To address this issue, machine learning techniques offer promising solutions by analysing profile data and user behaviour to identify and mitigate the impact of fraudulent profiles across various online platforms.

Apr 5, 2023 · In this research, we identify the minimal set of profile data that are necessary for identifying fake profiles in LinkedIn and identify the appropriate data mining approach for such task.

Language Processing (NLP) and Deep Learning techniques to enhance fake profile detection accuracy. Along with Support Vector Machine (SVM) and Naïve Bayes classifiers, we integrate a Convolutional Neural Network (CNN) model to analyse textual and behavioural patterns in profile data. The CNN model captures intricate

Detection of malicious account is significant. The methods based on machine learning-based were used to detect fake accounts that could mislead people. The dataset is pre-processed using various python libraries and a comparison model is obtained to get a feasible algorithm suitable for the given dataset.

This project aims to detect fake profiles on social media platforms using Artificial Neural Networks (ANNs). Through the analysis of user data and behavioral patterns, the model makes predictions about the authenticity of a profile.

We use an Artificial Neural Network to detect fraud in Instagram profiles. Based on prediction, performance is measured and accuracy is calculated. The dataset used in the experiment contains various attributes such as user name, profile picture, username length, and so on, with the final attribute indicating whether the

By using different machine learning and deep learning techniques on different datasets to find the best model and making an website to detect whether the user given profile real or fake.

Neural Networks to ensure high accuracy in detecting fake accounts. The system is designed to ... The Fake Profile Detection System consists of multiple functional components that work together to analyze and classify social media accounts as real or fake. The key functionalities include: ... Data Flow Diagram (DFD) Level 0: User data is ...

Detect fake profiles in online social networks using Support Vector Machine, Neural Network and Random Forest Resources

This module uses two classification tasks: account-level bot detection and tweet-level bot detection. when OSN data is pre- processed and Feature Extracted, BiLSTM-CNN model is used in the classification of users into either legitimate users or bots.