Probably the only interesting part of that study to me is how they are measuring “erratic” which is using a measure they’ve called “novelty”. Its in appendix A1:

A.1 Embedding and Novelty Measurement

To quantify content novelty, we first convert the text of each post into a high-dimensional vector representation (embedding). This process begins by cleaning the raw post content (e.g., stripping HTML tags) and feeding the text into a pre-trained SentenceTransformer model, specifically all-MiniLM-L6-v2. This model maps each post to a 384-dimensional vector. From the full corpus of N posts, we obtain a matrix of “raw" embeddings.

These raw embeddings are known to suffer from anisotropy (a non-uniform distribution in the vector space), which can make distance metrics unreliable [li2020sentence]. To correct this, we apply a standard decorrelation step. We fit a Principal Component Analysis model with whitening to the entire matrix 𝐄_raw. This transformation de-correlates the features and scales them to have unit variance, yielding a matrix of ‘whitened’ embeddings, 𝐄_white [su2021whitening]. These whitened vectors are used for all novelty calculations.

There is a decent primer on the transformer here:

https://medium.com/@rahultiwari065/unlocking-the-power-of-sentence-embeddings-with-all-minilm-l6-v2-7d6589a5f0aa

I’m not sure of a great primer on PCA, it kind of finds the dominant directions of a set of vectors.

With that novelty measurement the eracticness seems to be averaging over a window (seven day) and then measuring euclidean distance.

I did have a pint just before reading and writing this so there’s probably some mistakes here

That sounds alot like someone who “doesn’t care” if the epstein files get released.

Gotta try to divert attention! Throw anything out there and see what sticks!