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

@article{Gildersleve2017InspirationCA,
  title={Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation},
  author={Patrick Gildersleve and Taha Yasseri},
  journal={ArXiv},
  year={2017},
  volume={abs/1710.03326}
}
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… 

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References

SHOWING 1-10 OF 28 REFERENCES

How the structure of Wikipedia articles influences user navigation

The results highlight the importance of article structure and link position in Wikipedia navigation and suggest that better organization of information can help make information networks more navigable.

What Makes a Link Successful on Wikipedia?

The well-known classic PageRank algorithm is adapted and improved that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation to facilitate understanding navigational click behavior.

Mining Missing Hyperlinks from Human Navigation Traces: A Case Study of Wikipedia

This work proposes a novel approach to identifying missing links in Wikipedia by leveraging data sets of navigation paths collected through a Wikipedia-based human-computation game in which users must find a short path from a start to a target article by only clicking links encountered along the way.

How Web 1.0 fails: the mismatch between hyperlinks and clickstreams

It is shown that the mismatch between hyperlinks and clickstreams is indeed substantial and that this mismatch has arisen because webmasters attempt to build a global virtual world without geographical or cultural boundaries, but users in fact prefer to navigate within more fragmented, language-based groups of Web sites.

Why We Read Wikipedia

These findings advance the understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.

Agents, bookmarks and clicks: a topical model of web navigation

The resulting model reproduces individual behaviors from empirical data, reconciling the narrowly focused browsing patterns of individual users with the extreme heterogeneity of aggregate traffic measurements, and leading the way to more sophisticated, realistic, and effective ranking and crawling algorithms.

Predicting links in ego-networks using temporal information

This work defines several features to capture different kinds of temporal information and applies machine learning methods to combine these various features and improve the quality of the prediction of links among egos’ neighbors.

Characterizing user behavior in online social networks

A first of a kind analysis of user workloads in online social networks, based on detailed clickstream data collected over a 12-day period, shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities.

Off the beaten tracks: exploring three aspects of web navigation

This paper presents results of a long-term client-side Web usage study, updating previous studies that range in age from five to ten years, and confirms links to be the most important navigation element, while backtracking has lost more than half of its previously reported share and form submission has become far more common.

Improving Website Hyperlink Structure Using Server Logs

This work develops an approach for automatically finding useful hyperlinks to add to a website based exclusively on standard server logs, and defines the problem of link placement under budget constraints and proposes an efficient algorithm for solving it.