The next Research Showcase will be live-streamed on Wednesday, August 19,
at 9:30 AM PDT/16:30 UTC, and will be on the theme of readership and
YouTube stream: https://www.youtube.com/watch?v=MeUl0zjHdF8
As usual, you can join the conversation on IRC at #wikimedia-research. You
can also watch our past research showcases here:
This month's presentations:
What matters to us most and why? Studying popularity and attention dynamics
via Wikipedia navigation data.
By Taha Yasseri (University College Dublin), Patrick Gildersleve (Oxford
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
, electoral popularity , disaster triggered collective attention 
and collective memory , general navigation patterns and article typology
, and attention patterns in relation to news breakouts.
 Early Prediction of Movie Box Office Success Based on Wikipedia
Activity Big Data. PLoS One (2013).
 Wikipedia traffic data and electoral prediction: towards
theoretically informed models. EPJ Data Science (2016).
 Dynamics and biases of online attention: the case of aircraft
crashes. Royal Society Open Science (2016).
 The memory remains: Understanding collective memory in the digital
age. Science Advances (2018). https://doi.org/10.1126/sciadv.1602368
 Inspiration, captivation, and misdirection: Emergent properties in
networks of online navigation. Springer (2018).
Query for Architecture, Click through Military. Comparing the Roles of
Search and Navigation on Wikipedia
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.
Different Topic, Different Traffic: How Search and Navigation Interplay
on Wikipedia. Journal of Web Science (2019).
Janna Layton (she/her)
Administrative Associate - Product & Technology
Wikimedia Foundation <https://wikimediafoundation.org/>