A large scale study of reader interactions with images on Wikipedia

Author:

Rama DanieleORCID,Piccardi TizianoORCID,Redi MiriamORCID,Schifanella RossanoORCID

Abstract

AbstractWikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article’s body. However, despite their widespread usage on web platforms and the huge volume of visual content on Wikipedia, little is known about the importance of images in the context of free knowledge environments. To bridge this gap, we collect data about English Wikipedia reader interactions with images during one month and perform the first large-scale analysis of how interactions with images happen on Wikipedia. First, we quantify the overall engagement with images, finding that one in 29 pageviews results in a click on at least one image, one order of magnitude higher than interactions with other types of article content. Second, we study what factors associate with image engagement and observe that clicks on images occur more often in shorter articles and articles about visual arts or transports and biographies of less well-known people. Third, we look at interactions with Wikipedia article previews and find that images help support reader information need when navigating through the site, especially for more popular pages. The findings in this study deepen our understanding of the role of images for free knowledge and provide a guide for Wikipedia editors and web user communities to enrich the world’s largest source of encyclopedic knowledge.

Funder

compagnia di san paolo

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computer Science Applications,Modeling and Simulation

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18

2. How academic institutions can help to close Wikipedia’s gender gap;Nature;2022-05-23

3. Going Down the Rabbit Hole: Characterizing the Long Tail of Wikipedia Reading Sessions;Companion Proceedings of the Web Conference 2022;2022-04-25

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