  The writing style of a person can be affirmed as a unique identity indicator;
the words used, and the structuring of the sentences are clear measures which
can identify the author of a specific work. Stylometry and its subset -
Authorship Attribution, have a long history beginning from the 19th century,
and we can still find their use in modern times. The emergence of the Internet
has shifted the application of attribution studies towards non-standard texts
that are comparatively shorter to and different from the long texts on which
most research has been done. The aim of this paper focuses on the study of
short online texts, retrieved from messaging application called WhatsApp and
studying the distinctive features of a macaronic language (Hinglish), using
supervised learning methods and then comparing the models. Various features
such as word n-gram and character n-gram are compared via methods viz., Naive
Bayes Classifier, Support Vector Machine, Conditional Tree, and Random Forest,
to find the best discriminator for such corpora. Our results showed that SVM
attained a test accuracy of up to 95.079% while similarly, Naive Bayes attained
an accuracy of up to 94.455% for the dataset. Conditional Tree & Random Forest
failed to perform as well as expected. We also found that word unigram and
character 3-grams features were more likely to distinguish authors accurately
than other features.
