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

<em><strong>Version 109 of the dataset. 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 (1,329,136,097 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (343,273,315 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109。本数据集的同行评议论文已发表于MDPI旗下期刊《Epidemiologia》，可通过以下链接获取：https://doi.org/10.3390/epidemiologia2030024。使用本数据集时请引用该论文。鉴于新冠疫情（COVID-19）在全球范围内的关联性影响，我们发布从推特数据流（Twitter Stream）中采集的、与新冠相关讨论推文组成的数据集。自首次发布以来，我们从新的合作方处获得了额外数据，使本数据集规模扩充至当前体量。专属数据采集工作始于3月11日，单日采集推文量超400万条。我们新增了合作方于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 (NRU HSE)与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（hashtags）、提及（mentions）及表情符号（emojis）及其出现频率。自第14版起，我们在数据集清理版中加入了推文标识符（tweet identifiers）及其对应语言信息。自第20版起，我们为所有推文补充了语言及地点信息。本数据流采集的数据覆盖所有语言，但占比更高的语种为英语、西班牙语与法语。我们将所有推文与转推（retweet）数据发布于full_dataset.tsv文件（含1,329,136,097条唯一推文），同时发布不含转推的清理版数据集，存储于full_dataset-clean.tsv文件（含343,273,315条唯一推文）。我们保留转推数据有多重实际考量，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（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个二元语法（bigram）以及frequent_trigrams.csv中的前1000个三元语法（trigram）。两类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及可视化内容，请访问：http://www.panacealab.org/covid19/。更多细节可于以下链接查阅（更新频率更高）：https://github.com/thepanacealab/covid19_twitter，同时本数据集的预印本可通过https://arxiv.org/abs/2004.03688获取。与此前一致，根据推特的服务条款，本数据集仅可用于研究目的，因此此处仅发布推文标识符（附带日期与时间信息）。使用前需对其进行水化（hydrated）处理以获取完整推文内容。