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

<em><strong>Version 43 of 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 (891,324,837 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (223,249,143 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>**数据集第43版。** 受新冠全球大流行的相关性影响，我们发布从Twitter数据流获取的与新冠相关推文数据集。自首次发布以来，我们新增了来自新合作方的数据，使本资源规模扩充至当前体量。专属数据采集工作始于3月11日，单日采集量超400万条推文。我们还补充了新合作方于1月27日至3月27日期间提供的额外数据，以拓展纵向覆盖范围。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，由卡特娅·阿尔捷莫娃（Katya Artemova，NRU HSE）与叶莲娜·图图巴利娜（Elena Tutubalina，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、提及对象、表情符号及其出现频率。自第14版起，数据集清理版中加入了推文标识符及其对应语言。自第20版起，我们为所有推文补充了语言与地理位置信息。 从数据流采集的数据涵盖所有语言，但使用频率较高的语种包括英语、西班牙语与法语。我们将全部推文与转推发布在`full_dataset.tsv`文件中（含891,324,837条唯一推文），同时提供不含转推的清理版数据集`full_dataset-clean.tsv`（含223,249,143条唯一推文）。保留转推存在多项实际意义，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，我们在`frequent_terms.csv`中提供了出现频率最高的1000个词，在`frequent_bigrams.csv`中提供了出现频率最高的1000个二元语法，在`frequent_trigrams.csv`中提供了出现频率最高的1000个三元语法。两个数据集的每日通用统计信息分别收录于`full_dataset-statistics.tsv`与`full_dataset-clean-statistics.tsv`文件。如需查看更多统计结果与可视化内容，请访问：http://www.panacealab.org/covid19/ 更多细节可查阅本数据集的相关文档，相关内容将在以下地址得到更快更新：https://github.com/thepanacealab/covid19_twitter，同时本数据集的预印本论文可通过https://arxiv.org/abs/2004.03688获取 请注意，根据Twitter的使用条款，仅可出于科研目的重新分发Twitter数据，因此本次发布的推文仅包含推文标识符（附带日期与时间信息），需进行推文水化（hydrate）操作后方可正常使用。