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. 2022;5(1):687-729.
doi: 10.1007/s42001-021-00150-8. Epub 2021 Oct 20.

A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic

Charalampos Ntompras  1 , George Drosatos  2 , Eleni Kaldoudi  1   3
Affiliations

A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic

Charalampos Ntompras et al. J Comput Soc Sci. 2022.

Abstract

The COVID-19 pandemic has deeply impacted all aspects of social, professional, and financial life, with concerns and responses being readily published in online social media worldwide. This study employs probabilistic text mining techniques for a large-scale, high-resolution, temporal, and geospatial content analysis of Twitter related discussions. Analysis considered 20,230,833 English language original COVID-19-related tweets with global origin retrieved between January 25, 2020 and April 30, 2020. Fine grain topic analysis identified 91 meaningful topics. Most of the topics showed a temporal evolution with local maxima, underlining the short-lived character of discussions in Twitter. When compared to real-world events, temporal popularity curves showed a good correlation with and quick response to real-world triggers. Geospatial analysis of topics showed that approximately 30% of original English language tweets were contributed by USA-based users, while overall more than 60% of the English language tweets were contributed by users from countries with an official language other than English. High-resolution temporal and geospatial analysis of Twitter content shows potential for political, economic, and social monitoring on a global and national level.

Keywords: COVID-19; Geospatial analysis; Latent Dirichlet Allocation; Social media analysis; Topic modeling; Twitter.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Number of COVID-19-related English tweets per day from January 25 to April 30, 2020
Fig. 2
Fig. 2
Relative overall popularity of the topics categories
Fig. 3
Fig. 3
The top eight popular topics for the entire time span
Fig. 4
Fig. 4
Weekly variation of popularity for topics related to Life during the Pandemic category: a Most popular topics, with a peak of more than 2% for at least 1 week; b and c less popular topics
Fig. 5
Fig. 5
Weekly variation of popularity for topics related to Pandemic Management category. a Most popular topics, with a peak of more than 2% for at least 1 week; b and c less popular topics
Fig. 6
Fig. 6
Weekly variation of popularity for topics related to Medical category: a most popular topics, with a peak of more than 2% for at least 1 week; b and c less popular topics
Fig. 7
Fig. 7
Weekly variation of popularity for topics related to Outbreak category. a Most popular topics, with a peak of more than 2% for at least 1 week; and b less popular topics
Fig. 8
Fig. 8
Weekly variation of popularity for topics related to Lockdown category: a most popular topics, with a peak of more than 2% for at least 1 week; and b less popular topics
Fig. 9
Fig. 9
Weekly variation of popularity for topics related to Economy category: a most popular topics, with a peak of more than 2% for at least 1 week, and b less popular topics
Fig. 10
Fig. 10
Weekly variation of popularity for topics related to Cases and Deaths category: a most popular topics, with a peak of more than 2% for at least 1 week, and b less popular topics
Fig. 11
Fig. 11
Weekly variation of popularity for topics related to News and Fake News category
Fig. 12
Fig. 12
Weekly variation of popularity for topics related to Preventive Measures category
Fig. 13
Fig. 13
Geographical origin of the tweets in the final corpus for the top 20 contributing countries
Fig. 14
Fig. 14
Top 10 most popular tweets for the four countries with the highest number of tweets in the corpus
Fig. 15
Fig. 15
Representative events related to lockdown
Fig. 16
Fig. 16
Representative USA-related topics and events. a Most popular topics, with a peak of more than 2% for at least 1 week; and b less popular topics
Fig. 17
Fig. 17
Representative events related to disease outbreak
Fig. 18
Fig. 18
The topic 'toilet paper and other supplies panic-buying' and related events in the five countries where this topic was most popular
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