Spam Filter 101: Don't Let Junk Texts 'Message' Up Your Day

Sunday, October 26, 2008

Updated: October 27, 2019
The original post has been migrated from a sunset blog host (http://grace.umd.edu/~mmallari/), ported from XLMiner to R, Python, and Julia, and refreshed with newer datasets and references.

Text messaging is the lifeline of communication—connecting millions every day. For a fictional telecom giant, ATexTel Communications (ATxT), Short Message Service (SMS) isn’t just a service—it’s their competitive edge. Customers valued ATxT for delivering messages swiftly and reliably. But the rapid growth of SMS didn’t come without a cost. As text messaging soared, so did the rise of spam—threatening to undermine the very trust ATxT had worked so hard to build.

The Problem No One Saw Coming: Spam Floods and Customer Complaints

The deluge of spam messages posed challenges on multiple fronts. Customers were inundated with fraudulent offers, phishing attempts, and irrelevant promotions—tarnishing their experience. ATxT faced mounting pressure from three directions:

Trust Issues: Customers began doubting ATxT’s ability to safeguard their communication.
Legal Risks: Emerging regulations demanded stricter anti-spam measures, with noncompliance threatening steep fines.
Operational Costs: Handling spam-related complaints drained resources and hindered focus on innovation.

ATxT’s customer satisfaction metrics nosedived, and competitors were circling like vultures—ready to pounce on dissatisfied customers.

The High Stakes of Inaction: Why ATxT Had to Act

Failing to address the spam issue wasn’t just a minor inconvenience—it was an existential threat. Without immediate action:

Customer Attrition: Dissatisfied users would jump ship to competitors who promised better security.
Regulatory Blowback: Noncompliance with anti-spam laws could lead to crippling fines and lawsuits.
Brand Damage: Public perception of ATxT as a secure and reliable provider would disintegrate.
Inefficiency Overload: Resources spent on addressing customer complaints would balloon—leaving the company paralyzed.

It became clear to ATxT’s leadership: they couldn’t afford to let the problem linger.

How ATxT Fought Back: The Science of Stopping Spam

To restore customer trust and tame the spam crisis, ATxT turned to machine learning (ML). The team’s goal was to design a scalable, automated spam detection system—and Naïve Bayes emerged as the algorithm of choice. Why?

Simplicity Meets Power: Naïve Bayes, though simple, excelled in text classification tasks—making it ideal for analyzing SMS content.
Scalability: With billions of messages to process, ATxT needed an algorithm that could handle large-scale data efficiently.
Transparency: Probabilistic models like Naïve Bayes provided interpretable outputs—making it easier to explain to stakeholders and regulators.

Armed with a historical dataset of labeled messages, ATxT’s data scientists preprocessed SMS content, converted text into numerical representations (e.g., TF-IDF), and trained the Naïve Bayes model to classify messages as spam or legitimate.

Results That Spoke Volumes: Winning Back Customers and Regaining Control

The results of the project were transformational:

Spam Reduction: The Naïve Bayes classifier achieved high accuracy in filtering spam—reducing customer complaints by over 80%.
Customer Retention: Improved service quality restored user trust—stemming the tide of churn.
Regulatory Compliance: TexTel stayed ahead of anti-spam regulations—avoiding fines and lawsuits.
Operational Efficiency: Automating spam detection freed up resources—enabling the team to focus on innovation.

This wasn’t just a technological win—it was a reaffirmation of ATxT’s commitment to its customers.

Conclusion: Learning from ATxT’s Spam Victory

ATxT’s journey from crisis to resolution highlights the power of combining ML with strategic foresight. By deploying Naïve Bayes to tackle spam, the company didn’t just solve an immediate problem; it set a new benchmark for trust and innovation in the telecom industry.

For businesses facing similar challenges, ATxT’s story is a reminder: the right tools, combined with a customer-first mindset, can turn obstacles into opportunities. Don’t let spam mess up your day—take control, and let your data do the talking.

Appendix A: Environment, Reproducibility, and Coding Style

If you are interested in reproducing this work, here are the versions of R, Python, and Julia that I used. Additionally, my coding style here is verbose, in order to trace back where functions/methods and variables are originating from, and make this a learning experience for everyone—including me.

cat(
    R.version$version.string, "-", R.version$nickname,
    "\nOS:", Sys.info()["sysname"], R.version$platform,
    "\nCPU:", benchmarkme::get_cpu()$no_of_cores, "x", benchmarkme::get_cpu()$model_name
)

Registered S3 methods overwritten by 'tibble':
  method     from  
  format.tbl pillar
  print.tbl  pillar

R version 3.6.0 (2019-04-26) - Planting of a Tree 
OS: Darwin x86_64-apple-darwin15.6.0 
CPU: 4 x Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz

Python

import sys
import platform
import os
import cpuinfo
print(
    "Python", sys.version,
    "\nOS:", platform.system(), platform.platform(),
    "\nCPU:", os.cpu_count(), "x", cpuinfo.get_cpu_info()["brand"]
)

Python 3.7.0 (v3.7.0:1bf9cc5093, Jun 26 2018, 20:42:06) 
[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)] 
OS: Darwin Darwin-19.6.0-x86_64-i386-64bit 
CPU: 4 x Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz

Julia

using InteractiveUtils
InteractiveUtils.versioninfo()

Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin18.6.0)
  CPU: Intel(R) Core(TM) i5-3210M CPU @ 2.50GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-6.0.1 (ORCJIT, ivybridge)

Appendix B: Data Understanding

str(sms)

'data.frame':	5572 obs. of  2 variables:
 $ label  : Factor w/ 2 levels "ham","spam": 1 1 2 1 1 2 1 1 2 2 ...
 $ message: chr  "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..." "Ok lar... Joking wif u oni..." "Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C "U dun say so early hor... U c already then say..." ...

Spam Filter 101: Don't Let Junk Texts 'Message' Up Your Day

The Problem No One Saw Coming: Spam Floods and Customer Complaints

The High Stakes of Inaction: Why ATxT Had to Act

How ATxT Fought Back: The Science of Stopping Spam

Results That Spoke Volumes: Winning Back Customers and Regaining Control

Conclusion: Learning from ATxT’s Spam Victory

Appendix A: Environment, Reproducibility, and Coding Style

Appendix B: Data Understanding

Appendix C: Data Preparation

Appendix D: Modeling

Appendix E: Evaluation

Appending F: Deployment

Further Readings