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Improving Paid Microtasks through Gamification and Adaptive Furtherance Incentives.

, , , and . WWW, page 333-343. ACM, (2015)

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Calibrating Mechanisms for Privacy Preserving Text Analysis., , , and . PrivateNLP@WSDM, volume 2573 of CEUR Workshop Proceedings, page 8-11. CEUR-WS.org, (2020)Improving Paid Microtasks through Gamification and Adaptive Furtherance Incentives., , , and . WWW, page 333-343. ACM, (2015)TEM: High Utility Metric Differential Privacy on Text., , , and . SDM, page 883-890. SIAM, (2023)Research Challenges in Designing Differentially Private Text Generation Mechanisms., , , and . FLAIRS, (2021)Workshop on Privacy in NLP (PrivateNLP 2020)., , and . WSDM, page 903-904. ACM, (2020)Beyond Monetary Incentives: Experiments in Paid Microtask Contests., and . ACM Trans. Soc. Comput., 2 (2): 6:1-6:31 (2019)Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text., , and . ICDM, page 210-219. IEEE, (2019)Density-Aware Differentially Private Textual Perturbations Using Truncated Gumbel Noise., , , , and . FLAIRS, (2021)Quick-and-clean extraction of linked data entities from microblogs., , , , and . SEMANTiCS, page 5-12. ACM, (2014)Privacy-Preserving Textual Analysis via Calibrated Perturbations., and . PrivateNLP@WSDM, volume 2573 of CEUR Workshop Proceedings, page 41-42. CEUR-WS.org, (2020)