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TweetsKB is a public RDF corpus of anonymized data for a large collection of annotated tweets. The dataset currently contains data for nearly 3.0 billion tweets, spanning more than 9 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions and URLs are exposed in RDF using established RDF/S vocabularies. For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets. For a list of the previous dataset parts, example queries and more information see the TweetsKB's home page: https://data.gesis.org/tweetskb/.

TweetsKB is a public RDF corpus of anonymized data for a large collection of annotated tweets. The dataset currently contains data for nearly 3.0 billion tweets, spanning more than 9 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions and URLs are exposed in RDF using established RDF/S vocabularies. For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets. For a list of the previous dataset parts, example queries and more information see the TweetsKB's home page: https://data.gesis.org/tweetskb/.

TweetsCOV19 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies (for the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets). More information are available through TweetsCOV19's home page: https://data.gesis.org/tweetscov19/.

We also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:

1. Tweet Id: Long.

2. Username: String. Encrypted for privacy issues.

3. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).

4. #Followers: Integer.

5. #Friends: Integer.

6. #Retweets: Integer.

7. #Favorites: Integer.

8. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".

9. Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").

10. Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".

11. Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".

12. URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"

To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords.

You can find the previous part 3 at https://doi.org/10.5281/zenodo.4593523 .