tweets

This is data scraped from swyx's timeline! See blog post

1 row where "created_at" is on date 2021-08-25, quoted_status = 1128284234928418816 and retweet_count = 0

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id ▼ user created_at full_text retweeted_status quoted_status place source truncated display_text_range in_reply_to_status_id in_reply_to_user_id in_reply_to_screen_name geo coordinates contributors is_quote_status retweet_count favorite_count favorited retweeted possibly_sensitive lang scopes
1430608769319198720 swyx 33521530 2021-08-25T19:11:24+00:00 @mwarger @FernandoTheRojo @jamonholmgren yup! gqlless was inspired by babel blade. i think the static analysis approach could probably be built into relay/apollo if people cared enough, but ultimately my attention shifted to GraphQL schema creation rather than consumption https://twitter.com/graphqlasia/status/1128284234928418816   1128284234928418816 1128284234928418816   Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [41, 297] 1430602199801999364 15886909 mwarger       1 0 2 0 0 0 en  

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CREATE TABLE [tweets] (
   [id] INTEGER PRIMARY KEY,
   [user] INTEGER REFERENCES [users]([id]),
   [created_at] TEXT,
   [full_text] TEXT,
   [retweeted_status] INTEGER,
   [quoted_status] INTEGER,
   [place] TEXT REFERENCES [places]([id]),
   [source] TEXT REFERENCES [sources]([id]), [truncated] INTEGER, [display_text_range] TEXT, [in_reply_to_status_id] INTEGER, [in_reply_to_user_id] INTEGER, [in_reply_to_screen_name] TEXT, [geo] TEXT, [coordinates] TEXT, [contributors] TEXT, [is_quote_status] INTEGER, [retweet_count] INTEGER, [favorite_count] INTEGER, [favorited] INTEGER, [retweeted] INTEGER, [possibly_sensitive] INTEGER, [lang] TEXT, [scopes] TEXT,
   FOREIGN KEY([retweeted_status]) REFERENCES [tweets]([id]),
   FOREIGN KEY([quoted_status]) REFERENCES [tweets]([id])
);
CREATE INDEX [idx_tweets_source]
    ON [tweets] ([source]);