Yi Ding

Assistant Professor at UT Dallas

Key Words: Crowd, Gig

Taxonomy

  • Crowdsourcing Types: Collaborative
  • Data: Experiments, Survey, Participant Observations, Interview, Public Data.
  • Solution Proposed:
  • Evaluation: Real-world Experiments
  • Research Scheme: Empirical, Data-Driven,

Comments

  • [CSCW19-Fiverr] has a short and quick survey on gig economy.
  • [CSCW19-Upwork] has a moderate survey on the “Information and Power Asymmetries in the Gig Economy”
  • [CSCW21-Quality] and [CSCW21-Crowdsensing] are system design.
  • [CSCW19-Fiverr] and [CSCW21-Quality] are based on existing data-sets (instead of interview, survey, or experiments).

Papers

  • Paying Crowd Workers for Collaborative Work, CSCW19-Paying
    • Research Question: How to pay collaborative crowdsourcing workers.
    • Contributions:
      • Categorization of existing collaborative crowdsourcing tasks.
      • Two payment methods.
      • Empirical results.
    • Experiments: Amazon Mechanical Turk
    • Evaluations: Survey
  • Understanding the Skill Provision in Gig Economy from a Network Perspective: A Case Study of Fiverr, CSCW19-Fiverr
    • Research Question: Provision of skills on gig platform
    • Contributions:
      • A large-scale, data-driven empirical case study on Fiverr to understand the provision of skills in gig economy.
      • Four research questions are studied around skill provision.
    • Data: Public Data (No Survey)
    • Data-Driven
  • When DiDi Is Not Really A Choice in Small Chinese Cities, Taxi Drivers Build Their Own, CSCW19-Didi
    • Research Question: Ride-sharing in low-resource areas.
    • Contributions:
      • Identify barriers that make DiDi fail to solve the intercity mobility problems in small cities.
      • Document an effective ride-sharing innovations built by people in low resource and rural areas.
    • Data: Participant Observations, Interview
    • Empirical
  • Gig Platforms, Tensions, Alliances and Ecosystems: An Actor-Network Perspective, CSCW19-Upwork
    • Research Questions: Tensions, Alliances and Ecosystems around gig platform.
    • Data: 39 Interviews
  • Individual and Collaborative Behaviors of Rideshare Drivers in Protecting their Safety, CSCW-Safety
    • Research Questions: Driver’s safety in rider-sharing
    • Contributions:
      • Identified safety concerns of rideshare drivers and the methods they use to deal with safety.
    • Data: 20 Interviews
  • Crowdsourcing Perceptions of Fair Predictors for Machine Learning: A Recidivism Case Study, CSCW19-Fair
    • Research Question: How do humans perceive fairness in intelligible models.
    • Contribution:
      • Investigates the feasibility of utilizing crowdsourcing for fair predictor assessment in machine learning.
    • Data: Public Data, Experiments on MTurk
  • Unpacking Sharing in the P2P Economy: The Impact of Shared Needs and Backgrounds on Ride-Sharing, CSCW20-Sharing
    • Research Question: Differences between sharing and gig economy (i.e., sharing resources or providing services).
    • Data: Participant Observations, Interview
    • Paper Narrative: Data Collection + Findings from Data Analysis + Discussion + Implications
  • CrowdCO-OP: Sharing Risks and Rewards in Crowdsourcing, CSCW20-CrowdCO-OP
    • Research Question: How to share risk and reward among crowd workers.
    • Research Scheme: Data-Driven, Hypothesis Test.
    • Experiments: Amazon Mechanical Turk
    • Paper Narrative: Problem Analysis + Experiment + Results and Analysis + Discussion
  • Delivery Work and the Experience of Social Isolation, CSCW21-Isolation
    • Research Question: Social isolation in gig workers.
    • Data: 21 Interviews
    • Paper Narrative: Interviews + Findings from Data Analysis + Discussion
  • Task Assignment Strategies for Crowd Worker Ability Improvement, CSCW21-Improvement
    • Research Question: Skill improvement for crowd workers.
    • Experiments: Amazon Mechanical Turk
    • Paper Narrative: Problem Definition + Solution + Experiments + Conclusion
  • Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks, CSCW21-Quality
    • Comments: Works done by Daiqing Zhang, major in mobile computing community
    • Research Question: Maximize task execution quality
    • Paper Narrative: Problem Formulation + Proposed Approach + Evaluation + Discussion
    • Research Scheme: System Design, Data-Driven
    • Data: Public Data-set
  • Understanding Driver-Passenger Interactions in Vehicular Crowdsensing, CSCW21-Crowdsensing
    • Research Question: Understand drivers’ and passengers’ practices, motivations, and challenges.
    • Research Scheme: System Design, Deployed System
    • Paper Narrative: System Design + Study Design + Deployment Results + Discussion

Ref.

[CSCW19-Paying] d’Eon, Greg, Joslin Goh, Kate Larson, and Edith Law. “Paying crowd workers for collaborative work.” Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-24.

[CSCW19-Fiverr] Huang, Keman, Jinhui Yao, and Ming Yin. “Understanding the skill provision in gig economy from a network perspective: A case study of fiverr.” Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-23.

[CSCW19-Didi] Wang, Yi. “When Didi is not really a choice in small Chinese cities, taxi drivers build their own.” Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-30.

[CSCW19-Upwork] Kinder, Eliscia, Mohammad Hossein Jarrahi, and Will Sutherland. “Gig platforms, tensions, alliances and ecosystems: An actor-network perspective.” Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-26.

[CSCW10-Safety] Almoqbel, Mashael Yousef, and Donghee Yvette Wohn. “Individual and collaborative behaviors of rideshare drivers in protecting their safety.” Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-21.

[CSCW19-Fair] Van Berkel, Niels, Jorge Goncalves, Danula Hettiachchi, Senuri Wijenayake, Ryan M. Kelly, and Vassilis Kostakos. “Crowdsourcing perceptions of fair predictors for machine learning: A recidivism case study.” Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-21.

[CSCW20-Sharing] Ma, Ning F., and Benjamin V. Hanrahan. “Unpacking Sharing in the Peer-to-Peer Economy: The Impact of Shared Needs and Backgrounds on Ride-Sharing.” Proceedings of the ACM on Human-Computer Interaction 4, no. CSCW1 (2020): 1-19.

[CSCW20-CrowdCO-OP] Fan, Shaoyang, Ujwal Gadiraju, Alessandro Checco, and Gianluca Demartini. “CrowdCO-OP: Sharing Risks and Rewards in Crowdsourcing.” Proceedings of the ACM on Human-Computer Interaction 4, no. CSCW2 (2020): 1-24.

[CSCW21-Isolation] Seetharaman, Bhavani, Joyojeet Pal, and Julie Hui. “Delivery Work and the Experience of Social Isolation.” Proceedings of the ACM on Human-Computer Interaction 5, no. CSCW1 (2021): 1-17.

[CSCW21-Improvement] Borromeo, Ria, Masaki Matsubara, Atsuyuki Morishima, and Sihem Amer-Yahia. “Task Assignment Strategies for Crowd Worker Ability Improvement.” In The 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing. 2021.

[CSCW21-Quality] Wang, Liang, Zhiwen Yu, Dingqi Yang, Tian Wang, En Wang, Bin Guo, and Daqing Zhang. “Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks.” Proceedings of the ACM on Human-Computer Interaction 5, no. CSCW2 (2021): 1-29.

[CSCW21-Crowdsensing] Agarwal, Dhruv, Srishti Agarwal, Vidur Singh, Rohita Kochupillai, Rosemary Pierce-Messick, Srinivasan Iyengar, and Mohit Jain. “Understanding Driver-Passenger Interactions in Vehicular Crowdsensing.” Proceedings of the ACM on Human-Computer Interaction 5, no. CSCW2 (2021): 1-24.