Understanding the Spatio-temporal Topic Dynamics of Covid-19 using Nonnegative Tensor Factorization: A Case Study

TL;DR

This paper introduces a tensor-based approach using Non-negative Tensor Factorization to analyze and visualize the evolving topics related to Covid-19 on social media, specifically Twitter in Australia.

Contribution

It presents a novel tensor representation and NTF method for spatio-temporal topic detection in social media data during Covid-19.

Findings

Identified key Covid-19 topics discussed in Australian Twitter data.

Visualized the evolution of Covid-19 discussions over time and space.

Demonstrated effectiveness of tensor methods for complex social media analysis.

Abstract

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