research-article

The Information Network: Exploiting Causal Dependencies in Online Information Seeking

Authors:

Prasanta Bhattacharya,

Rishabh MehrotraAuthors Info & Claims

CHIIR '16: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval

Pages 223 - 232

https://doi.org/10.1145/2854946.2854974

Published: 13 March 2016 Publication History

Abstract

The Internet has emerged as a leading source of information about the world and its daily occurrences. Platforms like Wikipedia act as information conduits through which informational elements (e.g. topic pages) cater to the information seeking needs of users worldwide. While usage data from these informational elements help us to predict the information seeking behavior of users, especially in reaction to external news events, what has been largely ignored in past literature is the predictive value of the underlying informational network that connects these elements. In this study, we uncover causal linkages in information seeking behavior among related informational elements on Wikipedia. We demonstrate that incorporating this causal information leads to better predictions of page view counts of relevant Wikipedia pages, when compared to models that ignore such underlying causal linkages. We also provide additional evidence about the efficacy of our approach from the real world, by performing a judgment study with human annotators. This research is among the first to investigate and uncover the value of understanding the underlying relationships among informational elements.

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Cited By

Arabzadeh NFani HZarrinkalam FNavivala ABagheri ECuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Causal Dependencies for Future Interest Prediction on TwitterProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3269312(1511-1514)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3269312

Index Terms

The Information Network: Exploiting Causal Dependencies in Online Information Seeking
1. Information systems
  1. World Wide Web
    1. Web mining

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cover image ACM Conferences

CHIIR '16: Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval

March 2016

400 pages

ISBN:9781450337519

DOI:10.1145/2854946

General Chairs:
Diane Kelly
University of North Carolina at Chapel Hill, USA
,
Rob Capra
University of North Carolina at Chapel Hill, USA
,
Program Chairs:
Nick Belkin
Rutgers University, USA
,
Jaime Teevan
Microsoft Research, USA
,
Pertti Vakkari
University of Tampere, Finland

Copyright © 2016 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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 March 2016

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CHIIR '16

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SIGIR

CHIIR '16: Conference on Human Information Interaction and Retrieval

March 13 - 17, 2016

North Carolina, Carrboro, USA

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CHIIR '16 Paper Acceptance Rate 23 of 58 submissions, 40%;

Overall Acceptance Rate 55 of 163 submissions, 34%

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Cited By

Arabzadeh NFani HZarrinkalam FNavivala ABagheri ECuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Causal Dependencies for Future Interest Prediction on TwitterProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3269312(1511-1514)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3269312

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