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Short Abstract

Citizen science apps collect valuable biodiversity data, but coverage is uneven and data reliability varies. This study examines profiling strategies to detect behavioural, spatial, and temporal patterns that improve contribution quality while protecting user privacy.

Abstract

In recent years, mobile data collection apps have greatly expanded opportunities for citizen scientists to engage in biodiversity and environmental monitoring. However, the reliability and informative value of user-generated data remain uneven, influenced by individual behaviors, motivations, and contexts. This paper explores user profiling as a strategy to enhance both data quality and the overall scientific value of such platforms, while fully respecting contributor privacy.

We propose a framework for extracting and analyzing user- and app-level information to define metrics that capture behavioral, demographic, psychographic, and spatial aspects of user activity. Examples include activity duration, recurrence of observations, sustained effort, and geographic range of contributions. These metrics reveal spatial and temporal tendencies and preferences for certain activities or taxa, enabling more targeted guidance of monitoring efforts and user engagement.

Two complementary profiling approaches are considered: (a) direct profiling, based on explicit user preferences to tailor notifications and participation strategies, and (b) indirect profiling, where post-hoc analysis of collected metadata uncovers behavioral patterns that can inform app customization and data interpretation.

Our analysis, based on iNaturalist metadata, demonstrates how observation records can reveal user profiles that improve understanding of dataset structure, support better sampling design, and foster more effective participation.

While acknowledging the ethical dimensions of profiling, this work shows its potential to align user behaviors with platform goals, strengthen trust in citizen science, and maximize the collective value of participatory data collection.