Twitter posting stats

This project is for gathering posting statistics from Twitter scrapes. Since those scrapes can get pretty big, pyspark is used. The data amounts are probably not big enough to merit large Spark clusters, but in principle this should scale to bigger and more machines.

Input data

The input data should be line-delimited JSON files of Tweets scraped via the Twitter API. The Tweet objects should contain these fields as a minimum in this format:

{
  "id": str,
  "text": str,
  "created_at": datetime,
  "author_id": str,
  "public_metrics": {
    "retweet_count": int,
    "reply_count": int,
    "like_count": int,
    "quote_count": int
  },
  "includes": {
    "users": [
      {
        "id": str,
        "username": str,
        "verified": bool,
        "description": str,
        "protected": bool,
        "name": str,
        "created_at": datetime,
        "public_metrics": {
          "followers_count": int
          "following_count": int
          "tweet_count": int
          "listed_count": int
        }
      }
    ]
  }
}

How to run

First, install the package:

pip install -e .

Extract data from scrapes

You can run the Spark app just with Python, e.g.:

python extract-data.py "input/examples_*.ndjson""

Be mindful of quotes if you use a glob pattern.

You can also run with spark-submit which can give more control over Spark configuration, in which case you should pass the -n (--no-local) to the Python script, e.g.:

spark-submit --master "local[32]" --driver-memory "64G" extract-data.py -n "input/examples_*.ndjson"

Write extracted data to SQLite database

You can write the extracted data (tweets and users) to a SQLite database with the scripts in database-setup, e.g.

python database-setup/tweet-tables.py "out/examples/tweets/*" twitter-example

which will then create a twitter-example.db file.

Perform analysis on tweets and users

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