Abstract:

［Purpose/Significance］Data quality has been one of the serious problems of Altmetrics. In order to promote the theoretical development and practical application of Altmetrics, a thorough analysis on this problem needs to be conducted.［Design/Methodology］This study decomposed the life cycle of Altmetrics data, including data generation, data collection and data aggregation. Issues and impact factors of data quality have been analyzed from a dynamic perspective. ［Findings/Conclusion］In the stage of data generation, users' subjective wishes may cause data quality problems; In the stage of data collection, the accuracy of metadata from source platforms is not guaranteed, and different platforms may adopt different methods of document recognition, which could also affect the data quality; In the data aggregation stage, due to different targets and technical capabilities of various aggregators, the way of data integration and data cleaning is not clear, which also results in a lack of quality assurance. ［Originality/Value］This study illustrates the issue of Altmetrics data quality from the perspective of life cycle, which could provide an application of Altmetrics data in a more reasonable and accurate way in the future.

Keywords: Altmetrics, Data quality, Life cycle, Indicator value, Aggregating tools, Social media