Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study
This work addresses the need for effective topic detection in noisy social media data to provide insights into public behavior during the Covid-19 pandemic, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of analyzing complex social media data to understand spatio-temporal topic dynamics during the Covid-19 pandemic by proposing a tensor-based representation and Non-negative Tensor Factorization (NTF), applied to Australian tweets to identify and visualize these dynamics.
Social media platforms facilitate mankind a data-driven world by enabling billions of people to share their thoughts and activities ubiquitously. This huge collection of data, if analysed properly, can provide useful insights into people's behavior. More than ever, now is a crucial time under the Covid-19 pandemic to understand people's online behaviors detailing what topics are being discussed, and where (space) and when (time) they are discussed. Given the high complexity and poor quality of the huge social media data, an effective spatio-temporal topic detection method is needed. This paper proposes a tensor-based representation of social media data and Non-negative Tensor Factorization (NTF) to identify the topics discussed in social media data along with the spatio-temporal topic dynamics. A case study on Covid-19 related tweets from the Australia Twittersphere is presented to identify and visualize spatio-temporal topic dynamics on Covid-19