An Approach to Improve the Quality of User-Generated Content of Citizen Science Platforms
Bibliographic Data
| ID | 22034759 |
|---|---|
| Authors | Jiri Musto (0000-0002-7720-7101, Lappeenranta-Lahti University of Technology, corresponding author), Ajantha Dahanayake (0000-0003-4782-4296, Lappeenranta-Lahti University of Technology) |
| Year | 2021 |
| Volume | 10 |
| Issue | 7 |
| Pages | 434 |
| Publication date | 2021-06-25 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10070434 |
| OpenAlex | W3174655825 |
| Language | EN |
| References cited | 41 |
The quality of the user-generated content of citizen science platforms has been discussed widely among researchers. Content is categorized into data and information: data is content stored in a database of a citizen science platform, while information is context-dependent content generated by users. Understanding data and information quality characteristics and utilizing them during design improves citizen science platforms’ overall quality. This research investigates the integration of data and information quality characteristics into a citizen science platform for collecting information from the general public with no scientific training in the area where content is collected. The primary goal is to provide a framework for selecting and integrating data and information quality characteristics into the design for improving the content quality on platforms. The design and implementation of a citizen science platform that collects walking path conditions are presented, and the resulting implication is evaluated. The results show that the platform’s content quality can be improved by introducing quality characteristics during the design stage of the citizen science platform
Citizen science · Data quality · Data science · Information quality · Information system · World Wide Web · Advanced Clustering Algorithms Research · Computer Science · Data Quality and Management · Engineering · Semantic Web and Ontologies
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| Highly cited | No |
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