Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp
This addresses the issue of unwanted messages for WhatsApp users, but it is incremental as it applies existing methods to a new dataset.
The study tackled the problem of junk messaging on WhatsApp by analyzing 2.6 million messages in public groups in India, finding that nearly 10% of messages were unwanted content sent by junk senders who use strategies like changing phone numbers.
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.