Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset
arXiv:2003.10359
Abstract
The objective of this work is to explore popular discourse about the COVID-19 pandemic and policies implemented to manage it. Using Natural Language Processing, Text Mining, and Network Analysis to analyze corpus of tweets that relate to the COVID-19 pandemic, we identify common responses to the pandemic and how these responses differ across time. Moreover, insights as to how information and misinformation were transmitted via Twitter, starting at the early stages of this pandemic, are presented. Finally, this work introduces a dataset of tweets collected from all over the world, in multiple languages, dating back to January 22nd, when the total cases of reported COVID-19 were below 600 worldwide. The insights presented in this work could help inform decision makers in the face of future pandemics, and the dataset introduced can be used to acquire valuable knowledge to help mitigate the COVID-19 pandemic.
arXiv commenthttps://github.com/lopezbec/COVID19_Tweets_Dataset
Cited by in corpus (15)
- Causal Modeling of Twitter Activity During COVID-19
- ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks
- Large Arabic Twitter Dataset on COVID-19
- Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19
- NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset
- Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
- How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddings
- On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic
- EDNA-Covid: A Large-Scale Covid-19 Tweets Dataset Collected with the EDNA Streaming Toolkit
- A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets
- Revealing the Global Linguistic and Geographical Disparities of Public Awareness to Covid-19 Outbreak through Social Media
- An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
- TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
- Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media
- EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets