A CS Track team of researchers including Reuma De-Groot, Yaela N Golumbic, Fernando Martínez Martínez and H. Ulrich Hoppe recently published a paper entitled “Developing a framework for investigating Citizen Science through a combination of web analytics and social science methods – the CS Track perspective”. This article presents the project’s framework that 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.
The paper describes 3 case studies which illustrate how three levels of analytics (micro, meso, macro) are implemented in CS Track. Each includes a description of the methodologies used and insights that have emerged as a result. In-depth descriptions of each have been published in different reports.
Micro level: CS response to COVID-19 challenges
In a micro level study, which involved a sample of CS projects, chosen by explicit criteria, the project researchers examined the power of CS to respond to emerging health challenges, through the example of the COVID-19 pandemic (see full report by Turbe et al. (2022))Content analysis of projects’ websites revealed projects focused on three main domains, namely tracking the spread of the pandemic in the population, investigating the influence of COVID-19 on people’s wellbeing, and investigating the COVID-19 virus biology. Citizen scientists’ tasks centred around responding to an online survey, self-tracking data from a wearable device and distributed computing. Overall projects were widely accessible, targeting a broad audience and requiring no special skills. Most projects required at least a moderate degree of effort from participants, asking a few different types of questions, and many required frequent contributions at regular intervals.
Meso level: Identification of Research Areas for CS projects
In a meso level study, which included a sample of all CS projects listed on the Zooniverse platform,, the project researchers investigated the multi-disciplinarity nature of projects through an assessment of research areas within a subset of projects in the CS Track database. This combined diagram of the results from this analysis is based on 218 project descriptions taken from the Zooniverse platform. Notably, 147 of these projects (67,4 %) have more than one associated research area. This illustrates one of our findings, namely that multi or inter-disciplinarity is a prevailing characteristic of CS projects.
Macro level: CS on Twitter
Twitter data was used in a recent analysis of discussions related to climate change. Here, machine learning techniques for detecting sentiments were applied to tweets originating from within and outside the CS community. In total a dataset of 26,000 original tweets and 95,000 retweets was analysed and the project researchers found that the climate change debate is less polarised within the CS community than in other communities on this social media platform.
The results and insights gained from the analysis briefly described above together with other data collected by the CS Track team members informed policy recommendations and are going to be made available from the beginning of 2023.
Read the full paper here.
Full report by Turbe et al. (2022)
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