Coronavirus (COVID-19) Geo-tagged Tweets Dataset

Name: Coronavirus (COVID-19) Geo-tagged Tweets Dataset
Creator: Rabindra Lamsal
License: https://creativecommons.org/licenses/by/4.0/
Keywords: Machine Learning, COVID-19

Citation Author(s):: Rabindra
Lamsal

School of Computing and Information Systems, The University of Melbourne
Submitted by:: Rabindra Lamsal
Last updated:: Sun, 05/19/2024 - 21:30
DOI:: 10.21227/fpsb-jz61
Data Format:: CSV
Link to Paper:: Design and analysis of a large-scale COVID-19 tweets dataset
Links:: Author's Homepage
License:: Creative Commons Attribution

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Categories:: Machine Learning
COVID-19
Keywords:: Corona Tweets Dataset, COVID-19 Tweets Dataset, Corona Tweets, COVID-19 Tweets, Corona Twitter Sentiment, COVID-19 Twitter Sentiment, SARS-CoV-2 Tweets Dataset, SARS-CoV-2 Twitter Sentiment, Coronavirus English Tweets Dataset, COVID-19 English Tweets Dataset, Coronavirus Geotagged Tweets, COVID-19 Geotagged Tweets

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CITE

Abstract

This dataset (GeoCOV19Tweets) contains IDs and sentiment scores of geo-tagged tweets related to the COVID-19 pandemic. The real-time Twitter feed is monitored for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. The tweet IDs in this dataset belong to the tweets created providing an exact location. You can reconstruct the dataset by hydrating these IDs. For detailed instructions on the hydration of tweet IDs, please read this article.

Announcements:

We release CrisisTransformers (https://huggingface.co/crisistransformers), a family of pre-trained language models and sentence encoders introduced in the paper "CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines such as BERT, RoBERTa, BERTweet, etc. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder outperforms the state-of-the-art by more than 17% in sentence encoding tasks. Please refer to the associated paper for more details.
MegaGeoCOV Extended — an extended version of MegaGeoCOV has been released. The dataset is introduced in the paper "A Twitter narrative of the COVID-19 pandemic in Australia".
We have released BillionCOV — a billion-scale COVID-19 tweets dataset for efficient hydration. Hydration takes time due to limits placed by Twitter on its tweet lookup endpoint. We re-hydrated the tweets present in COV19Tweets and found that more than 500 million tweet identifiers point to either deleted or protected tweets. If we avoid hydrating those tweet identifiers alone, it saves almost two months in a single hydration task. BillionCOV will receive quarterly updates, while COV19Tweets will continue to receive updates every day. Learn more about BillionCOV on its page: https://dx.doi.org/10.21227/871g-yp65
We also release a million-scale COVID-19-specific geotagged tweets dataset — MegaGeoCOV (on GitHub). The dataset is introduced in the paper "Twitter conversations predict the daily confirmed COVID-19 cases".

Related publications:

Rabindra Lamsal. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence, 51(5), 2790-2804.
Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey. ACM Computing Surveys, 55(4), 1-38. (arXiv)
Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Twitter conversations predict the daily confirmed COVID-19 cases. Applied Soft Computing, 129, 109603. (arXiv)
Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Addressing the location A/B problem on Twitter: the next generation location inference research. In 2022 ACM SIGSPATIAL LocalRec (pp. 1-4).
Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read. (2022). Where did you tweet from? Inferring the origin locations of tweets based on contextual information. In 2022 IEEE International Conference on Big Data (pp. 3935-3944). (arXiv)
Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2023). BillionCOV: An Enriched Billion-scale Collection of COVID-19 tweets for Efficient Hydration. Data in Brief, 48, 109229. (arXiv)
Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2023). A Twitter narrative of the COVID-19 pandemic in Australia. In 20th International ISCRAM Conference (pp. 353-370). (arXiv)
Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2024). CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts. Knowledge-Based Systems, 296, 111916. (arXiv)
Rabindra Lamsal, Maria Rodriguez Read, Shanika Karunasekera. (2024). Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts. In 21st International ISCRAM Conference (in press). (arXiv)

Below is a quick overview of this dataset.

— Dataset name: GeoCOV19Tweets Dataset

— Number of tweets : 502,067 tweets

— Coverage : Global

— Language : English (EN)

— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Policy and (iii) cite the following paper:

Lamsal, R. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence, 51, 2790-2804. https://doi.org/10.1007/s10489-020-02029-z

BibTeX entry:

@article{lamsal2021design,
  title={Design and analysis of a large-scale COVID-19 tweets dataset},
  author={Lamsal, Rabindra},
  journal={Applied Intelligence},
  volume={51},
  number={5},
  pages={2790--2804},
  year={2021},
  publisher={Springer}
}

— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)

— Dataset updates : Everyday

— Keywords and hashtags: keywords.tsv

Please visit this page (primary dataset) for more details.

Collection date & Number of tweets

(2020) March 20 - March 21: 1290 tweets

(2020) March 21 - March 22: 1020 tweets

(2020) March 22 - March 23: 1069 tweets

(2020) March 23 - March 24: 1072 tweets

(2020) March 24 - March 25: 949 tweets

(2020) March 25 - March 26: 913 tweets

(2020) March 26 - March 27: 810 tweets

(2020) March 27 - March 28: 855 tweets

(2020) March 28 - March 29: 828 tweets

(2020) March 29 - March 30: 5318 tweets (this file was added on June 29, 2021; its primary file corona_tweets_11b.csv was created while excluding retweets right at the API level; compared to other days the geo-tagged tweets are significantly higher for this day; Reason: Twitter's full-search endpoint was asked to create a corpus while excluding retweets; retweets have NULL geo and place objects, and since they were excluded I was able to come up with 5318 geo-tagged tweets out of 1,677,362 tweets collected for this day; this was quite an interesting observation to note)

(2020) March 30 - March 31: 538 tweets

(2020) March 31 - April 1: 636 tweets

(2020) April 1 - April 2: 608 tweets

(2020) April 2 - April 3: 661 tweets

(2020) April 3 - April 4: 592 tweets

(2020) April 4 - April 5: 661 tweets

(2020) April 5 - April 6: 709 tweets

(2020) April 6 - April 7: 549 tweets

(2020) April 7 - April 8: 593 tweets

(2020) April 8 - April 9: 491 tweets

(2020) April 9 - April 10: 507 tweets

(2020) April 10 - April 11: 534 tweets

(2020) April 11 - April 12: 539 tweets

(2020) April 12- April 13: 543 tweets

(2020) April 13 - April 14: 510 tweets

(2020) April 14 - April 15: 387 tweets

(2020) April 15 - April 16: 321 tweets

(2020) April 16 - April 17: 443 tweets

(2020) April 17 - April 18: 373 tweets

(2020) April 18 - April 19: 1020 tweets

(2020) April 19 - April 20: 884 tweets

(2020) April 20 - April 21: 869 tweets

(2020) April 21 - April 22: 878 tweets

(2020) April 22 - April 23: 831 tweets

(2020) April 23 - April 24: 818 tweets

(2020) April 24 - April 25: 747 tweets

(2020) April 25- April 26: 693 tweets

(2020) April 26 - April 27: 939 tweets

(2020) April 27 - April 28: 744 tweets

(2020) April 28 - April 29: 1408 tweets

(2020) April 29 - April 30: 1751 tweets

(2020) April 30 - May 1: 1637 tweets

(2020) May 1 - May 2: 1866 tweets

(2020) May 2 - May 3: 1839 tweets

(2020) May 3 - May 4: 1566 tweets

(2020) May 4 - May 5: 1615 tweets

(2020) May 5 - May 6: 1635 tweets

(2020) May 6 - May 7: 1571 tweets

(2020) May 7 - May 8: 1621 tweets

(2020) May 8 - May 9: 1684 tweets

(2020) May 9 - May 10: 1474 tweets

(2020) May 10 - May 11: 1130 tweets

(2020) May 11 - May 12: 1281 tweets

(2020) May 12- May 13: 1630 tweets

(2020) May 13 - May 14: 1480 tweets

(2020) May 14 - May 15: 1652 tweets

(2020) May 15 - May 16: 1583 tweets

(2020) May 16 - May 17: 1487 tweets

(2020) May 17 - May 18: 1341 tweets

(2020) May 18 - May 19: 1398 tweets

(2020) May 19 - May 20: 1389 tweets

(2020) May 20 - May 21: 1397 tweets

(2020) May 21 - May 22: 1562 tweets

(2020) May 22 - May 23: 1558 tweets

(2020) May 23 - May 24: 1299 tweets

(2020) May 24 - May 25: 1297 tweets

(2020) May 25- May 26: 1190 tweets

(2020) May 26 - May 27: 1184 tweets

(2020) May 27 - May 28: 1257 tweets

(2020) May 28 - May 29: 1277 tweets

(2020) May 29 - May 30: 1202 tweets

(2020) May 30 - May 31: 1209 tweets

(2020) May 31 - June 1: 1080 tweets

(2020) June 1 - June 2: 1233 tweets

(2020) June 2 - June 3: 917 tweets

(2020) June 3 - June 4: 1055 tweets

(2020) June 4 - June 5: 1117 tweets

(2020) June 5 - June 6: 1184 tweets

(2020) June 6 - June 7: 1093 tweets

(2020) June 7 - June 8: 1054 tweets

(2020) June 8 - June 9: 1180 tweets

(2020) June 9 - June 10: 1155 tweets

(2020) June 10 - June 11: 1131 tweets

(2020) June 11 - June 12: 1148 tweets

(2020) June 12- June 13: 1189 tweets

(2020) June 13 - June 14: 1045 tweets

(2020) June 14 - June 15: 1024 tweets

(2020) June 15 - June 16: 1663 tweets

(2020) June 16 - June 17: 1692 tweets

(2020) June 17 - June 18: 1634 tweets

(2020) June 18 - June 19: 1610 tweets

(2020) June 19 - June 20: 1698 tweets

(2020) June 20 - June 21: 1613 tweets

(2020) June 21 - June 22: 1419 tweets

(2020) June 22 - June 23: 1524 tweets

(2020) June 23 - June 24: 1431 tweets

(2020) June 24 - June 25: 1454 tweets

(2020) June 25- June 26: 1539 tweets

(2020) June 26 - June 27: 1403 tweets

(2020) June 27 - June 28: 1766 tweets

(2020) June 28 - June 29: 1405 tweets

(2020) June 29 - June 30: 1534 tweets

(2020) June 30 - June 31: 1519 tweets

(2020) July 1 - July 2: 1841 tweets

(2020) July 2 - July 3: 1434 tweets

(2020) July 3 - July 4: 1475 tweets

(2020) July 4 - July 5: 2028 tweets

(2020) July 5 - July 6: 1491 tweets

(2020) July 6 - July 7: 1275 tweets

(2020) July 7 - July 8: 1336 tweets

(2020) July 8 - July 9: 1428 tweets

(2020) July 9 - July 10: 1831 tweets

(2020) July 10 - July 11: 1578 tweets

(2020) July 11 - July 12: 1575 tweets

(2020) July 12 - July 13: 1346 tweets

(2020) July 13 - July 14: 1295 tweets

(2020) July 14 - July 15: 1372 tweets

(2020) July 15 - July 16: 1213 tweets

(2020) July 16 - July 17: 1339 tweets

(2020) July 17 - July 18: 1588 tweets

(2020) July 18 - July 19: 1647 tweets

(2020) July 19 - July 20: 1452 tweets

(2020) July 20 - July 21: 1344 tweets

(2020) July 21 - July 22: 1557 tweets

(2020) July 22 - July 23: 1556 tweets

(2020) July 23 - July 24: 1541 tweets

(2020) July 24 - July 25: 1670 tweets

(2020) July 25 - July 26: 1536 tweets

(2020) July 26 - July 27: 1415 tweets

(2020) July 27 - July 28: 1262 tweets

(2020) July 28 - July 29: 1192 tweets

(2020) July 29 - July 30: 1284 tweets

(2020) July 30 - July 31: 1198 tweets

(2020) July 31 - August 1: 1399 tweets

(2020) August 1 - August 2: 1462 tweets

(2020) August 2 - August 3: 1247 tweets

(2020) August 3 - August 4: 1375 tweets

(2020) August 4 - August 5: 1507 tweets

(2020) August 5 - August 6: 1557 tweets

(2020) August 6 - August 7: 1576 tweets

(2020) August 7 - August 8: 1634 tweets

(2020) August 8 - August 9: 1630 tweets

(2020) August 9 - August 10: 1427 tweets

(2020) August 10 - August 11: 1363 tweets

(2020) August 11 - August 12: 1501 tweets

(2020) August 12 - August 13: 1632 tweets

(2020) August 13 - August 14: 1674 tweets

(2020) August 14 - August 15: 1689 tweets

(2020) August 15 - August 16: 1716 tweets

(2020) August 16 - August 17: 1515 tweets

(2020) August 17 - August 18: 1593 tweets

(2020) August 18 - August 19: 1945 tweets

(2020) August 19 - August 20: 1571 tweets

(2020) August 20 - August 21: 1667 tweets

(2020) August 21 - August 22: 1592 tweets

(2020) August 22 - August 23: 1439 tweets

(2020) August 23 - August 24: 1273 tweets

(2020) August 24 - August 25: 1379 tweets

(2020) August 25 - August 26: 1538 tweets

(2020) August 26 - August 27: 1535 tweets

(2020) August 27 - August 28: 1447 tweets

(2020) August 28 - August 29: 1376 tweets

(2020) August 29 - August 30: 1421 tweets

(2020) August 30 - August 31: 1195 tweets

(2020) August 31 - September 1: 1298 tweets

(2020) September 1 - September 2: 1449 tweets

(2020) September 2 - September 3: 1326 tweets

(2020) September 3 - September 4: 1299 tweets

(2020) September 4 - September 5: 1586 tweets

(2020) September 5 - September 6: 1374 tweets

(2020) September 6 - September 7: 1274 tweets

(2020) September 7 - September 8: 1316 tweets

(2020) September 8 - September 9: 1379 tweets

(2020) September 9 - September 10: 1120 tweets

(2020) September 10 - September 11: 628 tweets

(2020) September 11 - September 12: 678 tweets

(2020) September 12 - September 13: 681 tweets

(2020) September 13 - September 14: 644 tweets

(2020) September 14 - September 15: 645 tweets

(2020) September 15 - September 16: 693 tweets

(2020) September 16 - September 17: 635 tweets

(2020) September 17 - September 18: 582 tweets

(2020) September 18 - September 19: 737 tweets

(2020) September 19 - September 20: 702 tweets

(2020) September 20 - September 21: 588 tweets

(2020) September 21 - September 22: 660 tweets

(2020) September 22 - September 23: 634 tweets

(2020) September 23 - September 24: 607 tweets

(2020) September 24 - September 25: 666 tweets

(2020) September 25 - September 26: 660 tweets

(2020) September 26 - September 27: 657 tweets

(2020) September 27 - September 28: 600 tweets

(2020) September 28 - September 29: 635 tweets

(2020) September 29 - September 30: 609 tweets

(2020) September 30 - October 1: 580 tweets

(2020) October 1 - October 2: 641 tweets

(2020) October 2 - October 3: 318 tweets

(2020) October 3 - October 4: 384 tweets

(2020) October 4 - October 5: 389 tweets

(2020) October 5 - October 6: 361 tweets

(2020) October 6 - October 7: 396 tweets

(2020) October 7 - October 8: 464 tweets

(2020) October 8 - October 9: 537 tweets

(2020) Ocotber 9 - October 10: 574 tweets

(2020) October 10 - October 11: 577 tweets

(2020) October 11 - October 12: 504 tweets

(2020) October 12 - October 13: 551 tweets

(2020) October 13 - October 14: 486 tweets

(2020) October 14 - October 15: 611 tweets

(2020) October 15 - October 16: 518 tweets

(2020) October 16 - October 17: 593 tweets

(2020) October 17 - October 18: 624 tweets

(2020) October 18 - October 19: 507 tweets

(2020) October 19 - October 20: 594 tweets

(2020) October 20 - October 21: 589 tweets

(2020) October 21 - October 22: 580 tweets

(2020) October 22 - October 23: 582 tweets

(2020) October 23 - October 24: 707 tweets

(2020) October 24 - October 25: 644 tweets

(2020) October 25 - October 26: 507 tweets

(2020) October 26 - October 27: 576 tweets

(2020) October 27 - October 28: 485 tweets

(2020) October 28 - October 29: 537 tweets

(2020) October 29 - October 30: 686 tweets

(2020) October 30 - October 31: 698 tweets

(2020) October 31 - November 01: 1070 tweets

(2020) November 1 - November 2: 780 tweets

(2020) November 2 - November 3: 690 tweets

(2020) November 3 - November 4: 763 tweets

(2020) November 4 - November 5: 838 tweets

(2020) November 5 - November 6: 944 tweets

(2020) November 6 - November 7: 734 tweets

(2020) November 7 - November 8: 691 tweets

(2020) November 8 - November 9: 616 tweets

(2020) November 9 - November 10: 463 tweets

(2020) November 10 - November 11: 618 tweets

(2020) November 11 - November 12: 632 tweets

(2020) November 12 - November 13: 586 tweets

(2020) November 13 - November 14: 620 tweets

(2020) November 14 - November 15: 674 tweets

(2020) November 15 - November 16: 637 tweets

(2020) November 16 - November 17: 521 tweets

(2020) November 17 - November 18: 591 tweets

(2020) November 18 - November 19: 639 tweets

(2020) November 19 - November 20: 673 tweets

(2020) November 20 - November 21: 657 tweets

(2020) November 21 - November 22: 694 tweets

(2020) November 22 - November 23: 644 tweets

(2020) November 23 - November 24: 595 tweets

(2020) November 24 - November 25: 649 tweets

(2020) November 25 - November 26: 675 tweets

(2020) November 26 - November 27: 821 tweets

(2020) November 27 - November 28: 606 tweets

(2020) November 28 - November 29: 633 tweets

(2020) November 29 - November 30: 560 tweets

(2020) November 30 - December 1: 551 tweets

(2020) December 1 - December 2: 613 tweets

(2020) December 2 - December 3: 615 tweets

(2020) December 3 - December 4: 624 tweets

(2020) December 4 - December 5: 582 tweets

(2020) December 5 - December 6: 630 tweets

(2020) December 6 - December 7: 422 tweets

(2020) December 7 - December 8: 649 tweets

(2020) December 8 - December 9: 526 tweets

(2020) December 9 - December 10: 542 tweets

(2020) December 10 - December 11: 521 tweets

(2020) December 11 - December 12: 630 tweets

(2020) December 12 - December 13: 690 tweets

(2020) December 13 - December 14: 515 tweets

(2020) December 14 - December 15: 528 tweets

(2020) December 15 - December 16: 618 tweets

(2020) December 16 - December 17: 577 tweets

(2020) December 17 - December 18: 604 tweets

(2020) December 18 - December 19: 597 tweets

(2020) December 19 - December 20: 482 tweets

(2020) December 20 - December 21: 591 tweets

(2020) December 21 - December 22: 530 tweets

(2020) December 22 - December 23: 555 tweets

(2020) December 23 - December 24: 616 tweets

(2020) December 24 - December 25: 752 tweets

(2020) December 25 - December 26: 701 tweets

(2020) December 26 - December 27: 540 tweets

(2020) December 27 - December 28: 430 tweets

(2020) December 28 - December 29: 535 tweets

(2020) December 29 - December 30: 573 tweets

(2020) December 30 - December 31: 559 tweets

(2020/2021) December 31 - January 1: 877 tweets

(2021) January 1 - January 2: 582 tweets

(2021) January 2 - January 3: 458 tweets

(2021) January 3 - January 4: 423 tweets

(2021) January 4 - January 5: 479 tweets

(2021) January 5 - January 6: 675 tweets

(2021) January 6 - January 7: 584 tweets

(2021) January 7 - January 8: 662 tweets

(2021) January 8 - January 9: 646 tweets

(2021) January 9 - January 10: 677 tweets

(2021) January 10 - January 11: 554 tweets

(2021) January 11 - January 12: 577 tweets

(2021) January 12 - January 13: 660 tweets

(2021) January 13 - January 14: 566 tweets

(2021) January 14 - January 15: 709 tweets

(2021) January 15 - January 16: 625 tweets

(2021) January 16 - January 17: 545 tweets

(2021) January 17 - January 18: 587 tweets

(2021) January 18 - January 19: 541 tweets

(2021) January 19 - January 20: 625 tweets

(2021) January 20 - Janaury 21: 505 tweets

(2021) Janaury 21 - January 22: 534 tweets

(2021) Janaury 22 - January 23: 584 tweets

(2021) January 23 - January 24: 591 tweets

(2021) January 24 - January 25: 561 tweets

(2021) January 25 - January 26: 516 tweets

(2021) January 26 - January 27: 596 tweets

(2021) January 27 - January 28: 580 tweets

(2021) January 28 - January 29: 660 tweets

(2021) January 29 - January 30: 784 tweets

(2021) January 30 - January 31: 774 tweets

(2021) January 31 - February 1: 703 tweets

(2021) February 1 - February 2: 777 tweets

(2021) February 2 - February 3: 688 tweets

(2021) February 3 - February 4: 713 tweets

(2021) February 4 - February 5: 651 tweets

(2021) February 5 - February 6: 743 tweets

(2021) February 6 - February 7: 631 tweets

(2021) February 7 - February 8: 685 tweets

(2021) February 8 - February 9: 627 tweets

(2021) February 9 - February 10: 665 tweets

(2021) February 10 - February 11: 697 tweets

(2021) February 11 - February 12: 614 tweets

(2021) February 12 - February 13: 736 tweets

(2021) February 13 - February 14: 601 tweets

(2021) February 14 - February 15: 594 tweets

(2021) February 15 - February 16: 648 tweets

(2021) February 16 - February 17: 656 tweets

(2021) February 17 - February 18: 675 tweets

(2021) February 18 - February 19: 627 tweets

(2021) February 19 - February 20: 637 tweets

(2021) February 20 - February 21: 609 tweets

(2021) February 21 - February 22: 559 tweets

(2021) February 22 - February 23: 644 tweets

(2021) February 23 - February 24: 615 tweets

(2021) February 24 - February 25: 646 tweets

(2021) February 25 - February 26: 643 tweets

(2021) February 26 - February 27: 578 tweets

(2021) February 27 - February 28: 669 tweets

(2021) February 28 - March 1: 581 tweets

(2021) March 1 - March 2: 568 tweets

(2021) March 2 - March 3: 564 tweets

(2021) March 3 - March 4: 383 tweets

(2021) March 4 - March 5: 599 tweets

(2021) March 5 - March 6: 588 tweets

(2021) March 6 - March 7: 632 tweets

(2021) March 7 - March 8: 597 tweets

(2021) March 8 - March 9: 566 tweets

(2021) March 9 - March 10: 617 tweets

(2021) March 10 - March 11: 655 tweets

(2021) March 11 - March 12: 715 tweets

(2021) March 12 - March 13: 702 tweets

(2021) March 13 - March 14: 700 tweets

(2021) March 14 - March 15: 658 tweets

(2021) March 15 - March 16: 751 tweets

(2021) March 16 - March 17: 685 tweets

(2021) March 17 - March 18: 842 tweets

(2021) March 18 - March 19: 762 tweets

(2021) March 19 - March 20: 679 tweets

(2021) March 20 - March 21: 724 tweets

(2021) March 21 - March 22: 692 tweets

(2021) March 22 - March 23: 694 tweets

(2021) March 23 - March 24: 760 tweets

(2021) March 24 - March 25: 778 tweets

(2021) March 25 - March 26: 784 tweets

(2021) March 26 - March 27: 788 tweets

(2021) March 27 - March 28: 686 tweets

(2021) March 28 - March 29: 626 tweets

(2021) March 29 - March 30: 713 tweets

(2021) March 30 - March 31: 736 tweets

(2021) March 31 - April 1: 736 tweets

(2021) April 1 - April 2: 738 tweets

(2021) April 2 - April 3: 726 tweets

(2021) April 3 - April 4: 662 tweets

(2021) April 4 - April 5: 627 tweets

(2021) April 5 - April 6: 720 tweets

(2021) April 6 - April 7: 732 tweets

(2021) April 7 - April 8: 776 tweets

(2021) April 8 - April 9: 702 tweets

(2021) April 9 - April 10: 722 tweets

(2021) April 10 - April 11: 626 tweets

(2021) April 11 - April 12: 589 tweets

(2021) April 12 - April 13: 706 tweets

(2021) April 13 - April 14: 799 tweets

(2021) April 14 - April 15: 547 tweets

(2021) April 15 - April 16: 522 tweets

(2021) April 16 - April 17: 641 tweets

(2021) April 17 - April 18: 511 tweets

(2021) April 18 - April 19: 578 tweets

(2021) April 19 - April 20: 640 tweets

(2021) April 20 - April 21: 624 tweets

(2021) April 21 - April 22: 584 tweets

(2021) April 22 - April 23: 611 tweets

(2021) April 23 - April 24: 631 tweets

(2021) April 24 - April 25: 570 tweets

(2021) April 25 - April 26: 519 tweets

(2021) April 26 - April 27: 523 tweets

(2021) April 27 - April 28: 577 tweets

(2021) April 28 - April 29: 608 tweets

(2021) April 29 - April 30: 831 tweets

(2021) April 30 - May 1: 535 tweets

(2021) May 1 - May 2: 601 tweets

(2021) May 2 - May 3: 507 tweets

(2021) May 3 - May 4: 565 tweets

(2021) May 4 - May 5: 575 tweets

(2021) May 5 - May 6: 605 tweets

(2021) May 6 - May 7: 1115 tweets

(2021) May 7 - May 8: 1263 tweets

(2021) May 8 - May 9: 530 tweets

(2021) May 9 - May 10: 481 tweets

(2021) May 10 - May 11: 518 tweets

(2021) May 11 - May 12: 424 tweets

(2021) May 12 - May 13: 508 tweets

(2021) May 13 - May 14: 632 tweets

(2021) May 14 - May 15: 627 tweets

(2021) May 15 - May 16: 484 tweets

(2021) May 16 - May 17: 451 tweets

(2021) May 17 - May 18: 505 tweets

(2021) May 18 - May 19: 486 tweets

(2021) May 19 - May 20: 522 tweets

(2021) May 20 - May 21: 493 tweets

(2021) May 21 - May 22: 450 tweets

(2021) May 22 - May 23: 451 tweets

(2021) May 23 - May 24: 353 tweets

(2021) May 24 - May 25: 457 tweets

(2021) May 25 - May 26: 421 tweets

(2021) May 26 - May 27: 454 tweets

(2021) May 27 - May 28: 489 tweets

(2021) May 28 - May 29: 484 tweets

(2021) May 29 - May 30: 432 tweets

(2021) May 30 - May 31: 369 tweets

(2021) May 31 - June 1: 420 tweets

(2021) June 1 - June 2: 457 tweets

(2021) June 2 - June 3: 398 tweets

(2021) June 3 - June 4: 400 tweets

(2021) June 4 - June 5: 470 tweets

(2021) June 5 - June 6: 390 tweets

(2021) June 6 - June 7: 310 tweets

(2021) June 7 - June 8: 366 tweets

(2021) June 8 - June 9: 381 tweets

(2021) June 9 - June 10: 408 tweets

(2021) June 10 - June 11: 402 tweets

(2021) June 11 - June 12: 376 tweets

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(2023) January 14 - January 15: 54 tweets

(2023) January 15 - January 16: 51 tweets

(2023) January 16 - January 17: 44 tweets

(2023) January 17 - January 18: 58 tweets

(2023) January 18 - January 19: 50 tweets

(2023) January 19 - January 20: 67 tweets

(2023) January 20 - January 21: 55 tweets

(2023) January 21 - January 22: 66 tweets

(2023) January 22 - January 23: 56 tweets

(2023) January 23 - January 24: 51 tweets

(2023) January 24 - January 25: 51 tweets

(2023) January 25 - January 26: 42 tweets

(2023) January 26 - January 27: 51 tweets

(2023) January 27 - January 28: 56 tweets

(2023) January 28 - January 29: 52 tweets

(2023) January 29 - January 30: 46 tweets

(2023) January 30 - January 31: 48 tweets

(2023) January 31 - February 1: 57 tweets

(2023) February 1 - February 2: 34 tweets

(2023) February 2 - February 3: 45 tweets

(2023) February 3 - February 4: 59 tweets

(2023) February 4 - February 5: 41 tweets

(2023) February 5 - February 6: 29 tweets

(2023) February 6 - February 7: 51 tweets

(2023) February 7 - February 8: 32 tweets

(2023) February 8 - February 9: 50 tweets

(2023) February 9 - February 10: 23 tweets

(2023) February 10 - February 11: 49 tweets

(2023) February 11 - February 12: 36 tweets

(2023) February 12 - February 13: 24 tweets

(2023) February 13 - February 14: 34 tweets

(2023) February 14 - February 15: 16 tweets

(2023) February 15 - February 16: 16 tweets

(2023) February 16 - February 17: 28 tweets

(2023) February 17 - February 18: 21 tweets

(2023) February 18 - February 19: 17 tweets

(2023) February 19 - February 20: 14 tweets

(2023) February 20 - February 21: 15 tweets

(2023) February 21 - February 22: 27 tweets

(2023) February 22 - February 23: 25 tweets

(2023) February 23 - February 24: 24 tweets

(2023) February 24 - February 25: 33 tweets

(2023) February 25 - February 26: 30 tweets

(2023) February 26 - February 27: 56 tweets

(2023) February 27 - February 28: 35 tweets

(2023) February 28 - March 1: 27 tweets

(2023) March 1 - March 2: 27 tweets

(2023) March 2 - March 3: 38 tweets

(2023) March 3 - March 4: 48 tweets

(2023) March 4 - March 5: 31 tweets

(2023) March 5 - March 6: 26 tweets

(2023) March 6 - March 7: 45 tweets

(2023) March 7 - March 8: 39 tweets

(2023) March 8 - March 9: 55 tweets

(2023) March 9 - March 10: 39 tweets

(2023) March 10 - March 11: 41 tweets

(2023) March 11 - March 12: 32 tweets

(2023) March 12 - March 13: 39 tweets

(2023) March 13 - March 14: 37 tweets

(2023) March 14 - March 15: 43 tweets

Instructions:

Each CSV file contains a list of tweet IDs. You can use these tweet IDs to download fresh data from Twitter (read this article: hydrating tweet IDs). To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score computed by TextBlob has been appended as the second column. To hydrate the tweet IDs, you can use applications such as Hydrator (available for OS X, Windows and Linux) or twarc (python library).

Getting the CSV files of this dataset ready for hydrating the tweet IDs:

import pandas as pd

dataframe=pd.read_csv("april28_april29.csv", header=None)

dataframe=dataframe[0]

dataframe.to_csv("ready_april28_april29.csv", index=False, header=None)

The above example code takes in the original CSV file (i.e., april28_april29.csv) from this dataset and exports just the tweet ID column to a new CSV file (i.e., ready_april28_april29.csv). The newly created CSV file can now be consumed by the Hydrator application for hydrating the tweet IDs. To export the tweet ID column into a TXT file, just replace ".csv" with ".txt" in the to_csv function (last line) of the above example code.

If you are not comfortable with Python and pandas, you can upload these CSV files to your Google Drive and use Google Sheets to delete the second column. Once finished with the deletion, download the edited CSV files: File > Download > Comma-separated values (.csv, current sheet). These downloaded CSV files are now ready to be used with the Hydrator app for hydrating the tweet IDs.

Funding Agency:

Cloud Infrastructure funded by DigitalOcean.

Comments

Great Work!

Dataset Rating:

Submitted by Sadiksha sharma on Sun, 04/26/2020 - 04:14

Thanks, sadiksha!

Submitted by Rabindra Lamsal on Fri, 05/08/2020 - 02:46

Thank you very much for providing this dataset and your support

Submitted by hanaa hammad on Tue, 05/05/2020 - 09:39

My pleasure, Hanaa!

Submitted by Rabindra Lamsal on Tue, 05/05/2020 - 12:39

I created an ieee account just to download this dataset. There are numerous tweets datasets currently floating around but did not have particularly the list of tweets ids that had pin location. Thanks for your efforts.

Dataset Rating:

Submitted by Curran White on Fri, 05/08/2020 - 02:45

Thanks, Curran! I am glad that you found the dataset useful.

Submitted by Rabindra Lamsal on Fri, 05/08/2020 - 03:20

How to get tweet text and location from this csv data? Also tweet ID is not completely full, last 5 digits are only 0s

Submitted by GONGATI REDDY on Mon, 11/09/2020 - 04:23

(i) Please go through the paper associated with this dataset.
(ii) That's because you must be trying to view the CSV file using MS Excel. MS Excel does not handle digits up to 19th precision. That is the reason why you're seeing last 4 digits being replaced by zeros. Please use Google Sheets to make the CSV files ready for hydration.

Submitted by Rabindra Lamsal on Thu, 11/19/2020 - 00:03

Hello Sir,

I tried to open it in google sheet it shows only the 6 first digits and all the remaining are "0".
the importing the file in the hydrator I get the error invalid tweet ID.
Please help.

Submitted by Ismail AKPO on Sun, 12/04/2022 - 02:12

Hi, I hydrated IDS file using twarc. (https://github.com/echen102/COVID-19 TweetIDs/pull/2/commits/7d16ff3f29acf15af88c0d27424041b711865be3).

But when I tried to add the condition you used to get geolocation data, it gives me error for invalid syntax.

It would be nice if you can share which twarc code you used so that I can edit the variable names properly.

You have done great work!

Submitted by WonSeok Kim on Sat, 05/09/2020 - 15:17

Hey Kim. I think you meant using twarc (https://github.com/DocNow/twarc). That was just a pseudo-code which I had mentioned in the abstract (I've now replaced it with an excerpt of the real code to avoid confusion).

It does not matter how you are getting your JSON archived. Just make sure to add the following "if clause" in whatever way you're trying to pull the tweets. The "if clause" below will only be TRUE if the tweet contains an exact pin location.

data = json.loads(data)

if data["coordinates"]:

longitude, latitude = data["coordinates"]["coordinates"]

Now you can store the longitude and latitude values as per your convenience. I hope this helps!

Submitted by Rabindra Lamsal on Sun, 05/24/2020 - 12:53

hey i want to download full data not only id , how can i do so please give response

Submitted by charu v on Wed, 05/20/2020 - 13:46

Hello Charu. Twitter's data sharing policy does not allow anyone to share tweet information other than tweet ID and/or user ID. The list of IDs should be hydrated to re-create a full fresh tweet dataset. For this purpose, you can use applications such as DocNow's Hydrator or QCRI's Tweets Downloader.

Submitted by Rabindra Lamsal on Fri, 05/29/2020 - 22:28

Thanks for the data. I am not sure if this is just at my end but the csv files have issue with the tweet ID fields due to its 15 digit limit. The values are different from the one in json. Maybe export them to .txt files rather than .csv.

Submitted by Abhay Singh on Tue, 06/02/2020 - 21:38

Hello Abhay. Yes, I have heard from a couple of people about getting the tweet IDs fixed on their machines. That is why I am also uploading the JSON for those experiencing this issue.

Can you confirm if the IDs are fixed even when opened using some text editors (Notepad or Sublime)? I think you're opening the CSV files with MS Excel. I've seen multiple posts regarding Excel, at Stack Exchange, truncating the digits after 15.

Submitted by Rabindra Lamsal on Tue, 06/02/2020 - 22:18

Hello Rabindra,

No it doesnt happen if you open the dataset using some other editor. Reading the data in different systems (R/Python) leads to different results as may not convert it properly. Also, if someone is using hydrate app and converts the csv to txt with just the IDs then it will have errors. Anyway, its fairly straight forward to convert the json to txt containing IDs but some users may benefit with just .txt files.

Cheers

Submitted by Abhay Singh on Tue, 06/02/2020 - 22:43

Thanks for getting back.

If you use the DocNow's hydrator app you can straightway import the downloaded CSV file for the hydrating purpose (while removing the sentiment column). However, QCRI's Tweets Downloader requires a TXT file (with a single tweet ID per line). So you'll have to play around the CSV file, to some extent, for the task to be done.

I have been reached by a very handful of people having an issue similar to this. Most of them were opening the CSV files with MS Excel to remove the sentiment column. The problem was not even there when the downloaded CSV was imported as a pandas data frame and the sentiment column was dropped, and the final data frame was exported as a CSV file ready to be hydrated.

Submitted by Rabindra Lamsal on Wed, 06/03/2020 - 00:41

Thanks Rabindra. All good. As I said, its not that hard to deal with it. I mentioned it so that some one else in having a similar issue could benefit. Cheers.

Submitted by Abhay Singh on Wed, 06/03/2020 - 00:51

Roger-that.

Submitted by Rabindra Lamsal on Wed, 06/03/2020 - 11:34

I need 2000 twitter messages relevant COVID-19 for my course work. where I need to get the distribution of these tweets in world map. Can someone help me to get the twitter messages.

Submitted by Gayathri Parame... on Tue, 07/07/2020 - 01:28

[updated on August 7, 2020] Hello Gayathri. You'll have to hydrate the tweet IDs provided in this dataset to get your work done. I'd suggest you use twarc for this purpose. I am guessing you'll only need the tweet and geo-location for your work.

#import libraries

from twarc import Twarc

import sqlite3

#create a database

connection = sqlite3.connect('database.db')

c = connection.cursor()

#creating a table

def table():

try:

c.execute("CREATE TABLE IF NOT EXISTS geo_map(tweet TEXT, longitude REAL, latitude REAL)")

connection.commit()

except Exception as e:

print(str(e))

table()

#Initializing Twitter API keys

consumer_key=""

consumer_secret=""

access_token=""

access_token_secret=""

t = Twarc(consumer_key, consumer_secret, access_token, access_token_secret)

#hydrating the tweet IDs

for tweet in t.hydrate(open('ready_july5_july6.csv')):

text = tweet["full_text"]

longitude, latitude = tweet["coordinates"]["coordinates"]

c.execute("INSERT INTO geo_map (tweet, longitude, latitude) VALUES (?, ?, ?)", (text, longitude, latitude))

connection.commit()

Now you can simply make a connection to the above database-table to read its contents and plot the tweets using libraries such as Plotly. I hope this helps. Good luck!

Submitted by Rabindra Lamsal on Fri, 08/07/2020 - 07:54

If I am to filter out the tweets from the geotagged ones how can I do that? I have a tweet id dataset which has tweets from before march 20. I only want to filter the geotagged tweets from other tweets. And amazing work you have done here with the two datasets having daily files. Thanks.

Dataset Rating:

Submitted by Mohit Singh on Wed, 07/08/2020 - 10:17

Hello Mohit. Filtering geo-tagged tweets from the rest is quite straightforward if you use twarc for hydrating the tweet IDs. You'll have to add a condition to the "coordinates" Twitter object.

for tweet in t.hydrate(open('/path/to/tweet/file.csv')):

if tweet["coordinates"]:

#now you can extract whichever information you want

longitude, latitude = tweet["coordinates"]["coordinates"] #for getting geo-coordinates

You can go-through the code snippet replied to the comment thread just above this one for getting a headstart with storing the extracted information to a database.

Submitted by Rabindra Lamsal on Wed, 11/18/2020 - 23:53

Thank you for instant reply. May I ask which database you use in your project running at live.rlamsal.com.np?

Submitted by Mohit Singh on Wed, 07/08/2020 - 10:50

The project uses SQLite.

Submitted by Rabindra Lamsal on Wed, 07/08/2020 - 10:55

---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
in
1 for tweet in t.hydrate(open("id_nov8_nov9.csv")):
----> 2 "36.7783, 119.4179" == tweet["coordinates"]["coordinated"]

KeyError: 'coordinated'

Sir, Iam getting this error for this code. Could you please help me.....

Submitted by GONGATI REDDY on Wed, 11/18/2020 - 23:14

Hello Gongati. I see that you're trying to get the coordinates. The correct form would be: tweet["coordinates"]["coordinates"] for extracting coordinates info from the JSON.

Submitted by Rabindra Lamsal on Wed, 11/18/2020 - 23:58

hey, sorry if i'm being dense but i can't find the json files?

Submitted by Lucas Nakach on Thu, 07/09/2020 - 21:38

Hello Lucas. The JSON files were initially present in this dataset and were lately removed as they seemed redundant. The JSON files also included the same content that the CSV files had.

Submitted by Rabindra Lamsal on Fri, 07/10/2020 - 01:35

I have downloaded the data...what is the total number of rows in all the datasets taken togather.

Submitted by Moonis Shakeel on Sat, 07/18/2020 - 05:15

There are more than 140k tweet IDs in the dataset together.

Submitted by Rabindra Lamsal on Sat, 07/18/2020 - 12:20

It appears to be just a few thousand rows in all the datasets taken togather.

Submitted by Moonis Shakeel on Sat, 07/18/2020 - 05:24

Yes, there are 140k get-tagged tweets in this dataset. These are the tweets that have "point" location information. If you are okay with having a boundary location instead, you'll have to hydrate the tweets in this dataset (https://ieee-dataport.org/open-access/coronavirus-covid-19-tweets-dataset) and consider conditioning the ["place"] twitter object. The Coronavirus (COVID-19) Tweets Dataset has more than 310 million tweets, and I guess you'll be able to come up with a few million of tweets with the boundary condition enabled.

Submitted by Rabindra Lamsal on Sat, 07/18/2020 - 12:27

The geo tagging is from India alone?

Submitted by Moonis Shakeel on Sat, 07/18/2020 - 07:18

No. This is a global dataset.

Submitted by Rabindra Lamsal on Sat, 07/18/2020 - 12:21

Thanks. I was looking for day by day geo data.

Submitted by Somodo Non on Thu, 07/23/2020 - 02:28

Glad to be of help.

Submitted by Rabindra Lamsal on Thu, 07/23/2020 - 04:50

Thank you a lot for the dataset!

I'm trying to hydrate the tweets for 7.26 but it seems too slow since there are over 3 million tweets. Is there some faster way to hydrate them?

Submitted by Danqing Wang on Tue, 07/28/2020 - 01:33

Hello Danqing. Twitter has rate limits for its APIs. Both the hydrator app and twarc handle the rate limits and pull the JSON accordingly. If you're searching for some way to get the hydration process to expedite, I'd recommend involving some other person who has access to the Twitter Devs, and you can ask him/her to hydrate a portion of the IDs.

Submitted by Rabindra Lamsal on Tue, 07/28/2020 - 06:21

How to filter the tweets according to a particular country? For e.g India

Submitted by Trupti Kachare on Thu, 08/06/2020 - 14:39

Hello Trupti. Just to give you a headstart: If I were you, I would play around the location-specific Twitter Objects at three different levels. First, I would check if the tweet is geo-tagged (if it contains an exact location). Secondly, if the tweet is not geo-tagged, chances are that it might have a region or a country boundary box defined. Third, if none of the criteria satisfy, I would simply try to extract location information from the user's profile.

Here's an example of using twarc as a python library for this purpose.

from twarc import Twarc

consumer_key=""

consumer_secret=""

access_token=""

access_token_secret=""

t = Twarc(consumer_key, consumer_secret, access_token, access_token_secret)

for tweet in t.hydrate(open('tweet_ids.txt')):

if tweet["coordinates"]:

loc = tweet[‘‘place"]["country"] #place based on the "point" location

'''check the value in "loc" if it is from a country of your interest'''

'''however do check if tweet["place"] is of NoneType. In that condition get the long, lat from tweet["coordinates"]["coordinates"] and convert it to human readable format.

elif tweet["place"]:

loc = tweet[‘‘place"]["country"] #bounding box region

'''check the value in "loc" if it is from a country of your interest'''

else:

loc_profile = tweet["user"]["location"] #location from profile

'''check the value in "loc_profile" if it is from a country of your interest'''

However, this dataset contains the geo-tagged tweets IDs. I'd suggest you to use the Coronavirus (COVID-19) Tweets Dataset, that contains more than 386 million tweet IDs. Applying these geo specific conditions on that dataset would help you extract more tweets for your work. I hope this helps.

Submitted by Rabindra Lamsal on Thu, 08/06/2020 - 22:57

I have tried different codes, and got different errors, could you please help me..

KeyError Traceback (most recent call last)
in
1 for tweet in t.hydrate(open("ID_nov8_nov9.txt")):
2 if tweet["place"]:
----> 3 loc=tweet["place"]["USA"]

KeyError: 'USA'

for tweet in t.hydrate(open("ID_nov8_nov9.txt")):
if tweet["place"]:
"USA"== tweet["place"]["country"]
This code has no result, no error just executed.

for tweet in t.hydrate(open("ID_nov8_nov9.txt")):
if tweet["place"]:
India = tweet["place"]["country"]
This code has no result, no error just executed.

Submitted by GONGATI REDDY on Wed, 11/18/2020 - 23:22

Looks like you want to extract tweets tweeted from the United States. For that use case, you can have this simple condition in your code:

t = Twarc(consumer_key, consumer_secret, access_token, access_token_secret)
for tweet in t.hydrate(open('ids.csv')):
if tweet["place"]:
if tweet['place']['country'] == "United States":
store the tweet data

Forgive me, the comment box is not allowing me to add proper indentation in the code. I hope this helps.

Submitted by Rabindra Lamsal on Thu, 11/19/2020 - 00:16

Great work!

Which API do you use - twitter search api or twitter streaming api? Does the data includes retweet?

Submitted by antony zzr on Sat, 08/08/2020 - 11:09

Thanks, Antony. It's streaming API. Retweets have NULL geo and place objects. Therefore, retweets won't be making their way to this dataset. However, Quote tweets are included as they can have their own geo and place objects.

Submitted by Rabindra Lamsal on Sat, 08/08/2020 - 13:39

Hi, what algorithm are you using to calculate the sentiment scores, e.g. vader? Thank you!

Submitted by Molu Shi on Mon, 08/10/2020 - 09:00

Hello Molu. The TextBlob library has been used to compute the sentiment scores.

Submitted by Rabindra Lamsal on Tue, 08/11/2020 - 01:09

How do I use It?? Can you share the tweets and the sentiment label, so that I can use it in training my model

Submitted by Vaibhav Kumar on Wed, 09/16/2020 - 07:24

Please refer to my previous comments.

Submitted by Rabindra Lamsal on Tue, 09/29/2020 - 23:55

hi thank you sooo much for the tremendous work. I appreciated it very much. two quick questions: how do you know
whether those tweets are by robots. Have you applied any filtering techniques?

Second, if I would like to replicate your data collection from twitter myself, could you share your code regarding how to collect geo-tagged tweets.

Submitted by yi yang on Sat, 10/03/2020 - 21:03

Datasets

Open Access

Coronavirus (COVID-19) Geo-tagged Tweets Dataset

Abstract

Comments

Pages

Dataset Files

QUESTIONS?

Datasets

Open Access

Coronavirus (COVID-19) Geo-tagged Tweets Dataset

Abstract

Comments

Pages

Dataset Files

Related Datasets

QUESTIONS?