Using guided data collection to improve the quality of citizen science user-generated content

Razavi, Haniyesadat (2018) Using guided data collection to improve the quality of citizen science user-generated content. Masters thesis, Memorial University of Newfoundland.

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With the advent of Web 2.0, there has been tremendous growth in User-Generated Content (UGC), wherein members of the general public participate in contributing information online. Citizen science is a popular form of UGC in which participants support scientific data collection or analysis. However, in projects that rely on citizens to contribute data, obtaining data that is of sufficient quality to be useful for research is challenging. Among the challenges in obtaining data are: lack of control over the content of data supplied; lack of incentive to contribute; and lack of system flexibility to capture unanticipated data. Any of these challenges may lead to low-quality data that might not be useful in scientific research. Improving the data collection phase in online citizen science may facilitate capturing higher quality data. The primary purpose of this research is to propose and evaluate guidance features to support data entry to increase the quality of data collected. An experiment under three different conditions was conducted based on a citizen science project in the biology domain. Three types of guidance were tested to determine which is more effective in assisting the contributors in species identification (a widely used level of classification that is useful in biology research). The results demonstrate that using a guidance feature assists contributors in identifying species. Moreover, the guidance enables contributors to provide data of better quality in terms of relevance and objectivity. This thesis concludes by summarizing implications and provides suggestions for future study.

Item Type: Thesis (Masters)
Item ID: 13319
Additional Information: Includes bibliographical references (pages 62-67).
Keywords: Citizen Science, User Generated Content, Data Quality, Recommendation System
Department(s): Business Administration, Faculty of
Date: May 2018
Date Type: Submission
Library of Congress Subject Heading: Science -- Citizen participation -- Quality control; Data collection platforms -- Quality control; User-generated content

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