Using Network Analysis to Characterize Participation and Interaction in a Citizen Science Online Community

Amarasinghe, Ishari; Manske, Sven; Hoppe, H. Ulrich; Santos, Patrícia; Hernández‐Leo, Davinia

doi:10.1007/978-3-030-85071-5_5

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…(1) Micro level applications : Based on small samples using human judgement in the filtering and selection such as the analysis of communication and collaboration in project web forums for example, the analysis of the “Chimp & See” project on the Zooniverse platform which provided detailed insights in participation and role-taking between scientists and volunteers (Amarasinghe et al, 2021 ).…”

Section: The Cs Track Projectmentioning

confidence: 99%

Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective

DeGroot

Golumbic

Martínez

et al. 2022

Front. Res. Metr. Anal.

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Over the past decade, Citizen Science (CS) has shown great potential to transform the power of the crowd into knowledge of societal value. Many projects and initiatives have produced high quality scientific results by mobilizing peoples' interest in science to volunteer for the public good. Few studies have attempted to map citizen science as a field, and assess its impact on science, society and ways to sustain its future practice. To better understand CS activities and characteristics, CS Track employs an analytics and analysis framework for monitoring the citizen science landscape. Within this framework, CS Track collates and processes information from project websites, platforms and social media and generates insights on key issues of concern to the CS community, such as participation patterns or impact on science learning. In this paper, we present the operationalization of the CS Track framework and its three-level analysis approach (micro-meso-macro) for applying analytics techniques to external data sources. We present three case studies investigating the CS landscape using these analytical levels and discuss the strengths and limitations of combining web-analytics with quantitative and qualitative research methods. This framework aims to complement existing methods for evaluating CS, address gaps in current observations of the citizen science landscape and integrate findings from multiple studies and methodologies. Through this work, CS Track intends to contribute to the creation of a measurement and evaluation scheme for CS and improve our understanding about the potential of analytics for the evaluation of CS.

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Section: The Cs Track Projectmentioning

confidence: 99%

Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective

DeGroot

Golumbic

Martínez

et al. 2022

Front. Res. Metr. Anal.

Self Cite

View full text Add to dashboard Cite

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Highly precise community science annotations of video camera‐trapped fauna in challenging environments

Arandjelovic,

Stephens,

Dieguez

et al. 2024

Remote Sens Ecol Conserv

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As camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp&See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp&See community scientists. We considered 29 classification categories, including 17 species and 12 higher‐level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4%, which increased to 98.2% when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring.

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Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects

Krukowski

Amarasinghe

Gutiérrez-Páez

et al. 2022

Collaboration Technologies and Social Computing

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Using Network Analysis to Characterize Participation and Interaction in a Citizen Science Online Community

Cited by 4 publications

References 20 publications

Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective

Developing a framework for investigating citizen science through a combination of web analytics and social science methods—The CS Track perspective

Highly precise community science annotations of video camera‐trapped fauna in challenging environments

Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects

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