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Dataset Open Access

A Twitter Dataset of 100+ million tweets related to COVID-19

Banda, Juan M.; Tekumalla, Ramya; Wang, Guanyu; Yu, Jingyuan; Liu, Tuo; Ding, Yuning; Chowell, Gerardo


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  <identifier identifierType="DOI">10.5281/zenodo.3735274</identifier>
  <creators>
    <creator>
      <creatorName>Banda, Juan M.</creatorName>
      <givenName>Juan M.</givenName>
      <familyName>Banda</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8499-824X</nameIdentifier>
      <affiliation>Georgia State University</affiliation>
    </creator>
    <creator>
      <creatorName>Tekumalla, Ramya</creatorName>
      <givenName>Ramya</givenName>
      <familyName>Tekumalla</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1606-4856</nameIdentifier>
      <affiliation>Georgia State University</affiliation>
    </creator>
    <creator>
      <creatorName>Wang, Guanyu</creatorName>
      <givenName>Guanyu</givenName>
      <familyName>Wang</familyName>
      <affiliation>University of Missouri</affiliation>
    </creator>
    <creator>
      <creatorName>Yu, Jingyuan</creatorName>
      <givenName>Jingyuan</givenName>
      <familyName>Yu</familyName>
      <affiliation>Universitat Autònoma de Barcelona</affiliation>
    </creator>
    <creator>
      <creatorName>Liu, Tuo</creatorName>
      <givenName>Tuo</givenName>
      <familyName>Liu</familyName>
      <affiliation>Carl von Ossietzky Universität Oldenburg</affiliation>
    </creator>
    <creator>
      <creatorName>Ding, Yuning</creatorName>
      <givenName>Yuning</givenName>
      <familyName>Ding</familyName>
      <affiliation>Universität Duisburg-Essen</affiliation>
    </creator>
    <creator>
      <creatorName>Chowell, Gerardo</creatorName>
      <givenName>Gerardo</givenName>
      <familyName>Chowell</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2194-2251</nameIdentifier>
      <affiliation>Georgia State University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Twitter Dataset of 100+ million tweets related to COVID-19</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>social media</subject>
    <subject>twitter</subject>
    <subject>nlp</subject>
    <subject>covid-19</subject>
    <subject>covid19</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-03-31</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3735274</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3723939</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/biohackathon</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/covid-19</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/zenodo</relatedIdentifier>
  </relatedIdentifiers>
  <version>3.0</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;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. The first 9 weeks of data (from January 1st, 2020 to March 11th, 2020) contain very low tweet counts as we filtered other data we were collecting for other research purposes, however, one can see the dramatic increase as the awareness for the virus spread. Dedicated data gathering started from March 11th to March 30th&amp;nbsp;which yielded over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to February 27th, to provide extra longitudinal coverage.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The data collected from the stream captures all languages, but the higher prevalence are:&amp;nbsp; English, Spanish, and French. We release all tweets and retweets on the full_dataset.tsv file (101,400,452 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (20,244,746 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 statistics-full_dataset.tsv and statistics-full_dataset-clean.tsv files.&amp;nbsp;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;More details can be found (and will be updated faster at: &lt;a href="https://github.com/thepanacealab/covid19_twitter"&gt;https://github.com/thepanacealab/covid19_twitter&lt;/a&gt;)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;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. The need to be hydrated to be used. &lt;/strong&gt;&lt;/p&gt;</description>
    <description descriptionType="Other">This dataset will be updated bi-weekly at least with additional tweets, look at the github repo for these updates</description>
  </descriptions>
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