A Hybrid Approach for Efficient Spam Detection on X (Formerly Twitter): Leveraging Artificial Neural Networks and Fuzzy Decision Trees | Chapter 1 | Science and Technology: Developments and Applications Vol. 9

These days, there are numerous online social media platforms that connect people, such as Instagram, X (formerly Twitter), and Facebook. The vast amount of user-generated content on X has made it a leading social media platform, where users can interact, share updates, and meet new friends. To combat spam, X employs Google Safe Browsing, which detects and blocks spam URLs. However, the platform attracts various types of spammers due to its sophisticated API that allows users to read and publish data. Many previous studies have explored different machine learning algorithms to identify spam on X. Unfortunately, these methods have not been thoroughly tested and often prove inaccurate when applied to large datasets. To address these issues, this study proposes a hybrid approach was proposed in this study by integrating Artificial Neural Networks (ANNs) with Fuzzy Decision Trees to address these problems. The proposed classifier effectively distinguishes between spam and non-spam tweets based on their labels. This work introduces a novel solution by combining a deep learning method with a decision tree classifier. For testing, a large dataset comprising 600 million public tweets was utilized. To evaluate the performance of the proposed algorithm, metrics such as accuracy, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR) were employed. The results demonstrate that the proposed strategy is both reliable and effective.

 

Author (s) Details

M. Arunkrishna
PG & Research Department of Computer Science, Christhu Raj College (Affiliated to Bharathidhasan University), Tiruchirappalli, Tamil Nadu, India.

 

B. Senthilkumaran
PG & Research Department of Computer Science, Christhu Raj College (Affiliated to Bharathidhasan University), Tiruchirappalli, Tamil Nadu, India.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v9/4809

A Comparative Study of Naive Bayes and Enhanced Random Forest Algorithms in Spam Detection | Chapter 4 | Science and Technology: Developments and Applications Vol. 3

Online Social Networks (OSNs) or simply social Media such as Facebook, X (Formerly known as Twitter), Instagram have recently emerged as one of the crucial platforms in human communication worldwide that allows individual users to send messages, build friendships, share perceptions and voice out their opinions, and get inspired. In this era of technology, online social media has become a rapidly growing phenomenon. The main social media platforms such as Instagram, Facebook, and X (formerly known as Twitter) connect and unite people globally, as quickly as any other communication medium. The growth of social media is expected to increase tremendously in the future. Online social media users generate and consume information independently. Many domains recognize the vital role of analyzing social media data, as this can improve operations and enable organizations to stay competitive. Nowadays, people spend a significant amount of time on social media platforms. However, the growing popularity of social media has also led to an increase in spamming and hacking activities. Cyber-criminals often spam and hack through external phishing sites or malware downloads, which pose significant security risks and poor user experience in social media networks. To combat the issue of spam, several methods have been proposed, but there is still no perfect, effective solution for detecting spam with high accuracy. In this chapter, we propose a spam detection approach using Naive Bayes (NB) and Enhanced Random Forest (ERF) classifiers. The Naive Bayes classifier applies Bayes' theorem with feature independence assumptions, while the Enhanced Random Forest improves on the traditional Random Forest with optimized feature handling. In order to assess the effectiveness of the proposed model, metrics such as accuracy, precision, recall and F1 scores are used to compare the model’s performance in recognizing spam messages. In conclusion, this study emphasizes that Enhanced Random Forest is more balanced in terms of efficiency and performance for social network spam detection and it can perform well with lower computational requirements.

 

Author (s) Details

 

M. Arunkrishna
PG & Research Department of Computer Science, Christhu Raj College (Affiliated to Bharathidhasan University), Tiruchirappalli - 620 012, Tamil Nadu, India.

 

B. Mukunthan
Department of Computer Science, Jairams Arts and Science College (Affiliated to Bharathidhasan University), Karur - 639003, Tamil Nadu, India.

B. Senthilkumaran
PG & Research Department of Computer Science, Christhu Raj College (Affiliated to Bharathidhasan University), Tiruchirappalli - 620 012, Tamil Nadu, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/stda/v3/3442

Using a Content-Based Filtering Approach, Advanced Study on Spam Detection and Spammer Behavior Analysis on Twitter | Chapter 10 | Advanced Aspects of Engineering Research Vol. 16

 Because Twitter is one of the most widely used social media platforms, it is prone to abuse. Spamming is one of the ways that people abuse Twitter. Spam becomes a problem when a communication medium, particularly one that allows worldwide connection and handles large amounts of online data, becomes a problem. Because Twitter is so widely used, it makes it easier for spammers to thrive. Spammers are those who send unsolicited messages to others in order to either market a product or persuade them to click on dangerous links that may harm their computer systems. The primary goal of these spammers is to gain money off of their victims. Several systems have been developed in recent years with the goal of assessing whether or not a user is a spammer. However, these systems are unable to filter every spam message, and a spammer can create a new account and utilise it to send further messages. The content-based technique proposed in this paper can be used to filter spam tweets. To filter out unwanted tweets, the method involves combining tweets with machine learning and compression methods. The technology will continue to advance, assisting in the removal of spammers and the improvement of Twitter space. Spam detection has become an important aspect of Twitter's security strategy for protecting users from cyber criminals and other unwanted actors.

Author (S) Details

B. Mukunthan
Department of Computer Science, Sri Ramakrishna College of Arts and Science (Autonomous), Nava India, Coimbatore-641021, Tamil Nadu, India.

G. Rakesh
Department of Computer Science, Thiagarajar College, Madurai-625009, Tamil Nadu, India.

View Book :- https://stm.bookpi.org/AAER-V16/article/view/1995