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. 2022 Feb 8;2(1):e32378. doi: 10.2196/32378

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©Emily Chen, Julie Jiang, Ho-Chun Herbert Chang, Goran Muric, Emilio Ferrara. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 08.02.2022.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included.

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(A) Detected shapes of identified clusters, ordered by when each cluster peaked in popularity. Each line indicates the respective cluster’s popularity over time. (B) The top 10 most used bigrams associated with each cluster and bar chart showing their total usage in terms of raw volume of tweets. The 11 clusters were (1) general coronavirus concerns, (2) public health measures, (3) Black Lives Matter, (4) Trump rallies, (5) 6 months after the first COVID-19 case, (6) Indian national exams, (7) the second COVID-19 wave, (8) Trump tests positive, (9) vaccine development, (10) vaccine rollout, and (11) COVID relief bill.