Crowd Dynamics: Conflicts, Contradictions, and Community in Crowdsourcing

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dc.contributor.authorHansson, Karin
dc.contributor.authorLudwig, Thomas
dc.date.accessioned2020-06-06T13:06:07Z
dc.date.available2020-06-06T13:06:07Z
dc.date.issued2019
dc.date.issued2019
dc.identifier.doi10.1007/s10606-018-9343-z
dc.identifier.pissn1573-7551
dc.identifier.urihttp://dx.doi.org/10.1007/s10606-018-9343-z
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/3744
dc.publisherSpringer
dc.relation.ispartofComputer Supported Cooperative Work (CSCW): Vol. 28, No. 5
dc.relation.ispartofseriesComputer Supported Cooperative Work (CSCW)
dc.subjectContradiction
dc.subjectCrowd Dynamics
dc.subjectCrowdsourcing
dc.subjectCSCW
dc.titleCrowd Dynamics: Conflicts, Contradictions, and Community in Crowdsourcingde
dc.typeText/Journal Article
gi.citation.endPage794
gi.citation.startPage791
gi.citations.count12
gi.citations.elementAntónio Correia, Shoaib Jameel, Hugo Paredes, Benjamim Fonseca, Daniel Schneider (2019): Hybrid Machine-Crowd Interaction for Handling Complexity: Steps Toward a Scaffolding Design Framework, In: Human–Computer Interaction Series, doi:10.1007/978-3-030-12334-5_5
gi.citations.elementMingzhe Li, Wei Wang, Jin Zhang (2024): Towards Efficient and Deposit-Free Blockchain-Based Spatial Crowdsourcing, In: ACM Transactions on Sensor Networks 3(20), doi:10.1145/3656343
gi.citations.elementQuanwu Zhao, Jiamin Yuan, Yuqing Liu, Jiaqin Yang (2022): Continuous participation intention in on-demand logistics: interactive effects of order assignment and delivery-related information disclosure strategies, In: Industrial Management & Data Systems 11(122), doi:10.1108/imds-12-2021-0747
gi.citations.elementRui Lian, Yifeng Zheng, Cong Wang (2024): PrivRo: A Privacy-Preserving Crowdsourcing Service With Robust Quality Awareness, In: IEEE Transactions on Services Computing 4(17), doi:10.1109/tsc.2024.3377158
gi.citations.elementAntónio Correia, Andrea Grover, Daniel Schneider, Ana Paula Pimentel, Ramon Chaves, Marcos Antonio de Almeida, Benjamim Fonseca (2023): Designing for Hybrid Intelligence: A Taxonomy and Survey of Crowd-Machine Interaction, In: Applied Sciences 4(13), doi:10.3390/app13042198
gi.citations.elementPeter Washington (2023): A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health (Preprint), doi:10.2196/preprints.51138
gi.citations.elementZijing Ge, Xinxin Wang, Zeshui Xu (2021): A novel order evaluation model with nested probabilistic-numerical linguistic information applied to traditional order grabbing mode, In: Applied Intelligence 7(51), doi:10.1007/s10489-020-02088-2
gi.citations.elementYang Zhang, Ruohan Zong, Lanyu Shang, Ziyi Kou, Huimin Zeng, Dong Wang (2022): CrowdOptim: A Crowd-driven Neural Network Hyperparameter Optimization Approach to AI-based Smart Urban Sensing, In: Proceedings of the ACM on Human-Computer Interaction CSCW2(6), doi:10.1145/3555536
gi.citations.elementDanzhao Cheng, Eugene Ch’ng (2022): Facilitating Situated Crowdsourcing of 3D Cultural Heritage via Asynchronous Virtual Collaboration, doi:10.21203/rs.3.rs-2245108/v1
gi.citations.elementAsif Laghari, Hui He, Asiya Khan, Rashid Laghari, Shoulin Yin, Jiachi Wang (2022): Crowdsourcing platform for QoE evaluation for cloud multimedia services, In: Computer Science and Information Systems 3(19), doi:10.2298/csis220322038l
gi.citations.elementPeter Washington (2024): A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health, In: Journal of Medical Internet Research, doi:10.2196/51138
gi.citations.elementMurtuza Shahzad, Hamed Alhoori (2021): Public Reaction to Scientific Research via Twitter Sentiment Prediction, In: Journal of Data and Information Science 1(7), doi:10.2478/jdis-2022-0003

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JCSCW Vol. 28 (2019)