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All Outputs (4)

Predicting the environment from social media: A collective classification approach (2020)
Journal Article
Jeawak, S. S., Jones, C. B., & Schockaert, S. (2020). Predicting the environment from social media: A collective classification approach. Computers, Environment and Urban Systems, 82, https://doi.org/10.1016/j.compenvurbsys.2020.101487

We propose a method which uses Flickr tags to predict a wide variety of environmental features, such as climate data, land cover categories, species occurrence, and human assessments of scenicness. The role of Flickr tags in our method is two-fold. F... Read More about Predicting the environment from social media: A collective classification approach.

Predicting environmental features by learning spatiotemporal embeddings from social media (2019)
Journal Article
Jeawak, S. S., Jones, C. B., & Schockaert, S. (2020). Predicting environmental features by learning spatiotemporal embeddings from social media. Ecological Informatics, 55, https://doi.org/10.1016/j.ecoinf.2019.101031

Spatiotemporal modelling is an important task for ecology. Social media tags have been found to have great potential to assist in predicting aspects of the natural environment, particularly through the use of machine learning methods. Here we propose... Read More about Predicting environmental features by learning spatiotemporal embeddings from social media.

Mapping wildlife species distribution with social media: Augmenting text classification with species names (2018)
Journal Article
Jeawak, S. S., Jones, C. B., & Schockaert, S. (2018). Mapping wildlife species distribution with social media: Augmenting text classification with species names. LIPIcs, 114, https://doi.org/10.4230/LIPIcs.GIScience.2018.34

© Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. Social media has considerable potential as a source of passive citizen science observations of the natural environment, including wildlife monitoring. Here we compare and combine two ma... Read More about Mapping wildlife species distribution with social media: Augmenting text classification with species names.

Using flickr for characterizing the environment: An exploratory analysis (2017)
Journal Article
Jeawak, S. S., Jones, C. B., & Schockaert, S. (2017). Using flickr for characterizing the environment: An exploratory analysis. LIPIcs, 86, 21:1--21:13. https://doi.org/10.4230/LIPIcs.COSIT.2017.21

© Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. The photo-sharing website Flickr has become a valuable informal information source in disciplines such as geography and ecology. Some ecologists, for instance, have been manually analys... Read More about Using flickr for characterizing the environment: An exploratory analysis.