# Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media

arXiv: https://arxiv.org/abs/2007.02304 · HTML: https://mlsanity.com/paper/2007.02304/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [A Rapid Review of Clustering Algorithms](https://mlsanity.com/paper/2401.07389/) (2024)
- [Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions](https://mlsanity.com/paper/2111.04415/) (2021)
- [Representation Learning for Short Text Clustering](https://mlsanity.com/paper/2109.09894/) (2021)
- [BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor](https://mlsanity.com/paper/2503.01880/) (2025)
- [Surveillance of COVID-19 Pandemic using Social Media: A Reddit Study in North Carolina](https://mlsanity.com/paper/2106.04515/) (2021)
- [A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets](https://mlsanity.com/paper/2111.08658/) (2021)
- [Leveraging Twitter Data for Sentiment Analysis of Transit User Feedback: An NLP Framework](https://mlsanity.com/paper/2310.07086/) (2023)
- [Content-Localization based System for Analyzing Sentiment and Hate Behaviors in Low-Resource Dialectal Arabic: English to Levantine and Gulf](https://mlsanity.com/paper/2312.03727/) (2023)
- [Towards Temporal Knowledge-Base Creation for Fine-Grained Opinion Analysis with Language Models](https://mlsanity.com/paper/2509.02363/) (2025)
- [Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic](https://mlsanity.com/paper/2105.08165/) (2021)
- [Graph-based Joint Pandemic Concern and Relation Extraction on Twitter](https://mlsanity.com/paper/2106.09929/) (2021)
- [Leveraging Social Media Data for COVID-19 Studies](https://mlsanity.com/paper/2606.10459/) (2026)

## Abstract (arXiv)

The outbreak of the novel Coronavirus Disease (COVID-19) has greatly influenced people's daily lives across the globe. Emergent measures and policies (e.g., lockdown, social distancing) have been taken by governments to combat this highly infectious disease. However, people's mental health is also at risk due to the long-time strict social isolation rules. Hence, monitoring people's mental health across various events and topics will be extremely necessary for policy makers to make the appropriate decisions. On the other hand, social media have been widely used as an outlet for people to publish and share their personal opinions and feelings. The large scale social media posts (e.g., tweets) provide an ideal data source to infer the mental health for people during this pandemic period. In this work, we propose a novel framework to analyze the topic and sentiment dynamics due to COVID-19 from the massive social media posts. Based on a collection of 13 million tweets related to COVID-19 over two weeks, we found that the positive sentiment shows higher ratio than the negative sentiment during the study period. When zooming into the topic-level analysis, we find that different aspects of COVID-19 have been constantly discussed and show comparable sentiment polarities. Some topics like ``stay safe home" are dominated with positive sentiment. The others such as ``people death" are consistently showing negative sentiment. Overall, the proposed framework shows insightful findings based on the analysis of the topic-level sentiment dynamics.