Assessing candidate preference through web browsing history

Giovanni Comarela; Ramakrishnan Durairajan; Paul Barford; Dino Christenson; Mark Crovella

doi:10.1145/3219819.3219884

Assessing candidate preference through web browsing history

Giovanni Comarela
, Ramakrishnan Durairajan
, Paul Barford
, Dino Christenson
, Mark Crovella

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference-including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters' candidate preferences in ways that are difficult to achieve using polls.

Original language	English
Title of host publication	KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	158-167
Number of pages	10
ISBN (Print)	9781450355520
DOIs	https://doi.org/10.1145/3219819.3219884
State	Published - Jul 19 2018
Event	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom Duration: Aug 19 2018 → Aug 23 2018

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
Country/Territory	United Kingdom
City	London
Period	08/19/18 → 08/23/18

Keywords

Browsing behavior
Candidate preference
Machine learning

Access to Document

10.1145/3219819.3219884

Cite this

Comarela, G., Durairajan, R., Barford, P., Christenson, D., & Crovella, M. (2018). Assessing candidate preference through web browsing history. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 158-167). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3219819.3219884

Comarela, Giovanni ; Durairajan, Ramakrishnan ; Barford, Paul et al. / Assessing candidate preference through web browsing history. KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. pp. 158-167 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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title = "Assessing candidate preference through web browsing history",

abstract = "Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference-including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters' candidate preferences in ways that are difficult to achieve using polls.",

keywords = "Browsing behavior, Candidate preference, Machine learning",

author = "Giovanni Comarela and Ramakrishnan Durairajan and Paul Barford and Dino Christenson and Mark Crovella",

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series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

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Comarela, G, Durairajan, R, Barford, P, Christenson, D & Crovella, M 2018, Assessing candidate preference through web browsing history. in KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 158-167, 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 08/19/18. https://doi.org/10.1145/3219819.3219884

Assessing candidate preference through web browsing history. / Comarela, Giovanni; Durairajan, Ramakrishnan; Barford, Paul et al.
KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. p. 158-167 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Durairajan, Ramakrishnan

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AU - Christenson, Dino

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Y1 - 2018/7/19

N2 - Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference-including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters' candidate preferences in ways that are difficult to achieve using polls.

AB - Predicting election outcomes is of considerable interest to candidates, political scientists, and the public at large. We propose the use of Web browsing history as a new indicator of candidate preference among the electorate, one that has potential to overcome a number of the drawbacks of election polls. However, there are a number of challenges that must be overcome to effectively use Web browsing for assessing candidate preference-including the lack of suitable ground truth data and the heterogeneity of user populations in time and space. We address these challenges, and show that the resulting methods can shed considerable light on the dynamics of voters' candidate preferences in ways that are difficult to achieve using polls.

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KW - Candidate preference

KW - Machine learning

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U2 - 10.1145/3219819.3219884

DO - 10.1145/3219819.3219884

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T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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BT - KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Comarela G, Durairajan R, Barford P, Christenson D, Crovella M. Assessing candidate preference through web browsing history. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2018. p. 158-167. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3219819.3219884

Assessing candidate preference through web browsing history

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Keywords

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