TY - GEN
T1 - Modeling the influence of popular trending events on user search behavior
AU - Santu, Shubhra Kanti Karmaker
AU - Li, Liangda
AU - Park, Dae Hoon
AU - Chang, Yi
AU - Zhai, Cheng Xiang
N1 - Funding Information:
This work was done as part of an intern research project at Yahoo Research. This work is also supported in part by a Yahoo Faculty Research and Engagement Program award. We thank the five anonymous reviewers for their valuable feedback to help us improve the paper.
Publisher Copyright:
© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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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U2 - 10.1145/3041021.3054188
DO - 10.1145/3041021.3054188
M3 - Conference contribution
AN - SCOPUS:85058028933
T3 - 26th International World Wide Web Conference 2017, WWW 2017 Companion
SP - 535
EP - 544
BT - 26th International World Wide Web Conference 2017, WWW 2017 Companion
PB - International World Wide Web Conferences Steering Committee
T2 - 26th International World Wide Web Conference, WWW 2017 Companion
Y2 - 3 April 2017 through 7 April 2017
ER -