Mobile Search Behaviors: An In-depth Analysis Based on Contexts, APPs, and Devices

Dan Wu · Shaobo H. Liang

2022年5月 · Springer Nature

關於本電子書

With the rapid development of mobile Internet and smart personal devices in recent years, mobile search has gradually emerged as a key method with which users seek online information. In addition, cross-device search also has been regarded recently as an important research topic. As more mobile applications (APPs) integrate search functions, a user's mobile search behavior on different APPs becomes more significant. This book provides a systematic review of current mobile search analysis and studies user mobile search behavior from several perspectives, including mobile search context, APP usage, and different devices. Two different user experiments to collect user behavior data were conducted. Then, through the data from user mobile phone usage logs in natural settings, we analyze the mobile search strategies employed and offer a context-based mobile search task collection, which then can be used to evaluate the mobile search engine. In addition, we combine mobile search with APP usage to give more in-depth analysis, such as APP transition in mobile search and follow-up actions triggered by mobile search. The study, combining the mobile search with APP usage, can contribute to the interaction design of APPs, such as the search recommendation and APP recommendation. Addressing the phenomenon of users owning more smart devices today than ever before, we focus on user cross device search behavior. We model the information preparation behavior and information resumption behavior in cross-device search and evaluate the search performance in cross-device search. Research on mobile search behaviors across different devices can help to understand online user information behavior comprehensively and help users resume their search tasks on different devices.

關於作者

Dan Wu ([e-mail]) is a full Professor of Library and Information Science at Wuhan University. She is the department chair of the Library Science Department, School of Information Management. Dan is the National Youth Top-notch Scholar of China, and the Luojia Distinguished Professor of Wuhan University. She graduated from Peking University with a Ph.D. in Information Science and was a visiting scholar at the University of Pittsburgh from 2006-2007. She is now a Fulbright Scholar at the University of North Carolina at Chapel Hill as well as the team leader of the Human-Computer Interaction Team of Wuhan University. Dan's research interests span information retrieval, information behavior, human-computer interaction, and multilingual information processing. She has published 7 books and over 150 papers in important academic journals and conferences including Information Processing and Management, Journal of Information Science, Aslib Journal of Information Management, SIGIR, CIKM, CSCW, and JCDL, 21 of which are indexed by SSCI/SCI. She has been the Principal Investigator (PI) of over 20 projects, which are funded by such sources as the National Natural Science Foundation of China and the National Social Science Foundation of China. She serves on the editorial board for The Electronic Library, Journal of Convergence Information Technology, Journal of Data and Information Science, and Data and Information Management.Shaobo Liang ([e-mail]) is a Ph.D. candidate of Library and Information Science at Wuhan University. His research interests lie in understanding user information-seeking behavior, mobile search behavior, and cross-device search. He has published 13 papers in academic journals and conferences, 3 of which are indexed by SSCI. He has given presentations at several international conferences, such as JCDL and iConference. He is also a member of the Chinese American Librarians Association (CALA) and a member of the Association for Computing Machinery (ACM). He has received many honors including the Best Chinese Paper Nomination Award of iConference 2017 and the Chinese American Librarians Association (CALA) Scholarship for Excellent Student.