Social Media Spam Detection Using NLP in Machine Learning

Abstract

Many people use social media daily to talk with friends, share their opinions, and stay updated. But one common problem is the presence of spam messages. These messages often bother users and sometimes give false or harmful information. This project helps find and stop spam using Natural Language Processing (NLP) and a method called the Naive Bayes algorithm. It uses a set of social media posts that are already marked as spam or not. The text is first cleaned by breaking it into words, removing useless words, and reducing words to their base form. Then, a method called TF-IDF changes the text into numbers so the computer can understand it better. Once the data is ready, we apply the Naive Bayes method to check whether a message is spam. To see how well the system works, we look at how often it gives correct results and where it makes mistakes. We check this using accuracy and a few other basic methods. Overall, this method works well and can identify spam messages in most situations. Such a system is valuable for social media platforms, as it helps prevent spam from spreading and affecting more users.

Introduction

Related Work

Several studies have proposed different methods for spam detection:

• Chowdhury et al. suggested using NLP techniques to identify spam on Twitter, focusing on textual features.

• Jain et al. combined Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to improve spam detection accuracy across social media platforms.

• Yurtseven et al. reviewed various approaches for detecting spam across different social media platforms, examining their strengths and limitations.

• Ghanem and Erbay focused on using deep contextualized word representations to enhance the accuracy of spam detection on social networks.

• Sharmin and Zaman explored machine learning techniques for text mining to identify spam in social media posts.ResearchGate

• Jain et al. introduced effective techniques for identifying spam by analyzing social media text.

• Al Saidat et al. provided a review of recent developments in SMS spam detection, focusing on both NLP and machine learning approaches.

• Crawford et al. investigated several machine learning methods for detecting spam in online reviews.

• AbdulNabi and Yaseen looked into the application of deep learning for email spam detection, which can also be adapted for use on social media platforms.

Proposed Methodology

The proposed system aims to detect spam content in social media posts using an NLP pipeline integrated with a Naive Bayes classifier. The methodology involves:

1. Data Collection: Gathering a labeled dataset of social media posts tagged as "spam" or "ham" (non-spam).

2. Text Preprocessing: Applying NLP techniques such as lowercasing, tokenization, stop word removal, stemming or lemmatization, and special character and URL removal to clean the text.

3. Feature Extraction: Converting the cleaned text into numerical format using the Term Frequency-Inverse Document Frequency (TF-IDF) method.

4. Classification: Applying the Naive Bayes algorithm on the extracted features to classify messages as spam or non-spam.

5. Evaluation: Assessing the model's performance using metrics such as accuracy, precision, recall, F1-score, and analyzing the confusion matrix.

6. Implementation and Optimization: Implementing the system in Python using libraries like Scikit-learn and NLTK, performing hyperparameter tuning, and comparing Naive Bayes with other classifiers like Support Vector Machine (SVM) or Logistic Regression.

Machine Learning Models

• Natural Language Processing (NLP): NLP techniques are crucial for understanding and processing human language, converting it into structured data suitable for machine learning algorithms. The system employs text cleaning, tokenization, stop-word removal, stemming or lemmatization, and vectorization techniques like TF-IDF or Count Vectorization to transform the cleaned text into numerical format.

• Naive Bayes Classifier: The Naive Bayes classifier is a probabilistic model based on Bayes' Theorem, which calculates the likelihood that a given message belongs to a specific class (spam or not spam) based on its features. It assumes that all features (i.e., words) are independent of one another, simplifying computation while still yielding strong performance for text classification tasks.

Results and Discussion

The system's performance was evaluated using various metrics:

• Accuracy: The ratio of correctly classified messages to the total number of predictions.

• Precision: Measures how reliable the model is when it classifies a message as spam.

• Recall: Measures how effectively the model captures actual spam messages.

• F1-Score: The harmonic mean of precision and recall, balancing both metrics.

• Error Rate: The percentage of total misclassifications made by the model.

The Naive Bayes model achieved high accuracy and F1-score, indicating its reliability in identifying spam messages on social media platforms.

This methodology provides an effective approach to detecting spam content in social media posts, leveraging NLP techniques and machine learning models like Naive Bayes. The system's performance metrics demonstrate its potential for real-time spam detection and moderation.

Conclusion

In this project, we presented a lightweight and efficient approach for detecting spam in social media using Natural Language Processing (NLP) techniques and the Naive Bayes classification algorithm. Through proper preprocessing steps such as tokenization, stop-word removal, stemming, and feature extraction using TF-IDF, we were able to convert unstructured social media text into structured data suitable for machine learning. The Naive Bayes classifier was chosen for its simplicity, speed, and proven effectiveness in text classification tasks, especially in handling short and informal texts like tweets and social media posts. Our experimental results demonstrated that the model performed well in terms of accuracy, precision, recall, and F1-score. The model successfully distinguished between spam and non-spam content with minimal computational requirements, making it ideal for real-time spam detection scenarios. This validates the applicability of NLP techniques combined with classical machine learning algorithms for text-based spam filtering in social media platforms. Future work will focus on enhancing the model’s adaptability and robustness by incorporating a larger and more diverse dataset from multiple platforms. We also aim to integrate advanced NLP techniques such as contextual word embeddings (e.g., BERT or Word2Vec) to improve feature representation. Additionally, implementing hybrid models that combine Naive Bayes with deep learning architectures could further improve detection accuracy while preserving interpretability. Finally, developing a user-friendly dashboard or API to deploy the model in real-time, and adding explainability features, would make the system more practical and trustworthy for use by social media moderators and developers.

References

[1] A method based on NLP for twitter spam detection R Chowdhury, KG Das, B Saha, SK Bandyopadhyay - Preprints, 2020 [2] Spam detection in social media using convolutional and long short term memory neural network G Jain, M Sharma, B Agarwal - … of Mathematics and Artificial Intelligence, 2019 [3] A review of spam detection in social media ? Yurtseven, S Bagriyanik… - 2021 6th International …, 2021 [4] Spam detection on social networks using deep contextualized word representation R Ghanem, H Erbay - Multimedia Tools and Applications, 2023 - Springer [5] Spam detection in social media employing machine learning tool for text mining S Sharmin, Z Zaman - … on signal-image technology & internet …, 2017 [6] Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques AA Akinyelu - Journal of Computer Security, 2021 [7] Spam detection on social media text G Jain, M Sharma, B Agarwal - International Journal of Computer …, 2017 [8] Advancements of SMS Spam Detection: A Comprehensive Survey of NLP and ML Techniques MR Al Saidat, SY Yerima, K Shaalan - Procedia Computer Science, 2024 [9] Survey of review spam detection using machine learning techniques M Crawford, TM Khoshgoftaar, JD Prusa, AN Richter… - Journal of Big Data, 2015 [10] Spam email detection using deep learning techniques I AbdulNabi, Q Yaseen - Procedia Computer Science, 2021.

Copyright

Copyright © 2025 P. Sharveshvar, B. Bala Ganesh, A. Madhan Babu, M. Barath Kesavan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.