Dataset Open Access
TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 3, June 2020 - December 2020)
Baran, Erdal; Dimitrov, Dimitar
DCAT Export
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<dct:title>TweetsCOV19 - A Semantically Annotated Corpus of Tweets About the COVID-19 Pandemic (Part 3, June 2020 - December 2020)</dct:title>
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<dcat:keyword>twitter</dcat:keyword>
<dcat:keyword>tweets</dcat:keyword>
<dcat:keyword>linked data</dcat:keyword>
<dcat:keyword>microblogging</dcat:keyword>
<dcat:keyword>RDF</dcat:keyword>
<dcat:keyword>csv</dcat:keyword>
<dcat:keyword>covid-19</dcat:keyword>
<dcat:keyword>coronavirus</dcat:keyword>
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<dct:description><p><strong><a href="https://data.gesis.org/tweetscov19/">TweetsCOV19</a></strong><strong> </strong>is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of <a href="https://data.gesis.org/tweetskb">TweetsKB</a> and aims at capturing online discourse about various aspects of the pandemic and its societal impact. <strong>Metadata</strong> information about the tweets as well as extracted <strong>entities</strong>, <strong>sentiments</strong>, <strong>hashtags</strong>, <strong>user mentions</strong>, and <strong>resolved URLs </strong>are exposed in RDF using established RDF/S vocabularies*.</p> <p>We also provide a <em><strong>tab-separated values (tsv)</strong></em> version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character (&quot;\t&quot;). The following list indicate the feature indices:</p> <ol> <li>Tweet Id: Long.</li> <li>Username: String. Encrypted for privacy issues*.</li> <li>Timestamp: Format ( &quot;EEE MMM dd HH:mm:ss Z yyyy&quot; ).</li> <li>#Followers: Integer.</li> <li>#Friends: Integer.</li> <li>#Retweets: Integer.</li> <li>#Favorites: Integer.</li> <li>Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from <a href="https://github.com/yahoo/FEL">FEL</a> library. Each entity is separated from another entity by char &quot;;&quot;. Also, each entity is separated by char &quot;:&quot; in order to store &quot;original_text:annotated_entity:score;&quot;. If FEL did not find any entities, we have stored &quot;null;&quot;.</li> <li>Sentiment: String. <a href="http://sentistrength.wlv.ac.uk/">SentiStrength</a> produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char &quot; &quot;. Positive sentiment was stored first and then negative sentiment (i.e. &quot;2 -1&quot;).</li> <li>Mentions: String. If the tweet contains mentions, we remove the char &quot;@&quot; and concatenate the mentions with whitespace char &quot; &quot;. If no mentions appear, we have stored &quot;null;&quot;.</li> <li>Hashtags: String. If the tweet contains hashtags, we remove the char &quot;#&quot; and concatenate the hashtags with whitespace char &quot; &quot;. If no hashtags appear, we have stored &quot;null;&quot;.</li> <li>URLs: String: If the tweet contains URLs, we concatenate the URLs using &quot;:-: &quot;. If no URLs appear, we have stored &quot;null;&quot;</li> </ol> <p>To extract the dataset from <a href="https://data.gesis.org/tweetskb">TweetsKB</a>, we compiled a seed list of 268 COVID-19-related <a href="https://data.gesis.org/tweetscov19/keywords_v1.1.txt">keywords</a>.</p> <p><em>* For the sake of privacy, we anonymize&nbsp;user IDs&nbsp;and we do not provide the text of the tweets.</em></p></dct:description>
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1,237
323
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 1,237 | 1,237 |
| Downloads | 323 | 323 |
| Data volume | 430.2 GB | 430.2 GB |
| Unique views | 1,168 | 1,168 |
| Unique downloads | 252 | 252 |
Indexed in
- Publication date:
- March 10, 2021
- DOI:
-
- Keyword(s):
- twitter tweets linked data microblogging RDF csv covid-19 coronavirus
- Related identifiers:
- Documented by
- https://data.gesis.org/tweetscov19/ (Dataset)
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International