  Citizen science projects engage people in activities that are part of a
scientific research effort. On multi-project citizen science platforms,
scientists can create projects consisting of tasks. Volunteers, in turn,
participate in executing the project's tasks. Such type of platforms seeks to
connect volunteers and scientists' projects, adding value to both. However,
little is known about volunteer's cross-project engagement patterns and the
benefits of such patterns for scientists and volunteers. This work proposes a
Goal, Question, and Metric (GQM) approach to analyse volunteers' cross-project
task execution patterns and employs the Semiotic Inspection Method (SIM) to
analyse the communicability of the platform's cross-project features. In doing
so, it investigates what are the features of platforms to foster volunteers'
cross-project engagement, to what extent multi-project platforms facilitate the
attraction of volunteers to perform tasks in new projects, and to what extent
multi-project participation increases engagement on the platforms. Results from
analyses on real platforms show that volunteers tend to explore multiple
projects, but they perform tasks regularly in just a few of them; few projects
attract much attention from volunteers; volunteers recruited from other
projects on the platform tend to get more engaged than those recruited outside
the platform. System inspection shows that platforms still lack personalised
and explainable recommendations of projects and tasks. The findings are
translated into useful claims about how to design and manage multi-project
platforms.

Human-Computer Interaction