CrowdMuse

Supporting crowd idea generation through user modeling and adaptation

Victor Girotto, Erin Walker, Winslow Burleson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Online crowds, with their large numbers and diversity, show great potential for creativity. Research has explored different ways of augmenting their creative performance, particularly during large-scale brainstorming sessions. Traditionally, this comes in the form of showing ideators some form of inspiration to get them to explore more categories or generate more and better ideas. The mechanisms used to select which inspirations are shown to ideators thus far have not taken into consideration ideators' individualities, which could hinder the effectiveness of support. In this paper, we introduce and evaluate CrowdMuse, a novel adaptive system for supporting large-scale brainstorming. The system models ideators based on their past ideas and adapts the system views and inspiration mechanism accordingly. We evaluate CrowdMuse over two iterative large online studies and discuss the implication of our findings for designing adaptive creativity support systems.

Original languageEnglish (US)
Title of host publicationC and C 2019 - Proceedings of the 2019 Creativity and Cognition
PublisherAssociation for Computing Machinery, Inc
Pages95-106
Number of pages12
ISBN (Electronic)9781450359177
DOIs
StatePublished - Jun 13 2019
Externally publishedYes
Event12th ACM Creativity and Cognition Conference, C and C 2019 - San Diego, United States
Duration: Jun 23 2019Jun 26 2019

Publication series

NameC and C 2019 - Proceedings of the 2019 Creativity and Cognition

Conference

Conference12th ACM Creativity and Cognition Conference, C and C 2019
CountryUnited States
CitySan Diego
Period6/23/196/26/19

Fingerprint

Adaptive systems

Keywords

  • Adaptive systems
  • Brainstorming
  • Creativity
  • Crowd

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

Cite this

Girotto, V., Walker, E., & Burleson, W. (2019). CrowdMuse: Supporting crowd idea generation through user modeling and adaptation. In C and C 2019 - Proceedings of the 2019 Creativity and Cognition (pp. 95-106). (C and C 2019 - Proceedings of the 2019 Creativity and Cognition). Association for Computing Machinery, Inc. https://doi.org/10.1145/3325480.3325497

CrowdMuse : Supporting crowd idea generation through user modeling and adaptation. / Girotto, Victor; Walker, Erin; Burleson, Winslow.

C and C 2019 - Proceedings of the 2019 Creativity and Cognition. Association for Computing Machinery, Inc, 2019. p. 95-106 (C and C 2019 - Proceedings of the 2019 Creativity and Cognition).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Girotto, V, Walker, E & Burleson, W 2019, CrowdMuse: Supporting crowd idea generation through user modeling and adaptation. in C and C 2019 - Proceedings of the 2019 Creativity and Cognition. C and C 2019 - Proceedings of the 2019 Creativity and Cognition, Association for Computing Machinery, Inc, pp. 95-106, 12th ACM Creativity and Cognition Conference, C and C 2019, San Diego, United States, 6/23/19. https://doi.org/10.1145/3325480.3325497
Girotto V, Walker E, Burleson W. CrowdMuse: Supporting crowd idea generation through user modeling and adaptation. In C and C 2019 - Proceedings of the 2019 Creativity and Cognition. Association for Computing Machinery, Inc. 2019. p. 95-106. (C and C 2019 - Proceedings of the 2019 Creativity and Cognition). https://doi.org/10.1145/3325480.3325497
Girotto, Victor ; Walker, Erin ; Burleson, Winslow. / CrowdMuse : Supporting crowd idea generation through user modeling and adaptation. C and C 2019 - Proceedings of the 2019 Creativity and Cognition. Association for Computing Machinery, Inc, 2019. pp. 95-106 (C and C 2019 - Proceedings of the 2019 Creativity and Cognition).
@inproceedings{0b280bf63ac94beab38c673cd56ed172,
title = "CrowdMuse: Supporting crowd idea generation through user modeling and adaptation",
abstract = "Online crowds, with their large numbers and diversity, show great potential for creativity. Research has explored different ways of augmenting their creative performance, particularly during large-scale brainstorming sessions. Traditionally, this comes in the form of showing ideators some form of inspiration to get them to explore more categories or generate more and better ideas. The mechanisms used to select which inspirations are shown to ideators thus far have not taken into consideration ideators' individualities, which could hinder the effectiveness of support. In this paper, we introduce and evaluate CrowdMuse, a novel adaptive system for supporting large-scale brainstorming. The system models ideators based on their past ideas and adapts the system views and inspiration mechanism accordingly. We evaluate CrowdMuse over two iterative large online studies and discuss the implication of our findings for designing adaptive creativity support systems.",
keywords = "Adaptive systems, Brainstorming, Creativity, Crowd",
author = "Victor Girotto and Erin Walker and Winslow Burleson",
year = "2019",
month = "6",
day = "13",
doi = "10.1145/3325480.3325497",
language = "English (US)",
series = "C and C 2019 - Proceedings of the 2019 Creativity and Cognition",
publisher = "Association for Computing Machinery, Inc",
pages = "95--106",
booktitle = "C and C 2019 - Proceedings of the 2019 Creativity and Cognition",

}

TY - GEN

T1 - CrowdMuse

T2 - Supporting crowd idea generation through user modeling and adaptation

AU - Girotto, Victor

AU - Walker, Erin

AU - Burleson, Winslow

PY - 2019/6/13

Y1 - 2019/6/13

N2 - Online crowds, with their large numbers and diversity, show great potential for creativity. Research has explored different ways of augmenting their creative performance, particularly during large-scale brainstorming sessions. Traditionally, this comes in the form of showing ideators some form of inspiration to get them to explore more categories or generate more and better ideas. The mechanisms used to select which inspirations are shown to ideators thus far have not taken into consideration ideators' individualities, which could hinder the effectiveness of support. In this paper, we introduce and evaluate CrowdMuse, a novel adaptive system for supporting large-scale brainstorming. The system models ideators based on their past ideas and adapts the system views and inspiration mechanism accordingly. We evaluate CrowdMuse over two iterative large online studies and discuss the implication of our findings for designing adaptive creativity support systems.

AB - Online crowds, with their large numbers and diversity, show great potential for creativity. Research has explored different ways of augmenting their creative performance, particularly during large-scale brainstorming sessions. Traditionally, this comes in the form of showing ideators some form of inspiration to get them to explore more categories or generate more and better ideas. The mechanisms used to select which inspirations are shown to ideators thus far have not taken into consideration ideators' individualities, which could hinder the effectiveness of support. In this paper, we introduce and evaluate CrowdMuse, a novel adaptive system for supporting large-scale brainstorming. The system models ideators based on their past ideas and adapts the system views and inspiration mechanism accordingly. We evaluate CrowdMuse over two iterative large online studies and discuss the implication of our findings for designing adaptive creativity support systems.

KW - Adaptive systems

KW - Brainstorming

KW - Creativity

KW - Crowd

UR - http://www.scopus.com/inward/record.url?scp=85068645412&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068645412&partnerID=8YFLogxK

U2 - 10.1145/3325480.3325497

DO - 10.1145/3325480.3325497

M3 - Conference contribution

T3 - C and C 2019 - Proceedings of the 2019 Creativity and Cognition

SP - 95

EP - 106

BT - C and C 2019 - Proceedings of the 2019 Creativity and Cognition

PB - Association for Computing Machinery, Inc

ER -