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

<em><strong>Version 101 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 (1,311,640,469 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (337,741,163 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>版本101的数据集。本数据集的同行评议论文现已发表于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COVID-19全球大流行的相关性，我们发布从Twitter数据流中采集的与新冠疫情相关推文数据集。自首次发布以来，我们通过新的合作方获取了额外数据，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。我们新增了合作方提供的1月27日至3月27日期间的数据，以扩展纵向覆盖范围。版本10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·图图巴利纳（Elena Tutubalina，KFU）慷慨提供。自版本12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。自版本14起，我们在数据集的清洁版中加入了推文标识符及其对应语言。自版本20起，我们为所有推文补充了语言与位置信息。 本数据流采集的数据涵盖所有语言，但使用占比较高的语种包括英语、西班牙语与法语。我们将所有推文与转发推文发布于full_dataset.tsv文件（含1,311,640,469条唯一推文），并提供不含转发推文的清洁版数据集，存储于full_dataset-clean.tsv文件（含337,741,163条唯一推文）。保留转发推文有诸多实际考量，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，简称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个高频二元组，以及frequent_trigrams.csv中的前1000个高频三元组。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。更多统计数据与可视化内容可访问：http://www.panacealab.org/covid19/ 更多详细信息可查阅（更新速度更快的地址为：https://github.com/thepanacealab/covid19_twitter），以及我们关于本数据集的预印本（https://arxiv.org/abs/2004.03688） 一如既往，根据Twitter的条款与条件，本处分发的内容仅为推文标识符（附带日期与时间），且仅可出于研究目的重新分发Twitter数据。需经水化（hydrate）处理后方可使用。