测。结果表明,使用决策树模型可以达到75%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信