Wikimedia Research/Showcase/Archive/2020/08
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.
- 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.
- By Taha Yasseri (University College Dublin), Patrick Gildersleve (Oxford Internet Institute)
- [1] Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data. PLoS One (2013). https://doi.org/10.1371/journal.pone.0071226
- [2] Wikipedia traffic data and electoral prediction: towards theoretically informed models. EPJ Data Science (2016). https://doi.org/10.1140/epjds/s13688-016-0083-3
- [3] Dynamics and biases of online attention: the case of aircraft crashes. Royal Society Open Science (2016). https://doi.org/10.1098/rsos.160460
- [4] The memory remains: Understanding collective memory in the digital age. Science Advances (2018). https://doi.org/10.1126/sciadv.1602368
- [5] Inspiration, captivation, and misdirection: Emergent properties in networks of online navigation. Springer (2018). https://ora.ox.ac.uk/objects/uuid:73baed3c-d3fe-4200-8e90-2d80b11f21cf
- additional slides from 2nd part of the talk Navigation, Networks, News, Wikipedia (P. Gildersleve)
- 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.
- By Dimitar Dimitrov (GESIS - Leibniz Institute for the Social Sciences)
- Different Topic, Different Traffic: How Search and Navigation Interplay on Wikipedia. Journal of Web Science (2019). https://doi.org/10.34962/jws-71