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

<em><strong>Version 21 of the dataset, we have refactored the full_dataset.tsv and full_dataset_clean.tsv files (since version 20) to include two additional columns: language and place country code (when available). This change now includes language and country code for ALL the tweets in the dataset, not only clean tweets. With this change we have removed the clean_place_country.tar.gz and clean_languages.tar.gz files. With our refactoring of the dataset generating code we also found a small bug that made some of the retweets not be counted properly, hence the extra increase on tweets available. </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 (584,500,464 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (137,690,130 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>本数据集第21版中，我们自第20版起对"full_dataset.tsv"与"full_dataset_clean.tsv"文件进行重构，新增**语言（language）**与**所属国家代码（place country code，若可获取）**两列。此次更新为数据集中**所有推文**（而非仅清洗后的推文）都添加了语言与国家代码信息。同时，我们已移除"clean_place_country.tar.gz"与"clean_languages.tar.gz"两个文件。在重构数据集生成代码的过程中，我们还修复了一处导致部分转推（retweet）计数不准确的小漏洞，这也是本次可用推文数量新增的原因。 鉴于COVID-19全球大流行的重要意义，我们发布了从Twitter数据流中采集的、与COVID-19相关讨论的推文数据集。自首次发布以来，我们新增了来自新合作方的额外数据，使本资源规模扩充至当前体量。专项数据采集工作自3月11日启动，单日可采集超400万条推文。我们还补充了新合作方提供的1月27日至3月27日期间的数据，以增强数据集的纵向覆盖度。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，我们新增了每日话题标签（hashtag）、提及（mention）与表情符号（emoji）及其频率信息，并打包为对应压缩文件。自第14版起，我们为数据集的清洗版本新增了推文标识符（tweet identifier）及其对应语言信息。自第20版起，我们为所有推文新增了语言与地点位置信息。 本数据流采集的数据涵盖所有语言，但使用频次较高的语种包括英语、西班牙语与法语。我们在"full_dataset.tsv"文件中发布所有推文与转推（retweet），总计584,500,464条唯一推文；并在"full_dataset-clean.tsv"文件中发布不含转推的清洗版本，总计137,690,130条唯一推文。保留转推有诸多实际意义，例如追踪重要推文及其传播路径便是其中之一。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、"frequent_bigrams.csv"中的前1000个高频二元语法（bigram）以及"frequent_trigrams.csv"中的前1000个高频三元语法（trigram）。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。如需查看更多统计数据与可视化内容，请访问：http://www.panacealab.org/covid19/ 更多细节可查阅（且更新速度更快的地址为：https://github.com/thepanacealab/covid19_twitter），以及本数据集的预印本论文（https://arxiv.org/abs/2004.03688） 一如既往，根据Twitter的条款与条件，本处发布的推文仅包含推文标识符（tweet identifier，附带日期与时间信息），仅可用于研究目的进行二次分发。使用者需对其进行水化处理后方可使用。