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Spam and Bot Detection Across Multiple Communication Channels Using Machine Learning
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence and Machine Learning
DOI: 10.64388/ire1724844
Abstract
Digital communication platforms face a growing volume of spam, fraudulent content, and automated social media accounts, and the data available differ from one channel to another. This paper presents a multi-channel system that classifies Email and Short Message Service (SMS) messages as spam or legitimate (ham) and identifies suspicious automated accounts on Twitter (X). Email and SMS text is processed using Natural Language Processing (NLP) preprocessing and Term Frequency–Inverse Document Frequency (TF-IDF) features, which are classified by Random Forest models. Twitter accounts are analyzed separately from account metadata, namely follower count, following count, account age, and tweet volume, using a distinct Random Forest classifier. A FastAPI backend provides real-time prediction, batch processing, and input validation, and a web-based interface returns the predicted class, a confidence value, and dynamically generated explanations. The system reports an overall classification accuracy of 94.25% and an average prediction latency of 32 ms per request. The work shows how text-based spam classification and metadata-based bot detection can be integrated within a single application, and it identifies the evaluation details that remain to be documented.
Keywords
bot detection; FastAPI; machine learning; natural language processing; random forest; spam detection; TF-IDF; Twitter
References
[1] Klimt B, Yang Y. The Enron corpus: A new dataset for email classification research. In: Proc. 15th Eur. Conf. Mach. Learn. (ECML); 2004. p. 217–226. https://doi.org/10.1007/978-3-540-30115-8_22
[2] Almeida TA, Hidalgo JMG, Yamakami A. Contributions to the study of SMS spam filtering: New collection and results. In: Proc. 11th ACM Symp. Document Eng. (DocEng); 2011. p. 259–262. https://doi.org/10.1145/2034691.2034742
[3] Benevenuto F, Magno G, Rodrigues T, Almeida V. Detecting spammers on Twitter. In: Proc. 7th Annu. Collaboration, Electron. Messaging, Anti-Abuse and Spam Conf. (CEAS); 2010.
[4] Varol O, Ferrara E, Davis CA, Menczer F, Flammini A. Online human-bot interactions: Detection, estimation, and characterization. In: Proc. 11th Int. AAAI Conf. Web Social Media (ICWSM); 2017. p. 280–289. https://doi.org/10.1609/icwsm.v11i1.14871
How to cite this paper
@article{1724844,
author = {M. Thoshanth, N. Lahari, N. Pranav, M. Sanjay, Dr. Dharmendra Roy},
title = {Spam and Bot Detection Across Multiple Communication Channels Using Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {1476-1485},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724844.pdf},
abstract = {Digital communication platforms face a growing volume of spam, fraudulent content, and automated social media accounts, and the data available differ from one channel to another. This paper presents a multi-channel system that classifies Email and Short Message Service (SMS) messages as spam or legitimate (ham) and identifies suspicious automated accounts on Twitter (X). Email and SMS text is processed using Natural Language Processing (NLP) preprocessing and Term Frequency–Inverse Document Frequency (TF-IDF) features, which are classified by Random Forest models. Twitter accounts are analyzed separately from account metadata, namely follower count, following count, account age, and tweet volume, using a distinct Random Forest classifier. A FastAPI backend provides real-time prediction, batch processing, and input validation, and a web-based interface returns the predicted class, a confidence value, and dynamically generated explanations. The system reports an overall classification accuracy of 94.25% and an average prediction latency of 32 ms per request. The work shows how text-based spam classification and metadata-based bot detection can be integrated within a single application, and it identifies the evaluation details that remain to be documented.},
keywords = {bot detection; FastAPI; machine learning; natural language processing; random forest; spam detection; TF-IDF; Twitter},
month = {November},
doi = {https://doi.org/10.64388/ire1724844}
}