research-article

The last click: why users give up information network navigation

Authors:

Aju Thalappillil Scaria,

Rose Marie Philip,

Jure LeskovecAuthors Info & Claims

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Pages 213 - 222

https://doi.org/10.1145/2556195.2556232

Published: 24 February 2014 Publication History

Abstract

An important part of finding information online involves clicking from page to page until an information need is fully satisfied. This is a complex task that can easily be frustrating and force users to give up prematurely. An empirical analysis of what makes users abandon click-based navigation tasks is hard, since most passively collected browsing logs do not specify the exact target page that a user was trying to reach. We propose to overcome this problem by using data collected via Wikispeedia, a Wikipedia-based human-computation game, in which users are asked to navigate from a start page to an explicitly given target page (both Wikipedia articles) by only tracing hyperlinks between Wikipedia articles. Our contributions are two-fold. First, by analyzing the differences between successful and abandoned navigation paths, we aim to understand what types of behavior are indicative of users giving up their navigation task. We also investigate how users make use of back clicks during their navigation. We find that users prefer backtracking to high-degree nodes that serve as landmarks and hubs for exploring the network of pages. Second, based on our analysis, we build statistical models for predicting whether a user will finish or abandon a navigation task, and if the next action will be a back click. Being able to predict these events is important as it can potentially help us design more human-friendly browsing interfaces and retain users who would otherwise have given up navigating a website.

References

[1]

M. Ageev, Q. Guo, D. Lagun, and E. Agichtein. Find it if you can: A game for modeling different types of Web search success using interaction data. In SIGIR'11, 2011.

Digital Library

[2]

S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1), 1998.

Digital Library

[3]

E. H. Chi, P. Pirolli, K. Chen, and J. Pitkow. Using information scent to model user information needs and actions and the Web. In CHI'01, 2001.

Digital Library

[4]

A. Das Sarma, S. Gollapudi, and S. Ieong. Bypass rates: Reducing query abandonment using negative inferences. In KDD'08, 2008.

Digital Library

[5]

A. Diriye, R. W. White, G. Buscher, and S. Dumais. Leaving so soon? Understanding and predicting Web search abandonment rationales. In CIKM'12, 2012.

Digital Library

[6]

D. Downey, S. Dumais, D. Liebling, and E. Horvitz. Understanding the relationship between searchers' queries and information goals. In CIKM'08, 2008.

Digital Library

[7]

M. Harrigan, D. Archambault, P. Cunningham, and N. Hurley. EgoNav: Exploring networks through egocentric spatializations. In AVI'12, 2012.

Digital Library

[8]

J. Kleinberg. The small-world phenomenon: An algorithmic perspective. In STOC'00, 2000.

Digital Library

[9]

J. Li, S. Huffman, and A. Tokuda. Good abandonment in mobile and PC Internet search. In SIGIR'09, 2009.

Digital Library

[10]

S. Milgram. The small world problem. Psychology Today, 2, 1967.

[11]

C. Olston and E. H. Chi. ScentTrails: Integrating browsing and searching on the Web. TCHI, 10(3), September 2003.

Digital Library

[12]

J. Teevan, C. Alvarado, M. S. Ackerman, and D. R. Karger. The perfect search engine is not enough: a study of orienteering behavior in directed search. In CHI'04, 2004.

Digital Library

[13]

C. Trattner, D. Helic, P. Singer, and M. Strohmaier. Exploring the differences and similarities between hierarchical decentralized search and human navigation in information networks. In i-KNOW'12, 2012.

Digital Library

[14]

T. von Landesberger, M. Gorner, and T. Schreck. Visual analysis of graphs with multiple connected components. In VAST'09, 2009.

[15]

R. West and J. Leskovec. Automatic versus human navigation in information networks. In ICWSM'12, 2012.

[16]

R. West and J. Leskovec. Human wayfinding in information networks. In WWW'12, 2012.

Digital Library

[17]

R. West, J. Pineau, and D. Precup. Wikispeedia: An online game for inferring semantic distances between concepts. In IJCAI'09, 2009.

Digital Library

[18]

R. W. White and J. Huang. Assessing the scenic route: Measuring the value of search trails in Web logs. In SIGIR'10, 2010.

Digital Library

[19]

R. W. White and D. Morris. Investigating the querying and browsing behavior of advanced search engine users. In SIGIR'07, 2007.

Digital Library

[20]

R. W. White and A. Singla. Finding our way on the Web: Exploring the role of waypoints in search interaction. In WWW'11, 2011.

Digital Library

Cited By

Zhu MYasseri TKertész J(2024)Individual differences in knowledge network navigationScientific Reports10.1038/s41598-024-58305-214:1Online publication date: 9-Apr-2024
https://doi.org/10.1038/s41598-024-58305-2
Koopmann TBecker MLemmerich FHotho A(2024)CompTrails: comparing hypotheses across behavioral networksData Mining and Knowledge Discovery10.1007/s10618-023-00996-838:3(1258-1288)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-023-00996-8
Piccardi TGerlach MArora AWest R(2023)A Large-Scale Characterization of How Readers Browse WikipediaACM Transactions on the Web10.1145/358031817:2(1-22)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3580318
Show More Cited By

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The last click: why users give up information network navigation

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Published In

cover image ACM Conferences

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

February 2014

712 pages

ISBN:9781450323512

DOI:10.1145/2556195

General Chairs:
Ben Carterette
University of Delaware, USA
,
Fernando Diaz
Microsoft Research, USA
,
Program Chairs:
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Donald Metzler
Google, USA

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 February 2014

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WSDM 2014: Seventh ACM International Conference on Web Search and Data Mining

February 24 - 28, 2014

New York, New York, USA

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WSDM '14 Paper Acceptance Rate 64 of 355 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Zhu MYasseri TKertész J(2024)Individual differences in knowledge network navigationScientific Reports10.1038/s41598-024-58305-214:1Online publication date: 9-Apr-2024
https://doi.org/10.1038/s41598-024-58305-2
Koopmann TBecker MLemmerich FHotho A(2024)CompTrails: comparing hypotheses across behavioral networksData Mining and Knowledge Discovery10.1007/s10618-023-00996-838:3(1258-1288)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-023-00996-8
Piccardi TGerlach MArora AWest R(2023)A Large-Scale Characterization of How Readers Browse WikipediaACM Transactions on the Web10.1145/358031817:2(1-22)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3580318
Marisi R(2022)2. Distance Education Prompted by Covid-19 Lockdowns: A Challenge for Teaching Musical Performing ArtsReview of Artistic Education10.2478/rae-2022-000223:1(9-16)Online publication date: 30-Apr-2022
https://doi.org/10.2478/rae-2022-0002
Arora AGerlach MPiccardi TGarcía-Durán AWest RSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Wikipedia Reader NavigationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498496(16-26)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498496
He LShao BXiao YLi YLiu TChen EXia H(2022)Neurally-Guided Semantic Navigation in Knowledge GraphIEEE Transactions on Big Data10.1109/TBDATA.2018.28053638:3(607-615)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TBDATA.2018.2805363
Menghini CAnagnostopoulos AUpfal E(2021)How Inclusive Are Wikipedia’s Hyperlinks in Articles Covering Polarizing Topics?2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671943(1300-1307)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671943
Aldalur IPerez ALarrinaga F(2021)Web Augmentation as a Technique to Diminish User Interactions in Repetitive TasksIEEE Access10.1109/ACCESS.2021.31041879(112686-112704)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3104187
Piccardi TRedi MColavizza GWest R(2020)Quantifying Engagement with Citations on WikipediaProceedings of The Web Conference 202010.1145/3366423.3380300(2365-2376)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380300
Chen M(2020)Reducing Web Page Complexity to Facilitate Effective User NavigationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289324232:4(739-753)Online publication date: 1-Apr-2020
https://doi.org/10.1109/TKDE.2019.2893242
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