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GroundTruth: Augmenting Expert Image Geolocation with Crowdsourcing and Shared Representations

Published: 07 November 2019 Publication History

Abstract

Expert investigators bring advanced skills and deep experience to analyze visual evidence, but they face limits on their time and attention. In contrast, crowds of novices can be highly scalable and parallelizable, but lack expertise. In this paper, we introduce the concept of shared representations for crowd--augmented expert work, focusing on the complex sensemaking task of image geolocation performed by professional journalists and human rights investigators. We built GroundTruth, an online system that uses three shared representations-a diagram, grid, and heatmap-to allow experts to work with crowds in real time to geolocate images. Our mixed-methods evaluation with 11 experts and 567 crowd workers found that GroundTruth helped experts geolocate images, and revealed challenges and success strategies for expert-crowd interaction. We also discuss designing shared representations for visual search, sensemaking, and beyond.

Supplementary Material

ZIP File (cscw107aux.zip)
This contains the subtitles file (GTCSCW_captions.sbv) for the video figure, as well as the video figure description (GT_CSCW_video_description).
MP4 File (cscw107.mp4)
Supplemental video

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Cited By

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  • (2024)OSINT Research Studios: A Flexible Crowdsourcing Framework to Scale Up Open Source Intelligence InvestigationsProceedings of the ACM on Human-Computer Interaction10.1145/36373828:CSCW1(1-38)Online publication date: 26-Apr-2024
  • (2024)Comparing Traditional and LLM-based Search for Image GeolocationProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638305(291-302)Online publication date: 10-Mar-2024
  • (2024)A Browser Extension for in-place Signaling and Assessment of MisinformationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642473(1-21)Online publication date: 11-May-2024
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Published In

cover image Proceedings of the ACM on Human-Computer Interaction
Proceedings of the ACM on Human-Computer Interaction  Volume 3, Issue CSCW
November 2019
5026 pages
EISSN:2573-0142
DOI:10.1145/3371885
Issue’s Table of Contents
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 the author(s) 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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Association for Computing Machinery

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Publication History

Published: 07 November 2019
Published in PACMHCI Volume 3, Issue CSCW

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Author Tags

  1. crowd
  2. crowd-augmented expert work
  3. crowdsourcing
  4. expert
  5. geolocation
  6. investigation
  7. journalism
  8. misinformation
  9. real-time crowdsourcing
  10. sensemaking
  11. shared representations
  12. verification
  13. visual search

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Cited By

View all
  • (2024)OSINT Research Studios: A Flexible Crowdsourcing Framework to Scale Up Open Source Intelligence InvestigationsProceedings of the ACM on Human-Computer Interaction10.1145/36373828:CSCW1(1-38)Online publication date: 26-Apr-2024
  • (2024)Comparing Traditional and LLM-based Search for Image GeolocationProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638305(291-302)Online publication date: 10-Mar-2024
  • (2024)A Browser Extension for in-place Signaling and Assessment of MisinformationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642473(1-21)Online publication date: 11-May-2024
  • (2024)Crowdsourced geolocation: Detailed exploration of mathematical and computational modeling approachesCognitive Systems Research10.1016/j.cogsys.2024.10126688(101266)Online publication date: Dec-2024
  • (2023)Designing for Hybrid Intelligence: A Taxonomy and Survey of Crowd-Machine InteractionApplied Sciences10.3390/app1304219813:4(2198)Online publication date: 8-Feb-2023
  • (2023)Accessible Text Tools for People with Cognitive Impairments and Non-Native Readers: Challenges and OpportunitiesProceedings of Mensch und Computer 202310.1145/3603555.3603569(250-266)Online publication date: 3-Sep-2023
  • (2023)Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content ModerationProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594080(1280-1291)Online publication date: 12-Jun-2023
  • (2023)Supporting High-Stakes Investigations with Expert-Led CrowdsourcingCompanion Proceedings of the 2023 ACM International Conference on Supporting Group Work10.1145/3565967.3571764(79-81)Online publication date: 8-Jan-2023
  • (2023)CoSINT: Designing a Collaborative Capture the Flag Competition to Investigate MisinformationProceedings of the 2023 ACM Designing Interactive Systems Conference10.1145/3563657.3595997(2551-2572)Online publication date: 10-Jul-2023
  • (2022)Datavoidant: An AI System for Addressing Political Data Voids on Social MediaProceedings of the ACM on Human-Computer Interaction10.1145/35556166:CSCW2(1-29)Online publication date: 11-Nov-2022
  • Show More Cited By

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