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Can the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19., , , , , , , , и . Pers. Ubiquitous Comput., 27 (1): 59-89 (2023)How Many Crowd Workers Do I Need? On Statistical Power when Crowdsourcing Relevance Judgments., , , , , и . ACM Trans. Inf. Syst., 42 (1): 21:1-21:26 (января 2024)Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19., , , , , , , , и . CoRR, (2021)Crowd_Frame: A Simple and Complete Framework to Deploy Complex Crowdsourcing Tasks Off-the-shelf., , , и . WSDM, стр. 1605-1608. ACM, (2022)A Neural Model to Jointly Predict and Explain Truthfulness of Statements., , , , и . ACM J. Data Inf. Qual., 15 (1): 4:1-4:19 (марта 2023)HITS Hits Readersourcing: Validating Peer Review Alternatives Using Network Analysis., , и . BIRNDL@SIGIR, том 2414 из CEUR Workshop Proceedings, стр. 70-82. CEUR-WS.org, (2019)Reproduce and Improve: An Evolutionary Approach to Select a Few Good Topics for Information Retrieval Evaluation., , , и . ACM J. Data Inf. Qual., 10 (3): 12:1-12:21 (2018)Transparent assessment of information quality of online reviews using formal argumentation theory., , , и . Inf. Syst., (2022)E-BART: Jointly Predicting and Explaining Truthfulness., , , и . TTO, стр. 18-27. Hacks Hackers, (2021)Crowdsourcing Peer Review: As We May Do., и . IRCDL, том 988 из Communications in Computer and Information Science, стр. 259-273. Springer, (2019)