  Citizen science and machine learning should be considered for monitoring the
coastal and ocean environment due to the scale of threats posed by climate
change and the limited resources to fill knowledge gaps. Using data from the
annotation activity of citizen scientists in a Swedish marine project, we
constructed Deep Neural Network models to predict forthcoming engagement. We
tested the models to identify patterns in annotation engagement. Based on the
results, it is possible to predict whether an annotator will remain active in
future sessions. Depending on the goals of individual citizen science projects,
it may also be necessary to identify either those volunteers who will leave or
those who will continue annotating. This can be predicted by varying the
threshold for the prediction. The engagement metrics used to construct the
models are based on time and activity and can be used to infer latent
characteristics of volunteers and predict their task interest based on their
activity patterns. They can estimate if volunteers can accomplish a given
number of tasks in a certain amount of time, identify early on who is likely to
become a top contributor or identify who is likely to quit and provide them
with targeted interventions. The novelty of our predictive models lies in the
use of Deep Neural Networks and the sequence of volunteer annotations. A
limitation of our models is that they do not use embeddings constructed from
user profiles as input data, as many recommender systems do. We expect that
including user profiles would improve prediction performance.
