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The last click: why users give up information network navigation

Published:24 February 2014Publication 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.

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          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

          Copyright © 2014 ACM

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          Publication History

          • Published: 24 February 2014

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          WSDM '14 Paper Acceptance Rate64of355submissions,18%Overall Acceptance Rate498of2,863submissions,17%

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