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

<em><strong>Version 22 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 (602,921,788</strong><strong> unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (142,360,288 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>本数据集版本为22。自版本20起，我们已对full_dataset.tsv与full_dataset_clean.tsv文件进行重构，新增两个字段：语言字段以及可用时的所在国家代码字段。此次更新将为数据集中所有推文（而非仅清洗后的推文）补充语言与国家代码信息。同时，我们已移除了clean_place_country.tar.gz与clean_languages.tar.gz两个压缩包文件。在重构数据集生成代码的过程中，我们还修复了一处会导致部分转推推文统计计数错误的小漏洞，这也是本次可用推文数量新增的原因之一。鉴于新冠疫情（COVID-19）全球大流行的重要性，我们发布了从Twitter数据流（Twitter Stream）中采集的、与新冠相关话题推文数据集。自首次发布以来，我们通过新增合作方获得了更多数据，使本资源规模达到当前体量。专项数据采集工作始于3月11日，当日采集量突破400万条推文。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，以拓展纵向覆盖范围。版本10新增了约150万条俄语推文，采集时段为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·图图巴利纳（Elena Tutubalina，KFU）友好提供。自版本12起，我们新增了每日话题标签、提及对象与表情符号（emoji）及其频率的对应压缩包文件。自版本14起，我们为清洗后的数据集补充了推文标识符及其对应语言信息。自版本20起，我们为所有推文补充了语言与所在位置信息。本数据流采集覆盖所有语言，但使用频率较高的语种为英语、西班牙语与法语。我们将所有推文与转推推文存储于full_dataset.tsv文件中（含602,921,788条唯一推文），同时提供不含转推推文的清洗版本，存储于full_dataset-clean.tsv文件中（含142,360,288条唯一推文）。保留转推推文有诸多实际意义，其中之一便是追踪重要推文及其传播路径。针对自然语言处理（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，以及frequent_trigrams.csv中的前1000个高频三元语法。两个数据集的每日通用统计信息分别存储于full_dataset-statistics.tsv与full_dataset-clean-statistics.tsv文件中。如需查看更多统计数据与可视化内容，请访问：http://www.panacealab.org/covid19/。更多细节可查阅（并将在https://github.com/thepanacealab/covid19_twitter 处更快更新），以及本数据集的预印本论文（https://arxiv.org/abs/2004.03688）。与往常一致，由于推特的服务条款仅允许出于科研目的重新分发推特数据，本处分发的仅为推文标识符（附带日期与时间信息），需对其进行推文水化（hydrate）后方可使用。