An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
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Abstract (arXiv)
The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out the distinctive characteristics of the Greater Region (GR) through conducting a data-driven exploratory study of Twitter COVID-19 information in the GR and related countries using machine learning and representation learning methods. We find that tweets volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 2020-01-22 to 2020-06-05, figuring out the main differences between GR and related countries.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Aug 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2023Knowledge-augmented Graph Machine Learning for Drug Discovery: A Surveycited by 6
- 2022BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweetscited by 2
- 2024A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweetscited by 2
- 2024A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets30 Mar 2024
- 2023Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey16 Feb 2023
- 2022BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets4 Nov 2022