Perceived Performance of Webpages In the Wild: Insights from Large-scale Crowdsourcing of Above-the-Fold QoE
Qingzhu Gao, Prasenjit Dey, and Parvez Ahammad

TL;DR
This study introduces 'SpeedPerception', a large-scale crowdsourcing framework that analyzes how humans perceive webpage loading speed, revealing limitations of traditional metrics and proposing a machine learning model for better prediction of perceived performance.
Contribution
The paper presents a novel crowdsourcing framework and dataset for understanding perceived webpage speed, and develops a machine learning model that better predicts human judgments than traditional metrics.
Findings
Traditional metrics like onLoad and TTFB poorly match human perception.
A simple 3-variable machine learning model achieves 87% accuracy in predicting perceived speed.
End-users evaluate perceived webpage speed faster than visualComplete event timing.
Abstract
Clearly, no one likes webpages with poor quality of experience (QoE). Being perceived as slow or fast is a key element in the overall perceived QoE of web applications. While extensive effort has been put into optimizing web applications (both in industry and academia), not a lot of work exists in characterizing what aspects of webpage loading process truly influence human end-user's perception of the "Speed" of a page. In this paper we present "SpeedPerception", a large-scale web performance crowdsourcing framework focused on understanding the perceived loading performance of above-the-fold (ATF) webpage content. Our end goal is to create free open-source benchmarking datasets to advance the systematic analysis of how humans perceive webpage loading process. In Phase-1 of our "SpeedPerception" study using Internet Retailer Top 500 (IR 500) websites…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Open Source Software Innovations · Digital Marketing and Social Media
