Toward harmonizing self-reported and logged social data for understanding human behavior

Vivek K. Singh; Arushi Jain

doi:10.1145/3025453.3025856

Toward harmonizing self-reported and logged social data for understanding human behavior

Vivek K. Singh, Arushi Jain

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

Original language	English
Title of host publication	CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publication	Explore, Innovate, Inspire
Publisher	Association for Computing Machinery
Pages	2233-2238
Number of pages	6
ISBN (Electronic)	9781450346559
DOIs	https://doi.org/10.1145/3025453.3025856
State	Published - May 2 2017
Externally published	Yes
Event	2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 - Denver, United States Duration: May 6 2017 → May 11 2017

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings
Volume	2017-May

Conference

Conference	2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017
Country/Territory	United States
City	Denver
Period	5/6/17 → 5/11/17

ASJC Scopus Subject Areas

Software
Human-Computer Interaction
Computer Graphics and Computer-Aided Design

Keywords

Bias
Call-log data
Dissonance coefficient
Self-reported
Social ties
Socio-mobile behavior

Access to Document

10.1145/3025453.3025856

Cite this

Singh, V. K., & Jain, A. (2017). Toward harmonizing self-reported and logged social data for understanding human behavior. In CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire (pp. 2233-2238). (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2017-May). Association for Computing Machinery. https://doi.org/10.1145/3025453.3025856

Toward harmonizing self-reported and logged social data for understanding human behavior. / Singh, Vivek K.; Jain, Arushi.
CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Association for Computing Machinery, 2017. p. 2233-2238 (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2017-May).

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

Singh, VK & Jain, A 2017, Toward harmonizing self-reported and logged social data for understanding human behavior. in CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Conference on Human Factors in Computing Systems - Proceedings, vol. 2017-May, Association for Computing Machinery, pp. 2233-2238, 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017, Denver, United States, 5/6/17. https://doi.org/10.1145/3025453.3025856

Singh VK, Jain A. Toward harmonizing self-reported and logged social data for understanding human behavior. In CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Association for Computing Machinery. 2017. p. 2233-2238. (Conference on Human Factors in Computing Systems - Proceedings). doi: 10.1145/3025453.3025856

Singh, Vivek K. ; Jain, Arushi. / Toward harmonizing self-reported and logged social data for understanding human behavior. CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems: Explore, Innovate, Inspire. Association for Computing Machinery, 2017. pp. 2233-2238 (Conference on Human Factors in Computing Systems - Proceedings).

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Toward harmonizing self-reported and logged social data for understanding human behavior

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

Other files and links

Cite this