tweets

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

2 rows where display_text_range = "[21, 96]", lang = "en", source = "1f89d6a41b1505a3071169f8d0d028ba9ad6f952" and user = 33521530 sorted by lang

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created_at (date) 2 ✖

user 1 ✖

  • swyx · 2 ✖

lang 1 ✖

display_text_range 1 ✖

  • [21, 96] · 2 ✖
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
1461227322900549633 swyx 33521530 2021-11-18T06:58:36+00:00 @DennisCode @Netlify what can you share with us about what product for an ecosystem will entail?       Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [21, 96] 1461138312698097664 47547933 DennisCode       0 0 0 0 0   en  
1462918162660290560 swyx 33521530 2021-11-22T22:57:24+00:00 @calcsam @temporalio log scale, i like it we're also measuring our active clusters on log scale       Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [21, 96] 1462904683006103554 788059556 calcsam       0 0 1 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]);