  We explore the understudied area of social payments to evaluate whether or
not we can predict the gender and political affiliation of Venmo users based on
the content of their Venmo transactions. Latent attribute detection has been
successfully applied in the domain of studying social media. However, there
remains a dearth of previous work using data other than Twitter. There is also
a continued need for studies which explore mobile payments spaces like Venmo,
which remain understudied due to the lack of data access. We hypothesize that
using methods similar to latent attribute analysis with Twitter data, machine
learning algorithms will be able to predict gender and political affiliation of
Venmo users with a moderate degree of accuracy. We collected crowdsourced
training data that correlates participants' political views with their public
Venmo transaction history through the paid Prolific service. Additionally, we
collected 21 million public Venmo transactions from recently active users to
use for gender classification. We then ran the collected data through a TF-IDF
vectorizer and used that to train a support vector machine (SVM). After
hyperparameter training and additional feature engineering, we were able to
predict user's gender with a high level of accuracy (.91) and had modest
success predicting user's political orientation (.63).
