Abstract
Modern web users have access to a wide and diverse range of client platforms to browse the web. While it is anecdotally believed that the same URL may result in a different web page across different client platforms, the extent to which this occurs is not known. In this work, we systematically study the impact of different client platforms (browsers, operating systems, devices, and vantage points) on the content of base HTML pages. We collect and analyze the base HTML page downloaded for 3876 web pages composed of the top 250 web sites using 32 different client platforms for a period of 30 days — our dataset includes over 3.5 million web page downloads. We find that client platforms have a statistically significant influence on web page downloads in both expected and unexpected ways. We discuss the impact that these results will have in several application domains including web archiving, user experience, social interactions and information sharing, and web content sentiment analysis.
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Notes
- 1.
Please note that each of the User-agents we use in this study were obtained from deep packet inspection of web traffic as generated using known client platforms.
- 2.
Please refer to [3] for a complete list of these features.
- 3.
Please note that the significant differences discussed here are primarily true for browser version analysis for Opera and Firefox. This is because we have the largest range in release dates for these two browsers.
- 4.
Please note that while we study the top 250 web sites in the world, many of these sites are served by content providers that are in the U.S.
- 5.
We discuss results pertaining to bing.com because other search engines such as google.com are blocked in some countries.
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Acknowledgements
This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1144081 as well as by NSF under Grant CNS-1526268.
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Sanders, S., Sanka, G., Aikat, J., Kaur, J. (2015). The Influence of Client Platform on Web Page Content: Measurements, Analysis, and Implications. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_1
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