Digital Data Mining

Abstract

Abstract Recreational fishers who interact with internet-connected and smart devices generate large volumes of digital data that can be mined to gain valuable and often unique insight into recreational fisher behaviours. Common sources of digital data include general online activity (e.g., searches, page views), social media (e.g., fishing forums, Facebook), smartphone applications, and connected devices (e.g., smartphones, fish finders). Research is stimulating a great deal of interest in these data, but fisheries monitoring and management applications are rare. Our aim is to facilitate research and the appropriate adoption and integration of digital behaviour data within fisheries research, management, and governance. We begin with an inventory of available data sources, the types of fisher behaviours described, and methods for obtaining suitable and relevant datasets. We then identify barriers to the use of these data within a solutions-oriented framework that emphasises privacy and transparency, standardisation, non-probabilistic techniques, validation, and integration. Although digital data for describing fisher behaviours tend to accumulate passively, we describe digital platforms as a rare opportunity to engage large numbers of fishers in active data collection, and influence behaviours through education and outreach.

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