What Makes a Link Successful on Wikipedia?

Dimitrov, Dimităr; Singer, Philipp; Lemmerich, Florian; Strohmaier, Markus

doi:10.1145/3038912.3052613

Cited by 43 publications

(48 citation statements)

References 52 publications

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“…This hints to the fact that -to some extent -users enter Wikipedia by search on more central articles, and then navigate outwards from more to less central nodes. This is consistent with previous findings studying navigation on Wikipedia [7]. Regarding articles with different topical alignments, we see certain evidence that the thematic domain of a user's information pursuit seems connected with the "mode" of how this information is attained.…”

Section: Discussionsupporting

confidence: 92%

“…Dimitrov et al conducted a large-scale study on the navigational behavior on Wikipedia. They found that users tend to select links located in the beginning of Wikipedia articles and links leading to articles located in the network periphery [6,7]. By constructing a navigational phase space from transition data, Gilderslave and Yasseri studied internal navigation on Wikipedia and identified articles with extreme, atypical, and mimetic behavior [11].…”

Section: Related Workmentioning

confidence: 99%

“…Information consumption on the Web has been of special interest to researchers since the Web's earliest days [13,14]. While both search [21,32,34] and navigation [7,11,[15][16][17] have been investigated thoroughly in related work, they were mostly looked at separately. Consequently, so far little is known about which parts and content types of a specific website (inter)act in which structural roles, begetting different information access patterns.…”

Section: Introductionmentioning

confidence: 99%

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Query for Architecture, Click through Military

Dimitrov

Lemmerich

Flöck

et al. 2018

Proceedings of the 10th ACM Conference on Web Science

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As one of the richest sources of encyclopedic information on the Web, Wikipedia generates an enormous amount of traffic. In this paper, we study large-scale article access data of the English Wikipedia in order to compare articles with respect to the two main paradigms of information seeking, i.e., search by formulating a query, and navigation by following hyperlinks. To this end, we propose and employ two main metrics, namely (i) searchsharethe relative amount of views an article received by search -, and (ii) resistance -the ability of an article to relay traffic to other Wikipedia articles -to characterize articles. We demonstrate how articles in distinct topical categories differ substantially in terms of these properties. For example, architecture-related articles are often accessed through search and are simultaneously a "dead end" for traffic, whereas historical articles about military events are mainly navigated. We further link traffic differences to varying network, content, and editing activity features. Lastly, we measure the impact of the article properties by modeling access behavior on articles with a gradient boosting approach. The results of this paper constitute a step towards understanding human information seeking behavior on the Web.

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Section: Discussionsupporting

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Query for Architecture, Click through Military

Dimitrov

Lemmerich

Flöck

et al. 2018

Proceedings of the 10th ACM Conference on Web Science

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“…Lamprecht et al investigate how different naïve link selection models compare to data from the Wikipedia clickstream, finding that a model based on article structure best explains user navigation choices [23]. This work is built on by Dimitrov et al, by using a more complex model utilizing Bayesian inference, supplemented by mixed effects hurdle models using network, semantic and visual features to predict transition counts in clickstream data [24]. Finally, 'Why We Read Wikipedia' [25] provides a comprehensive overview of navigation on Wikipedia to create a taxonomy of users, their behaviours and their motivations by matching survey responses with data including user clickstreams.…”

Section: Introductionmentioning

confidence: 99%

Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation

Gildersleve

Yasseri

2018

Complex Networks IX

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The World Wide Web (WWW) has fundamentally changed the ways billions of people are able to access information. Thus, understanding how people seek information online is an important issue of study. Wikipedia is a hugely important part of information provision on the web, with hundreds of millions of users browsing and contributing to its network of knowledge. The study of navigational behaviour on Wikipedia, due to the site's popularity and breadth of content, can reveal more general information seeking patterns that may be applied beyond Wikipedia and the Web. Our work addresses the relative shortcomings of existing literature in relating how information structure influences patterns of navigation online. We study aggregated clickstream data for articles on the English Wikipedia in the form of a weighted, directed navigational network. We introduce two parameters that describe how articles act to source and spread traffic through the network, based on their in/out strength and entropy. From these, we construct a navigational phase space where different article types occupy different, distinct regions, indicating how the structure of information online has differential effects on patterns of navigation. Finally, we go on to suggest applications for this analysis in identifying and correcting deficiencies in the Wikipedia page network that may also be adapted to more general information networks.

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“…In particular, I study the relationship between the link properties and the link popularity as measured by transitional click data. To that end, I formulate navigational hypotheses based on different link features, i.e., network features, semantic features and visual features [2,3]. The plausibility of these hypotheses is then tested using a Markov chain-based Bayesian hypothesis testing framework.…”

mentioning

confidence: 99%

Modeling Navigation in Information Networks

Dimitrov

2017

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

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Navigation in an information space is a natural way to explore and discover its content. Information systems on the Web like digital encyclopedias (e.g., Wikipedia) are interested in providing good navigational support to their users. To that end, navigation models can be useful for estimating the general navigability of an information space and for understanding how users interact with it. Such models can also be applied to identify problems faced by the users during navigation and to improve user interfaces.Studying navigation on the Web is a challenging task that has a long tradition in our scientific community. Based on large studies, researchers have made significant steps towards understanding navigational user behavior on the Web identifying general usage patterns, regularities, and strategies users apply during navigation. The seminal information foraging theory has been developed suggesting that people follow links by constantly estimating their quality in terms of information value and cost associated with obtaining that value by interacting with the environment. Furthermore, models describing the network structure of the Web like the bow tie model, and the small world models have been introduced. These models contributed valuable insights towards characterizing the underlying network topology on which the users operate and the extent to which it allows efficient navigation. In the context of information networks, researchers have successfully modeled user navigation resorting to Markov chains and to decentralized search. With respect to the users' navigational behavior and their click activities to traverse a link, researchers have found a valuable source of information in the log files of Web servers. Click data has also been collected by letting humans play navigational games on Wikipedia. With this data, researchers tested different navigational hypotheses; for example, (i) if humans tend to navigate between semantically similar articles, (ii) if they experience a trade-off between following links leading towards semantically similar articles and following links leading towards possibly well-connected articles. For navigation with a particular target in mind, users are found to be greedy with respect to the next click if they are confident to be on the right path, whereas they tend to explore the information network at random if they feel insecure or lost and have no intuition about the next click.Although these research lines have advanced our understanding of navigational user behavior in information networks, for the goal of the proposed thesis-modeling navigation-related work does not address and cover the following questions: (i) What is the relationship between the user's awareness regarding the structure and the topology of the information network and the efficiency of navigation, i.e., modeled as decentralized search and (ii) How do users interact with the content to explore and discover it, i.e., are there some specific links that are especially appealing and what are their characteristics?...

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What Makes a Link Successful on Wikipedia?

Cited by 43 publications

References 52 publications

Query for Architecture, Click through Military

Query for Architecture, Click through Military

Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation

Modeling Navigation in Information Networks

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