Unmasking Fake Social Network Accounts with Explainable Intelligence

The recent global social network platforms have intertwined a web connecting people universally, encouraging unprecedented social interactions and information exchange. However, this digital connectivity has also spawned the growth of fake social media accounts used for mass spamming and targeted at...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2024, Vol.15 (3)
Main Authors: Alnagi, Eman, Ahmad, Ashraf, Al-Haija, Qasem Abu, Aref, Abdullah
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Boolean
Computer science
Cybersecurity
Datasets
Digital media
Explainable artificial intelligence
Feature selection
Internet
Machine learning
Social factors
Social networks
Spamming
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Summary:The recent global social network platforms have intertwined a web connecting people universally, encouraging unprecedented social interactions and information exchange. However, this digital connectivity has also spawned the growth of fake social media accounts used for mass spamming and targeted attacks on certain accounts or sites. In response, carefully con-structed artificial intelligence (AI) models have been used across numerous digital domains as a defense against these dishonest accounts. However, clear articulation and validation are required to integrate these AI models into security and commerce. This study navigates this crucial turning point by using Explainable AI’s SHAP technique to explain the results of an XGBoost model painstakingly trained on a pair of datasets collected from Instagram and Twitter. These outcomes are painstakingly inspected, assessed, and benchmarked against traditional feature selection techniques using SHAP. This analysis comes to a head in a demonstrative discourse demonstrating SHAP’s suitability as a reliable explainable AI (XAI) for this crucial goal.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.01503125