Mixing Social Media Analysis and Physical Models to Monitor Invasive Species

Invasive species, such as jellyfish, cause economic losses in millions annually. Therefore, being able to accurately monitor and predict jellyfish is vital to several stakeholders (e.g. tourism, fishery, government). A potential tool to help these communities could be by combining a biophysical drift model with a processing chain for soft information fusion which would predict jellyfish occurrences. To guarantee accuracy, the model needs to be validated by actual data. This data can be gathered from citizens who reported jellyfish sightings on social media or a dedicated citizen science mobile app. As the information provided by citizens is spread among numerous atomic reports, we use a platform for soft information fusion to aggregate and fuse these reports into a single information network. The soft information fusion platform relies on the use of domain knowledge, provided through an ontology. The information network can then be queried to extract relevant features to validate the jellyfish drift model. Future work includes the initialisation of the model with soft information, as well as making use of the different levels of quality of the reports provided by citizens, in order to assess the quality of the fused information.