This paper describes the MediaEval 2020 \textit{Predicting Media Memorability} task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its 3rd edition this year, as the prediction of short-term and long-term video memorability (VM) remains a challenging task. In 2020, the format remained the same as in previous editions. This year the videos are a subset of the TRECVid 2019 Video-to-Text dataset, containing more action rich video content as compared with the 2019 task. In this paper a description of some aspects of this task is provided, including its main characteristics, a description of the collection, the ground truth dataset, evaluation metrics and the requirements for participants' run submissions.

Increasingly social networks are being used for citizen science, where members of the public contribute knowledge to scientific endeavours. Tasks can be presented and solved using human computation, termed groupsourcing, with users benefiting from community tuition and experts gaining knowledge from the crowd. This paper gives details of a prototype that utilises groupsourcing to solve image classification tasks, to support social learning and to facilitate knowledge discovery in the domain of marine biology.

Crowdsourcing and citizen science have established themselves in the mainstream of research methodology in recent years, employing a variety of methods to solve problems using human computation. An approach described here, termed "groupsourcing", uses social networks to present problems and collect solutions. This paper details a method for archiving social network messages and investigates messages containing an image classification task in the domain of marine biology. In comparison to other methods, groupsourcing offers a high accuracy, data-driven and low cost approach.