tweets

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

1 row where quoted_status = 1231455889019699200 sorted by lang

✎ View and edit SQL

This data as json, CSV (advanced)

user 1 ✖

created_at (date) 1 ✖

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
1424588758746161155 swyx 33521530 2021-08-09T04:30:02+00:00 @dhaiwat10 @supabase it helps but surely isn't the only factor. Stripe is well regarded as a bigger co that is still shipping fast. but on a small team, there is less opportunity for a HIPPO to constantly retard momentum with Apple Pie Positions https://twitter.com/shreyas/status/1231455889019699200   1231455889019699200 1231455889019699200   Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [21, 271] 1424587828982206468 1025757442594889734 dhaiwat10       1 0 7 0 0 0 en  

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

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]);