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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...mpenvurbsys.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.

Exploiting Flickr meta-data for predicting environmental features (2019)
Thesis
Jeawak, S. Exploiting Flickr meta-data for predicting environmental features. (Thesis). Cardiff University. Retrieved from https://uwe-repository....ribe.com/output/6004502

The photo-sharing website Flickr has become used as an informal information source in disciplines such as geography and ecology. Many recent studies have highlighted the fact that Flickr tags capture valuable ecological information, which can complem... Read More about Exploiting Flickr meta-data for predicting environmental features.

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, 101031. 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.

Embedding geographic locations for modelling the natural environment using flickr tags and structured data (2019)
Conference Proceeding
Jeawak, S. S., Jones, C. B., & Schockaert, S. (2019). Embedding geographic locations for modelling the natural environment using flickr tags and structured data. In Advances in Information Retrieval. , (51-66). https://doi.org/10.1007/978-3-030-15712-8_4

Meta-data from photo-sharing websites such as Flickr can be used to obtain rich bag-of-words descriptions of geographic locations, which have proven valuable, among others, for modelling and predicting ecological features. One important insight from... Read More about Embedding geographic locations for modelling the natural environment using flickr tags and structured data.

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.


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