Twitch crowdsourcing: crowd contributions in short bursts of time
Abstract
To lower the threshold to participation in crowdsourcing, we present twitch crowdsourcing: crowdsourcing via quick contributions that can be completed in one or two seconds. We introduce Twitch, a mobile phone application that asks users to make a micro-contribution each time they unlock their phone. Twitch takes advantage of the common habit of turning to the mobile phone in spare moments. Twitch crowdsourcing activities span goals such as authoring a census of local human activity, rating stock photos, and extracting structured data from Wikipedia pages. We report a field deployment of Twitch where 82 users made 11,240 crowdsourcing contributions as they used their phone in the course of everyday life. The median Twitch activity took just 1.6 seconds, incurring no statistically distinguishable costs to unlock speed or cognitive load compared to a standard slide-to-unlock interface.
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Measurements of EDA and derived metrics have been successfully applied, including user experience evaluation tools, detecting office workload, and driver-related tasks.
“Measurements of EDA and derived metrics have been successfully applied, including, e.g., user experience evaluation tools [76], detecting office workload [216] and driver-related tasks [202,217,224].”
From A Survey on Measuring Cognitive Workload in Human-Computer Interaction (2023) · cites this paper as [224]
Users are increasingly likely to "snack" on apps, visiting them frequently, but in short bursts.
“Users are increasingly likely to “snack” on apps, visiting them frequently, but in short bursts [17,18].”
From Efficacy of a web-based, crowdsourced peer-to-peer cognitive reappraisal platform for depression: Randomized controlled trial (2015) · cites this paper as [18]
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Quick Question: Interrupting Users for Microtasks with Reinforcement LearningPreprint
Ho, B.-J., Balaji, B., Köseoglu, M., Sandha, S. S., Pei, S., & Srivastava, M. B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09515 · Cited by 7 · see citing works
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The Use of Historical Information to Support Civic Crowdsourcing
Sasao, T., & Konomi, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39862-4_43 · Cited by 3 · see citing works
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Matching or Crashing? Personality-based Team Formation in Crowdsourcing EnvironmentsPreprint
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LOCAL: Online Visibility for Local Shopkeepers Through Participatory Geo-information Systems
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Low Effort Crowdsourcing: Leveraging Peripheral Attention for Crowd Work
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Self-citationApplications for In-Situ Feedback on Social Network Notifications
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Intentional Strategies for Changing Character Traits
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A Microtask Drawing Generation System that Links with a Commercial Crowdsourcing Site
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Challenges and Lessons from Working with Data Collected by Crowdfunding in the Wild
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CloseUp—A Community-Driven Live Online Search Engine
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Solidarity and A.I. for Transitioning to Crowd Work during COVID-19
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CrowdCafe - Mobile Crowdsourcing PlatformPreprint
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Crowd-Aware Space Monitoring by Crowdsourcing a Micro QA Task
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Task Design and Crowd Sentiment in Biocollections Information Extraction
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Local Crowdsourcing for Annotating Audio: the Elevator Annotator platform
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- Authors
- Vaish, R., Wyngarden, K., Chen, J., Cheung, B., & Bernstein, M. S.
- Affiliations
- University of California, Santa Cruz (US)
- Stanford University (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2014-04-26
- Pages
- 3645-3654
- OpenAlex
- W2140225336
- Cited by
- 95
- References
- 33
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 1 · 2022: 3 · 2021: 5 · 2020: 6 · 2019: 15 · 2018: 12 · 2017: 14 · 2016: 17 · 2015: 9 · 2014: 7
- FWCI
- 9.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
- Keywords
- mobile crowdsourcing, field deployment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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