pandas | Read json file with list/array-like fields to Boolean columns

Here is a JSON string that contains a list of objects with each having another list embedded.

[
  {
    "name": "Alice",
    "hobbies": [
      "volleyball",
      "shopping",
      "movies"
    ]
  },
  {
    "name": "Bob",
    "hobbies": [
      "fishing",
      "movies"
    ]
  }
]

Using pandas.read_json() this turns into a DataFrame like this:

  name      hobbies
  --------------------------------------
1 Alice     [volleyball, shopping, movies]
2 Bob       [fishing, movies]

However, I would like to flatten the lists into Boolean columns like this:

  name      volleyball  shopping    movies  fishing 
  ----------------------------------------------------
1 Alice     True        True        True    False
2 Bob       False       False       True    True

I.e. when the list contains a value, the field in the corresponding column is filled with a Boolean True, otherwise with False.

I have also looked into pandas.io.json.json_normalize(), but that does not seem support this idea either. Is there any built-in way (either Python3, or pandas) to do this?

(PS. I realize that you can cook up your own code to 'normalize' the dictionary objects before loading the whole list into a DataFrame, but I might be reinventing the wheel with that and probably in a very inefficient way).