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Whither Social Networks for Web Search?

Authors:

Rakesh Agrawal,

Behzad Golshan,

Evangelos PapalexakisAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1661 - 1670

https://doi.org/10.1145/2783258.2788571

Published: 10 August 2015 Publication History

Abstract

Access to diverse perspectives nurtures an informed citizenry. Google and Bing have emerged as the duopoly that largely arbitrates which English language documents are seen by web searchers. A recent study shows that there is now a large overlap in the top organic search results produced by them. Thus, citizens may no longer be able to gain different perspectives by using different search engines.

We present the results of our empirical study that indicates that by mining Twitter data one can obtain search results that are quite distinct from those produced by Google and Bing. Additionally, our user study found that these results were quite informative. The gauntlet is now on search engines to test whether our findings hold in their infrastructure for different social networks and whether enabling diversity has sufficient business imperative for them.

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Cited By

Badache I(2020)2SRM: Learning social signals for predicting relevant search resultsWeb Intelligence10.3233/WEB-20042618:1(15-33)Online publication date: 9-Mar-2020
https://doi.org/10.3233/WEB-200426
Dritsa KSotiropoulos TSkarpetis HLouridas P(2020)Search Engine Similarity Analysis: A Combined Content and Rankings ApproachWeb Information Systems Engineering – WISE 202010.1007/978-3-030-62008-0_2(21-37)Online publication date: 21-Oct-2020
https://doi.org/10.1007/978-3-030-62008-0_2
Brusilovsky PSmyth BShapira B(2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_7
Show More Cited By

Index Terms

Whither Social Networks for Web Search?
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 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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Publication History

Published: 10 August 2015

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National Science Foundation

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KDD '15

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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KDD '25

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Cited By

Badache I(2020)2SRM: Learning social signals for predicting relevant search resultsWeb Intelligence10.3233/WEB-20042618:1(15-33)Online publication date: 9-Mar-2020
https://doi.org/10.3233/WEB-200426
Dritsa KSotiropoulos TSkarpetis HLouridas P(2020)Search Engine Similarity Analysis: A Combined Content and Rankings ApproachWeb Information Systems Engineering – WISE 202010.1007/978-3-030-62008-0_2(21-37)Online publication date: 21-Oct-2020
https://doi.org/10.1007/978-3-030-62008-0_2
Brusilovsky PSmyth BShapira B(2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
https://doi.org/10.1007/978-3-319-90092-6_7
Alonso OKandylas VTremblay SHofman JSen SFox PMcGuinness DPoirer LBoldi PKinder-Kurlanda K(2017)What's Happening and What HappenedProceedings of the 2017 ACM on Web Science Conference10.1145/3091478.3091484(191-200)Online publication date: 25-Jun-2017
https://dl.acm.org/doi/10.1145/3091478.3091484
Agrawal RGolshan BPapalexakis E(2017)Homogeneity in Web Search ResultsACM Transactions on Intelligent Systems and Technology10.1145/30577318:5(1-35)Online publication date: 12-Jul-2017
https://dl.acm.org/doi/10.1145/3057731
Yang LPapalexakis E(2017)Exploration of Social and Web Image Search Results Using Tensor Decomposition2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW.2017.239(1915-1920)Online publication date: Jul-2017
https://doi.org/10.1109/CVPRW.2017.239
Aldhaheri AJeongkyu Lee (2017)Event detection on large social media using temporal analysis2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC.2017.7868467(1-6)Online publication date: Jan-2017
https://doi.org/10.1109/CCWC.2017.7868467
Papalexakis EFaloutsos CSidiropoulos N(2016)Tensors for Data Mining and Data FusionACM Transactions on Intelligent Systems and Technology10.1145/29159218:2(1-44)Online publication date: 3-Oct-2016
https://dl.acm.org/doi/10.1145/2915921
Zhao FZhu YJin HYang L(2016)A personalized hashtag recommendation approach using LDA-based topic model in microblog environmentFuture Generation Computer Systems10.1016/j.future.2015.10.01265:C(196-206)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.future.2015.10.012
Agrawal RGolshan BPapalexakis ESharma AAgrawal RGrossglauser M(2015)Overlap Between Google and Bing Web Search Results!Proceedings of the 2015 ACM on Conference on Online Social Networks10.1145/2817946.2820604(95-95)Online publication date: 2-Nov-2015
https://dl.acm.org/doi/10.1145/2817946.2820604
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