Good Morning Turning to Spam Morning

Gupta, Aakriti; Singh, Sunil K.; Ahuja, Kabir; Gupta, Ankit

doi:10.1007/978-981-13-8461-5_1

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…3 We also remove messages containing just URLs (no accompanying text), boilerplate content such as 'hi' or 'good morning', and, messages consisting solely of emojis, which constitute around 25% of our data. We filter such content to avoid characterising low entropy posts -such messages could fall under both on-topic and off-topic as junk [18]. As later discussed, the percentage of boilerplate messages among junk posters is 7% (12% for legitimate users).…”

Section: Message Filteringmentioning

confidence: 99%

Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp

Agarwal

Ibosiola

Sastry

et al. 2022

Proceedings of the ACM Web Conference 2022

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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. CCS CONCEPTS• Social and professional topics → Censorship; User characteristics; • Computing methodologies → Machine learning.

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Section: Message Filteringmentioning

confidence: 99%

Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp

Agarwal

Ibosiola

Sastry

et al. 2022

Proceedings of the ACM Web Conference 2022

View full text Add to dashboard Cite

show abstract

“…We also remove messages containing just URLs (no accompanying text), boilerplate content such as 'hi' or 'good morning', and, messages consisting solely of emojis, which constitute around 25% of our data. We filter such content to avoid characterising low entropy posts -although they are off-topic and widely considered as spam as well [20], it is relatively easy to implement client-side filters for specific text such as 'hi' or 'good morning', whereas filtering out the other messages which we classify as spam is a harder problem (studied in §5). Filtering these out, we are left with 766K messages.…”

Section: Message Filteringmentioning

confidence: 99%

Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp

Agarwal¹,

Ibosiola²,

Tyson³

et al. 2021

Preprint

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WhatsApp is a popular messaging app used by over a billion users around the globe. Due to this popularity, spam on WhatsApp is an important issue. Despite this, the distribution of spam via WhatsApp remains understudied by researchers, in part because of the end-toend encryption offered by the platform. This paper addresses this gap by studying spam on a dataset of 2.6 million messages sent to 5,051 public WhatsApp groups in India over 300 days. First, we characterise spam content shared within public groups and find that nearly 1 in 10 messages is spam. We observe a wide selection of topics ranging from job ads to adult content, and find that spammers post both URLs and phone numbers to promote material. Second, we inspect the nature of spammers themselves. We find that spam is often disseminated by groups of phone numbers, and that spam messages are generally shared for longer duration than non-spam messages. Finally, we devise content and activity based detection algorithms that can counter spam.

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Good Morning Turning to Spam Morning

Cited by 2 publications

References 11 publications

Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp

Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp

Jettisoning Junk Messaging in the Era of End-to-End Encryption: A Case Study of WhatsApp

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