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[1903.10670] Detecting and Gauging Impact on Wikipedia Page Views
paperAuthors
Xiaoxi Chelsy Xie·Isaac Johnson·Anne Gomez
Credibility Rating
3/5
Good(3)Good quality. Reputable source with community review or editorial standards, but less rigorous than peer-reviewed venues.
Rating inherited from publication venue: arXiv
This paper is tangentially related to AI safety; it analyzes Wikipedia traffic patterns and may be relevant to understanding information ecosystems or measuring the societal impact of AI-generated content or AI-related news events.
Paper Details
Citations
7
1 influential
Year
2019
Methodology
survey
Metadata
Importance: 12/100arxiv preprintprimary source
Summary
This paper presents methods for detecting and measuring the impact of external events on Wikipedia page view traffic, identifying when and how real-world events cause spikes or shifts in article viewership. It develops statistical techniques to distinguish genuine impact signals from normal traffic variation. The work has applications in understanding information-seeking behavior and the propagation of public attention.
Key Points
- •Develops statistical methods to detect anomalous spikes in Wikipedia page view data caused by real-world events.
- •Proposes metrics to quantify the magnitude and duration of impact events on article traffic.
- •Analyzes patterns in how news events, disasters, or cultural moments drive Wikipedia readership.
- •Provides a framework applicable to studying information diffusion and public attention dynamics at scale.
- •Uses large-scale Wikipedia traffic logs as a proxy for collective human information-seeking behavior.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Wikipedia Views | Project | 38.0 |
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[1903.10670] Detecting and Gauging Impact on Wikipedia Page Views
Detecting and Gauging Impact on Wikipedia Page Views
Xiaoxi Chelsy Xie
cxie@wikimedia.org
Wikimedia Foundation
,
Isaac Johnson
isaac@wikimedia.org
Wikimedia Foundation
and
Anne Gomez
agomez@wikimedia.org
Wikimedia Foundation
(2019)
Abstract.
Understanding how various external campaigns or events affect readership on Wikipedia is important to efforts aimed at improving awareness and access to its content. In this paper, we consider how to build time-series models aimed at predicting page views on Wikipedia with the goal of detecting whether there are significant changes to the existing trends. We test these models on two different events: a video campaign aimed at increasing awareness of Hindi Wikipedia in India and the page preview feature roll-out—a means of accessing Wikipedia content without actually visiting the pages—on English and German Wikipedia. Our models effectively estimate the impact of page preview roll-out, but do not detect a significant change following the video campaign in India. We also discuss the utility of other geographies or language editions for predicting page views from a given area on a given language edition.
wikipedia; bayesian structural time series; page views; causal inference
† † journalyear: 2019 † † copyright: iw3c2w3 † † conference: Companion Proceedings of the 2019 World Wide Web Conference; May 13–17, 2019; San Francisco, CA, USA † † booktitle: Companion Proceedings of the 2019 World Wide Web Conference (WWW ’19 Companion), May 13–17, 2019, San Francisco, CA, USA † † doi: 10.1145/3308560.3316751 † † isbn: 978-1-4503-6675-5/19/05 † † ccs: Information systems Web mining † † ccs: Human-centered computing Empirical studies in collaborative and social computing
1. Introduction
Wikipedia is the fifth-most-visited website worldwide (Alexa, 2018 ) at 190 billion page views in 2018 alone (Erhart, 2019 ) and is turned to by readers for all sorts of reasons ranging from simple curiosity to fact-checking to making a personal decision (Singer et al . , 2017 ) . Despite its success, there are still many regions in the world where it is relatively unknown (Gill and McCune, 2018 ) or access is blocked (Clark
et al . , 2017 ) . In an attempt to improve access and awareness worldwide, the Wikimedia Foundation (WMF) has conducted various campaigns and efforts aimed at improving access to Wikipedia in various regions. 1 1 1 https://meta.wikimedia.org/wiki/New_Readers
Many researchers have sought to estimate the impact of external events on Wikipedia page views. This has included the effect of posting Wikipedia articles on external websites (Vincent
et al . , 2018 ; Moyer et al . , 2015 ) , privacy concerns on viewing of sensitive Wikipedia articles (Penney, 2016 ) , and censorship (Zhang and Zhu, 2011 ) . Conversely, much research has also s
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