Wikimedia Research/Showcase/Archive/2020/08

From mediawiki.org

August 2020[edit]

Theme
Readership and navigation

August 19, 2020 Video: YouTube

What matters to us most and why? Studying popularity and attention dynamics via Wikipedia navigation data.
slides
By Taha Yasseri (University College Dublin), Patrick Gildersleve (Oxford Internet Institute)
While Wikipedia research was initially focused on editorial behaviour or the content to a great extent, soon researchers realized the value of the navigation data both as a reflection of readers interest and, more generally, as a proxy for behaviour of online information seekers. In this talk we will report on various projects in which we utilized pageview statistics or readers navigation data to study: movies financial success [1], electoral popularity [2], disaster triggered collective attention [3] and collective memory [4], general navigation patterns and article typology [5], and attention patterns in relation to news breakouts.


Query for Architecture, Click through Military. Comparing the Roles of Search and Navigation on Wikipedia
slides
By Dimitar Dimitrov (GESIS - Leibniz Institute for the Social Sciences)
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) searchshare -- the 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.