Using machine learning to understand Twitter users' urban green space activities during COVID-19 pandemic period

ABSTRACT

Volunteered Geographic Information (VGI) provides effective information for evaluating the usage of urban green space (UGS). Geo-referenced Tweets become very popular in the assessment of UGS use because of data availability and large data volume compared with traditional surveying methods, which are time-consuming and inefficient. However, previous studies lack efficient methods to extract and interpret Twitter data for UGS activities evaluation. Therefore, this paper aims to present a framework that enables high-efficient extraction of public UGS activities from Twitter. Greater London was selected as a case study to describe the framework development. First, Twitter data within Greater London over a certain COVID-19 lockdown period are collected, cleaned and pre-processed. Second, word vector representations were generated using Word2vec model, and then document vector representations were obtained by using Doc2vec model. Next, all the Tweets were clustered by using K-means algorithm to reveal the UGS activities during lockdown period. The framework can be used as a tool for UGS planners and managers to enable a holistic understanding of public activities engagement in UGS and increase the degree of public participation in UGS management. © 2022 IEEE.