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Identifier 000428262
Title A measurement study of the online header bidding ad-ecosystem
Alternative Title Ανάλυση μετρήσεων του διαδικτυακού οικοσυστήματος διαφημίσεων πονταρίσματος κεφαλίδας
Author Παχυλάκης, Μιχαήλ Γ.
Thesis advisor Μαρκάτος, Ευάγγελος
Reviewer Ιωαννίδης, Σωτήρης
Δημητρόπουλος, Ξενοφώντας
Abstract In recent years, Header Bidding has gained popularity among web publishers, challenging the status quo in the ad ecosystem. Contrary to the traditional waterfall standard, Header Bidding aims to give back to publishers control of their ad inventory, increase transparency, fairness and competition among advertisers, resulting in higher ad-slot prices. Although promising, little is known about how this ad protocol works: What are Header Bidding's possible implementations, who are the major players, and what is its network and UX overhead? In this thesis, we present HBDetector, a novel methodology to detect Header Bidding auctions on a website in real time based on different signals. Those signals include the DOM events that are being triggered in a webpage by the Header Bidding libraries and web requests that are being sent to the advertising partners. Using HBDetector we crawled the 35,000 top Alexa websites, where we managed to collect and analyze a dataset of 800,000 auctions. Based on the data collected we were able to identify three different facets of Header Bidding currently used by the publishers. Furthermore, we find that: (i) 14.18% of the top websites utilize Header Bidding. (ii) Publishers prefer to collaborate with a few Demand Partners who also dominate the waterfall market. (iii) Header Bidding latency can be significantly higher (up to 3x in median cases) than waterfall. In this thesis, we present the design and implementation of HBDetector, and conduct the first in depth analysis of the Header Bidding advertising ecosystem. We provide a detailed analysis on how this new advertising standard works, how it can be detected and we shed light on its mechanics on the web.
Language English
Subject Advertisements
Web
Διαδίκτυο
Διαφημίσεις
Κεφαλίδα
Issue date 2020-03-27
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/b/2/d/metadata-dlib-1581676220-233669-7190.tkl Bookmark and Share
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