A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration

<em><strong>Version 157 of the dataset. FUTURE CHANGES: Due to the imminent paywalling of Twitter's API access this might be the last full update of this dataset. If the API access is not blocked, we will be stopping updates for this dataset with release 160 - a full 3 years after our initial release. It's been a joy seeing all the work that uses this resource and we are glad that so many found it useful. </strong></em> <em><strong>The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). The peer-reviewed publication for this dataset has now been published in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.</strong></em> <strong>Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 <em>we</em> have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.</strong> <strong>The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (1,390,106,295 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (359,983,412 unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/ </strong> <strong>More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688) </strong> <strong>As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used.</strong>本数据集当前版本为157版。后续更新说明：鉴于Twitter API访问即将开启付费墙机制，本数据集或迎来最后一次全量更新。若API访问未被封锁，我们将在第160版发布时停止本数据集的更新——距离本数据集首次发布已整整三年。由衷感谢各界对本数据集的使用，我们亦欣喜于众多研究者从中获益。 本数据集的文件包括full_dataset.tsv.gz与full_dataset_clean.tsv.gz，已通过Linux系统的split工具拆分为单份最大1GB的分片。请务必先合并所有分片后再进行解压操作。此前我们在上传超过2GB的大文件时遭遇了严重问题，因此不得不采用此拆分方案，这也是本次数据集发布延期的原因。本数据集的同行评议论文现已发表于《Epidemiologia》（MDPI旗下期刊），访问链接为：https://doi.org/10.3390/epidemiologia2030024。使用本数据集时，请引用该论文。 鉴于新型冠状病毒肺炎（COVID-19）全球大流行的相关背景，我们发布了从Twitter数据流中采集的新冠讨论相关推文数据集。自首次发布以来，我们通过新增合作方获取了更多数据，使本数据集得以扩充至当前规模。专项数据采集工作始于3月11日，彼时单日采集推文量超400万条。我们还新增了合作方提供的1月27日至3月27日期间的数据，以进一步提升数据集的纵向覆盖范围。第10版新增了约150万条俄语推文，采集时间为1月1日至5月8日，由Katya Artemova（国家研究型高等经济大学，NRU HSE）与Elena Tutubalina（喀山联邦大学，KFU）慷慨提供。自第12版起，我们在对应压缩包中加入了每日话题标签、@提及内容及表情符号的出现频率。自第14版起，净化版数据集新增了推文ID及其对应的语言标签。自第20版起，所有推文均附带语言与地理位置信息。 本数据集采集的数据流覆盖所有语言，但使用频率较高的语言为英语、西班牙语与法语。全量数据集文件full_dataset.tsv收录了所有原始推文与转发推文，共计1,390,106,295条唯一推文；净化版数据集文件full_dataset-clean.tsv则剔除了转发推文，共计359,983,412条唯一推文。保留转发推文出于多项实际考量，其中之一便是便于追踪重要推文及其传播路径。针对自然语言处理（Natural Language Processing，NLP）任务，我们提供了frequent_terms.csv中的前1000个高频词项、frequent_bigrams.csv中的前1000个高频二元语法组合，以及frequent_trigrams.csv中的前1000个高频三元语法组合。两份数据集的每日通用统计数据分别存储于full_dataset-statistics.tsv与full_dataset-clean-statistics.tsv文件中。如需查看更多统计结果与可视化内容，请访问：http://www.panacealab.org/covid19/ 更多细节可查阅本项目的GitHub仓库（更新频率更高：https://github.com/thepanacealab/covid19_twitter），以及本数据集的预印本论文（https://arxiv.org/abs/2004.03688）。 与此前一致，根据Twitter的服务条款，本数据集仅允许出于研究目的重新分发Twitter数据，因此本次发布的内容仅包含推文ID（附带采集日期与时间信息）。使用者需对这些ID进行推文水化处理后方可正常使用。