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

arXiv: https://arxiv.org/abs/2009.09253 · HTML: https://mlsanity.com/paper/2009.09253/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2020.

## Built on by (most cited first, 3 of 3)

- [Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh](https://mlsanity.com/paper/2101.09667/) (2021)
- [Nonnegative Matrix Factorization to understand Spatio-Temporal Traffic Pattern Variations during COVID-19: A Case Study](https://mlsanity.com/paper/2111.03592/) (2021)
- [Evaluating Cross-Lingual Classification Approaches Enabling Topic Discovery for Multilingual Social Media Data](https://mlsanity.com/paper/2602.17051/) (2026)

## Abstract (arXiv)

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