Predicting the environment from social media: A collective classification approach

Jeawak, Shelan S.; Jones, Christopher B.; Schockaert, Steven

Abstract

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. First, we show that Flickr tags capture information which is highly complementary to what is found in traditional structured environmental datasets. By combining Flickr tags with traditional datasets, we can thus make more accurate predictions than is possible using either Flickr tags or traditional datasets alone. Second, we propose a collective prediction model which crucially relies on Flickr tags to define a neighbourhood structure. The use of a collective prediction formulation is motivated by the fact that most environmental features are strongly spatially autocorrelated. While this suggests that geographic distance should play a key role in determining neighbourhoods, we show that considerable gains can be made by additionally taking Flickr tags and traditional data into consideration.

• Journal Article Type: Article
• Acceptance Date: Mar 29, 2020
• Online Publication Date: Apr 14, 2020
• Publication Date: Jul 1, 2020
• Deposit Date: Jun 5, 2020
• Journal: Computers, Environment and Urban Systems
• Print ISSN: 0198-9715
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 82
• Article Number: 101487
• DOI: https://doi.org/10.1016/j.compenvurbsys.2020.101487
• Keywords: Ecological Modelling; Geography, Planning and Development; General Environmental Science; Urban Studies; Collective prediction; Text mining; Social media; Ecology; Geographic information systems
• Public URL: https://uwe-repository.worktribe.com/output/6004494
• Related Public URLs: http://orca.cf.ac.uk/131089/
• Additional Information: This article is maintained by: Elsevier; Article Title: Predicting the environment from social media: A collective classification approach; Journal Title: Computers, Environment and Urban Systems; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.compenvurbsys.2020.101487; Content Type: article; Copyright: © 2020 Elsevier Ltd. All rights reserved.