Determining completeness of dataset
If you are talking about exploring your data for patterns of missing data you can try using Self-Organizing Maps [https://en.wikipedia.org/wiki/Self-organizing_map], which are a special flavor of neural networks. Here is a small research paper explaining the concept a bit. Here is another [link][3] with historical info and links to some original papers about the topic, specifically by Kohonen et al. 

From Wikipedia: 


  Self-organizing maps are different from other artificial neural
  networks as they apply competitive learning as opposed to
  error-correction learning (such as backpropagation with gradient
  descent), and in the sense that they use a neighborhood function to
  preserve the topological properties of the input space. This makes
  SOMs useful for visualizing low-dimensional views of high-dimensional
  data...


Essentially this method will allow you to look for patterns in your data. From there you can determine what level of completeness you are dealing with in your data set.

Specifically for your example for Twitter data, I would imagine there are many fields in the JSON data with missing values. Maybe some users choose not to fill in their gender or age etc. Visualizing your data as well calculating summary statistics will help you to paint an overall picture of your data. And when you have high dimensional data using tools to visualize it in a lower dimensional space can always be handy. Hope this helps you!