Robert West

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Robert West is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
The last click: Why users give up information network navigation Abandonment
Browsing
Information networks
Navigation
Wikipedia
Wikispeedia
WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining English 2014 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. 0 0
Drawing a Data-Driven Portrait of Wikipedia Editors Wikipedia
Editors
Web usage
Expertise
WikiSym English August 2012 While there has been a substantial amount of research into the editorial and organizational processes within Wikipedia, little is known about how Wikipedia editors (Wikipedians) relate to the online world in general. We attempt to shed light on this issue by using aggregated log data from Yahoo!’s browser toolbar in order to analyze Wikipedians’ editing behavior in the context of their online lives beyond Wikipedia. We broadly characterize editors by investigating how their online behavior differs from that of other users; e.g., we find that Wikipedia editors search more, read more news, play more games, and, perhaps surprisingly, are more immersed in popular culture. Then we inspect how editors’ general interests relate to the articles to which they contribute; e.g., we confirm the intuition that editors are more familiar with their active domains than average users. Finally, we analyze the data from a temporal perspective; e.g., we demonstrate that a user’s interest in the edited topic peaks immediately before the edit. Our results are relevant as they illuminate novel aspects of what has become many Web users’ prevalent source of information. 0 0
A data-driven sketch of Wikipedia editors Editors
Expertise
Web usage
Wikipedia
WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion English 2012 Who edits Wikipedia? We attempt to shed light on this question by using aggregated log data from Yahoo!'s browser toolbar in order to analyzeWikipedians' editing behavior in the context of their online lives beyond Wikipedia. We broadly characterize editors by investigating how their online behavior differs from that of other users; e.g., we find that Wikipedia editors search more, read more news, play more games, and, perhaps surprisingly, are more immersed in pop culture. Then we inspect how editors' general interests relate to the articles to which they contribute; e.g., we confirm the intuition that editors show more expertise in their active domains than average users. Our results are relevant as they illuminate novel aspects of what has become many Web users' prevalent source of information and can help in recruiting new editors. Copyright is held by the author/owner(s). 0 0
Drawing a data-driven portrait of Wikipedia editors Editors
Expertise
Web usage
Wikipedia
WikiSym 2012 English 2012 While there has been a substantial amount of research into the editorial and organizational processes within Wikipedia, little is known about how Wikipedia editors (Wikipedians) relate to the online world in general. We attempt to shed light on this issue by using aggregated log data from Yahoo!'s browser toolbar in order to analyze Wikipedians' editing behavior in the context of their online lives beyond Wikipedia. We broadly characterize editors by investigating how their online behavior differs from that of other users; e.g., we find that Wikipedia editors search more, read more news, play more games, and, perhaps surprisingly, are more immersed in popular culture. Then we inspect how editors' general interests relate to the articles to which they contribute; e.g., we confirm the intuition that editors are more familiar with their active domains than average users. Finally, we analyze the data from a temporal perspective; e.g., we demonstrate that a user's interest in the edited topic peaks immediately before the edit. Our results are relevant as they illuminate novel aspects of what has become many Web users' prevalent source of information. 0 0
Human wayfinding in information networks Browsing
Human computation
Information networks
Navigation
Wikipedia
Wikispeedia
WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web English 2012 Navigating information spaces is an essential part of our everyday lives, and in order to design efficient and user-friendly information systems, it is important to understand how humans navigate and find the information they are looking for. We perform a large-scale study of human wayfinding, in which, given a network of links between the concepts of Wikipedia, people play a game of finding a short path from a given start to a given target concept by following hyperlinks. What distinguishes our setup from other studies of human Web-browsing behavior is that in our case people navigate a graph of connections between concepts, and that the exact goal of the navigation is known ahead of time. We study more than 30,000 goal-directed human search paths and identify strategies people use when navigating information spaces. We find that human wayfinding, while mostly very efficient, differs from shortest paths in characteristic ways. Most subjects navigate through high-degree hubs in the early phase, while their search is guided by content features thereafter. We also observe a trade-off between simplicity and efficiency: conceptually simple solutions are more common but tend to be less efficient than more complex ones. Finally, we consider the task of predicting the target a user is trying to reach. We design a model and an efficient learning algorithm. Such predictive models of human wayfinding can be applied in intelligent browsing interfaces. 0 0
Automatically suggesting topics for augmenting text documents Data mining
Eigenarticles
Principal component analysis
Topic suggestion
Wikipedia
International Conference on Information and Knowledge Management, Proceedings English 2010 We present a method for automated topic suggestion. Given a plain-text input document, our algorithm produces a ranking of novel topics that could enrich the input document in a meaningful way. It can thus be used to assist human authors, who often fail to identify important topics relevant to the context of the documents they are writing. Our approach marries two algorithms originally designed for linking documents to Wikipedia articles, proposed by Milne and Witten [15] and West et al. [22], While neither of them can suggest novel topics by itself, their combination does have this capability. The key step towards finding missing topics consists in generalizing from a large background corpus using principal component analysis. In a quantitative evaluation we conclude that our method achieves the precision of human editors when input documents are Wikipedia articles, and we complement this result with a qualitative analysis showing that the approach also works well on other types of input documents. 0 0
Completing Wikipedia's hyperlink structure through dimensionality reduction Data mining
Graph mining
Link mining
Principal component analysis
Wikipedia
International Conference on Information and Knowledge Management, Proceedings English 2009 Wikipedia is the largest monolithic repository of human knowledge. In addition to its sheer size, it represents a new encyclopedic paradigm by interconnecting articles through hyperlinks. However, since these links are created by human authors, links one would expect to see are often missing. The goal of this work is to detect such gaps automatically. In this paper, we propose a novel method for augmenting the structure of hyperlinked document collections such as Wikipedia. It does not require the extraction of any manually defined features from the article to be augmented. Instead, it is based on principal component analysis, a well-founded mathematical generalization technique, and predicts new links purely based on the statistical structure of the graph formed by the existing links. Our method does not rely on the textual content of articles; we are exploiting only hyperlinks. A user evaluation of our technique shows that it improves the quality of top link suggestions over the state of the art and that the best predicted links are significantly more valuable than the 'average' link already present in Wikipedia. Beyond link prediction, our algorithm can potentially be used to point out topics an article misses to cover and to cluster articles semantically. Copyright 2009 ACM. 0 0
Wikispeedia: An online game for inferring semantic distances between concepts IJCAI International Joint Conference on Artificial Intelligence English 2009 Computing the semantic distance between real-world concepts is crucial for many intelligent applications. We present a novel method that leverages data from 'Wikispeedia', an online game played on Wikipedia; players have to reach an article from another, unrelated article, only by clicking links in the articles encountered. In order to automatically infer semantic distances between everyday concepts, our method effectively extracts the common sense displayed by humans during play, and is thus more desirable, from a cognitive point of view, than purely corpus-based methods. We show that our method significantly outperforms Latent Semantic Analysis in a psychometric evaluation of the quality of learned semantic distances. 0 0