research-article

Modeling the Influence of Popular Trending Events on User Search Behavior

Authors:

Shubhra Kanti Karmaker Santu,

Chengxiang ZhaiAuthors Info & Claims

WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

Pages 535 - 544

https://doi.org/10.1145/3041021.3054188

Published: 03 April 2017 Publication History

Abstract

Understanding how users' search behavior is influenced by real world events is important both for social science research and for designing better search engines for users. In this paper, we study how to model the influence of events on user queries by framing it as a novel data mining problem. Specifically, given a text description of an event, we mine the search log data to identify queries that are triggered by it and further characterize the temporal trend of influence created by the same event on user queries. We solve this data mining problem by proposing computational measures that quantify the influence of an event on a query to identify triggered queries and then, proposing a novel extension of Hawkes process to model the evolutionary trend of the influence of an event on search queries. Evaluation results using news articles and search log data show that the proposed approach is effective for identification of queries triggered by events reported in news articles and characterization of the influence trend over time, opening up many interesting opportunities of applications such as comparative analysis of influential events and prediction of event-triggered queries by users.

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Cited By

Li KYu ZZhang YXi JGuo B(2024)Predictive Model of Cultural Product Search Interest Based on Collective Memory Hawkes Process2024 11th International Conference on Behavioural and Social Computing (BESC)10.1109/BESC64747.2024.10780717(1-8)Online publication date: 16-Aug-2024
https://doi.org/10.1109/BESC64747.2024.10780717
Li KYu ZZhang YGuo B(2023)Impact of COVID-19 Pandemic on Cultural Products InterestsCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587594(1190-1195)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587594
Wu WLiu HZhang XLiu YZha H(2023)Modeling Event Propagation via Graph Biased Temporal Point ProcessIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.300462634:4(1681-1691)Online publication date: Apr-2023
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Index Terms

Modeling the Influence of Popular Trending Events on User Search Behavior
1. Information systems

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WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web Companion

April 2017

1738 pages

ISBN:9781450349147

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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Research-article

Funding Sources

Yahoo Research
Yahoo Faculty Research and Engagement Program award

Conference

WWW '17

Sponsor:

IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Companion Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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https://doi.org/10.1109/BESC64747.2024.10780717
Li KYu ZZhang YGuo B(2023)Impact of COVID-19 Pandemic on Cultural Products InterestsCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587594(1190-1195)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587594
Wu WLiu HZhang XLiu YZha H(2023)Modeling Event Propagation via Graph Biased Temporal Point ProcessIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.300462634:4(1681-1691)Online publication date: Apr-2023
https://doi.org/10.1109/TNNLS.2020.3004626
Murayama TMatsubara YSakurai YAl Hasan MXiong L(2022)Mining Reaction and Diffusion Dynamics in Social ActivitiesProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557396(1521-1531)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557396
Carmel DChang YDeng HNie J(2020)Future Directions of Query UnderstandingQuery Understanding for Search Engines10.1007/978-3-030-58334-7_9(205-224)Online publication date: 2-Dec-2020
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Karmaker Santu SLi LChang YZhai CCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)JIMProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271681(637-646)Online publication date: 17-Oct-2018
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Mansouri BZahedi MCampos RFarhoodi MRahgozar MChampin PGandon FMédini LLalmas MIpeirotis P(2018)Understanding User's Search Behavior towards Spiky EventsCompanion Proceedings of the The Web Conference 201810.1145/3184558.3191638(1763-1769)Online publication date: 23-Apr-2018
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Nguyen TKanhabua NNejdl W(2018)Multiple Models for Recommending Temporal Aspects of EntitiesThe Semantic Web10.1007/978-3-319-93417-4_30(462-480)Online publication date: 3-Jun-2018
https://doi.org/10.1007/978-3-319-93417-4_30

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