# Spam Detection Using BERT

arXiv: https://arxiv.org/abs/2206.02443 · HTML: https://mlsanity.com/paper/2206.02443/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Universal Spam Detection using Transfer Learning of BERT Model](https://mlsanity.com/paper/2202.03480/) (2022)
- [Classification of Spam Emails through Hierarchical Clustering and Supervised Learning](https://mlsanity.com/paper/2005.08773/) (2020)

## Built on by (most cited first, 6 of 6)

- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)
- [Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection](https://mlsanity.com/paper/2304.01238/) (2023)
- [A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Ranking](https://mlsanity.com/paper/2210.15147/) (2022)
- [ExplainableDetector: Exploring Transformer-based Language Modeling Approach for SMS Spam Detection with Explainability Analysis](https://mlsanity.com/paper/2405.08026/) (2024)
- [A Comprehensive Analysis of Adversarial Attacks against Spam Filters](https://mlsanity.com/paper/2505.03831/) (2025)
- [Diagnosing Generalization Failures in Fine-Tuned LLMs: A Cross-Architectural Study on Phishing Detection](https://mlsanity.com/paper/2601.10524/) (2026)

## Abstract (arXiv)

Emails and SMSs are the most popular tools in today communications, and as the increase of emails and SMSs users are increase, the number of spams is also increases. Spam is any kind of unwanted, unsolicited digital communication that gets sent out in bulk, spam emails and SMSs are causing major resource wastage by unnecessarily flooding the network links. Although most spam mail originate with advertisers looking to push their products, some are much more malicious in their intent like phishing emails that aims to trick victims into giving up sensitive information like website logins or credit card information this type of cybercrime is known as phishing. To countermeasure spams, many researches and efforts are done to build spam detectors that are able to filter out messages and emails as spam or ham. In this research we build a spam detector using BERT pre-trained model that classifies emails and messages by understanding to their context, and we trained our spam detector model using multiple corpuses like SMS collection corpus, Enron corpus, SpamAssassin corpus, Ling-Spam corpus and SMS spam collection corpus, our spam detector performance was 98.62%, 97.83%, 99.13% and 99.28% respectively. Keywords: Spam Detector, BERT, Machine learning, NLP, Transformer, Enron Corpus, SpamAssassin Corpus, SMS Spam Detection Corpus, Ling-Spam Corpus.