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Crowdsourcing represents a significant source of data which needs to be analyzed and interpreted. These tasks influence the quality of the output as well as the efficiency of the process. Visualization proved to be an effective way of dealing with large amount of data. In this paper we propose a visualization analytic model in the context of the CrowdTruth framework and CrowdTruth metrics for optimizing the crowdsourcing process and improving its data quality. The requirements for the dynamic, scalable and interactive visualizations were extracted through literature and interviews with users of the framework.