  WhatsApp is a popular messaging app used by over a billion users around the
globe. Due to this popularity, understanding misbehavior on WhatsApp is an
important issue. The sending of unwanted junk messages by unknown contacts via
WhatsApp remains understudied by researchers, in part because of the end-to-end
encryption offered by the platform. We address this gap by studying junk
messaging on a multilingual dataset of 2.6M messages sent to 5K public WhatsApp
groups in India. We characterise both junk content and senders. We find that
nearly 1 in 10 messages is unwanted content sent by junk senders, and a number
of unique strategies are employed to reflect challenges faced on WhatsApp,
e.g., the need to change phone numbers regularly. We finally experiment with
on-device classification to automate the detection of junk, whilst respecting
end-to-end encryption.
