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The good, the bad, and the random: an eye-tracking study of ad quality in web search

Authors:

Susan T. Dumais,

Edward CutrellAuthors Info & Claims

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

Pages 42 - 49

https://doi.org/10.1145/1835449.1835459

Published: 19 July 2010 Publication History

Abstract

We investigate how people interact with Web search engine result pages using eye-tracking. While previous research has focused on the visual attention devoted to the 10 organic search results, this paper examines other components of contemporary search engines, such as ads and related searches. We systematically varied the type of task (informational or navigational), the quality of the ads (relevant or irrelevant to the query), and the sequence in which ads of different quality were presented. We measured the effects of these variables on the distribution of visual attention and on task performance. Our results show significant effects of each variable. The amount of visual attention that people devote to organic results depends on both task type and ad quality. The amount of visual attention that people devote to ads depends on their quality, but not the type of task. Interestingly, the sequence and predictability of ad quality is also an important factor in determining how much people attend to ads. When the quality of ads varied randomly from task to task, people paid little attention to the ads, even when they were good. These results further our understanding of how attention devoted to search results is influenced by other page elements, and how previous search experiences influence how people attend to the current page.

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Cited By

Castilla DDel Tejo Catalá OPons PSignol FRey BSuso-Ribera CPerez-Cortes J(2024)Improving the understanding of web user behaviors through machine learning analysis of eye-tracking dataUser Modeling and User-Adapted Interaction10.1007/s11257-023-09373-y34:2(293-322)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11257-023-09373-y
Tseng YLin YYadav GBier NAleven V(2024)Curio: Enhancing STEM Online Video Learning Experience Through Integrated, Just-in-Time Help-SeekingTechnology Enhanced Learning for Inclusive and Equitable Quality Education10.1007/978-3-031-72315-5_30(437-451)Online publication date: 13-Sep-2024
https://doi.org/10.1007/978-3-031-72315-5_30
Azzopardi LMaxwell DHalvey MHauff C(2023)Driven to Distraction: Examining the Influence of Distractors on Search Behaviours, Performance and ExperienceProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578298(83-94)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578298
Show More Cited By

Index Terms

The good, the bad, and the random: an eye-tracking study of ad quality in web search
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Conferences

SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

July 2010

944 pages

ISBN:9781450301534

DOI:10.1145/1835449

General Chairs:
Fabio Crestani
University of Lugano, CH
,
Stéphane Marchand-Maillet
University of Geneva, CH
,
Program Chairs:
Hsin-Hsi Chen
National Taiwan University, TW
,
Efthimis N. Efthimiadis
University of Washington, USA
,
Jacques Savoy
University of Neuchatel, CH

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

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SIGIR '10

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SIGIR

SIGIR '10: The 33rd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2010

Geneva, Switzerland

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SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Castilla DDel Tejo Catalá OPons PSignol FRey BSuso-Ribera CPerez-Cortes J(2024)Improving the understanding of web user behaviors through machine learning analysis of eye-tracking dataUser Modeling and User-Adapted Interaction10.1007/s11257-023-09373-y34:2(293-322)Online publication date: 1-Apr-2024
Tseng YLin YYadav GBier NAleven V(2024)Curio: Enhancing STEM Online Video Learning Experience Through Integrated, Just-in-Time Help-SeekingTechnology Enhanced Learning for Inclusive and Equitable Quality Education10.1007/978-3-031-72315-5_30(437-451)Online publication date: 13-Sep-2024
Azzopardi LMaxwell DHalvey MHauff C(2023)Driven to Distraction: Examining the Influence of Distractors on Search Behaviours, Performance and ExperienceProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578298(83-94)Online publication date: 19-Mar-2023
Hashavit AWang HStern TKraus SChen HDuh WHuang HKato MMothe JPoblete B(2023)Not Just Skipping: Understanding the Effect of Sponsored Content on Users' Decision-Making in Online Health SearchProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591744(1056-1065)Online publication date: 19-Jul-2023
Sahni NZhang C(2023)Are consumers averse to sponsored messages? The role of search advertising in information discoveryQuantitative Marketing and Economics10.1007/s11129-023-09270-z22:1(63-114)Online publication date: 20-Nov-2023
Chen MLiu CLiu ZSun JZhang ARangwala H(2022)Scalar is Not EnoughProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539468(136-145)Online publication date: 14-Aug-2022
Chen JZhang LQian W(2022)Cognitive differences between readers attentive and inattentive to task-related information: an eye-tracking studyAslib Journal of Information Management10.1108/AJIM-01-2022-000775:5(917-939)Online publication date: 4-Oct-2022
Li XFrancois JKwon J(2022)Investigating Consumers’ Online Restaurant Selection Behaviors Using Eye-tracking Technology and Retrospective Think-aloud InterviewsInternational Journal of Hospitality & Tourism Administration10.1080/15256480.2022.205569024:5(720-752)Online publication date: 27-Mar-2022
Lalmas MO’Brien HYom-Tov E(2022)Measuring User EngagementundefinedOnline publication date: 10-Mar-2022
Gómez-Carmona DCruces-Montes SMarín-Dueñas PSerrano-Domínguez CParamio AGarcía A(2021)Do You See It Clearly? The Effect of Packaging and Label Format on Google AdsJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1605009316:5(1648-1666)Online publication date: 20-May-2021
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