spectrum, we significantly expand the task to twelve diverse identities with varying levels of representation and ideological overlap while maintaining classification accuracy.

In Table \[pol\_mat\] we report the matrix of highest weighted words for separating users in each pairwise class comparison. As with race, belief in star sign, and religion, we plan on making expanded pairwise lists available online. In Table \[cm\_pol\] we report the confusion matrix. Note that many errors are between similar labels, such as liberal and democrat. Ease of training, strong performance, and representation of minority classes make a majority vote system of shallow pairwise classifiers a good approach for this task.

For binary comparison, by pooling {‘very liberal’,‘liberal’,‘democrat’} and {‘very conservative’,‘conservative’,‘republican’} we achieve 76.4% accuracy; 12.1% above baseline. Table \[words\_rel\_pol\] shows the top 55 liberal and conservative words.

### Religion

Religion seems to be more difficult to glean from statuses than political identity. At 54.1%, accuracy is a modest 5.3% above guessing the mode. The most highly weighted pairwise words are on Table \[mat\_rel\], and Table \[cm\_rel\] shows the confusion matrix. The most highly weighted word to distinguish someone who is agnostic from an atheist is ‘boyfriend’. This led us to look deeper at that pairwise 