Human accuracy in mobile data collection Abstract   The collection of participant data xe2x80x98in the wildxe2x80x99 is widely employed by HumanComputer Interaction researchers. A variety of methods, including experience sampling, mobile crowdsourcing, and citizen science, rely on repeated participant contributions for data collection. Given this strong reliance on participant data, ensuring that the data is complete, reliable, timely, and accurate is key. Although previous work has made significant progress on ensuring that a sufficient amount of data is collected, the accuracy of human contributions has remained underexposed. In this article we argue for an emerging need for an increased focus on this aspect of humanlabelled data. The articles published in this special issue demonstrate how a focus on the accuracy of the collected data has implications on all aspects of a study xe2x80x93 ranging from study design to the analysis and reporting of results. We put forward a fivepoint research agenda in which we outline future opportunities in assessing and improving human accuracy in mobile data collection.

Evaluating the crowd quality for subjective questions based on a Spark computing environment Abstract   Crowdsourcing is a popular method of data collection or problem solving, which has been widely used in business, scientific research and other fields. Personnel quality control is a key issue in the field of crowdsourcing, and the method of estimating personnel quality is receiving more and more attention. This paper proposes a new algorithm for the quality estimation of crowdsourcing personnel, which is based on subjective questions innovatively. Specifically, confidence intervals are used to indicate personnel quality. To improve its scalability and efficiency in big data environment, algorithm is implemented on Spark, a widelyadopted distributed computing platform. Finally, extensive experiments are conducted on realworld data sets, and results demonstrate that algorithm can effectively and quickly estimate the quality of crowdsourcing personnel in the big data environment.