In this work, we have proposed a Machine learning based model that can identify fake or bot created accounts accurately. This paper is divided into multiple parts: Introduction, Literature Review, Methodology, Results and Discussion, Conclusion.

Discover how machine learning techniques can detect fake social media profiles. Learn about LSTM, XG Boost, Random Forest, and Neural Networks. Find out which technique is the best for identifying phony profiles. Python and essential libraries are used for implementation.

Researchers and technology businesses have turned to machine learning as a potent method for Fake profile identification due to the severity of this problem. With its ability to examine enormous datasets and identify complex patterns, machine learning presents a possible answer to the enduring problem of fake profile identification.

III. SYSTEM ARCHITECTURE AND METHODOLOGY Figure 1 – System Architecture In our research work, a novel approach has been presented for the identification of fake profiles on social media using supervised machine learning algorithms. The proposed model has applied data preprocessing techniques to datasets before analyzing them.

Apr 10, 2023 · This project uses several machine learning techniques to discriminate between fake and authentic Twitter profiles based on characteristics such as follower and friend counts, status...

Our project aims to detect the fake profiles of various famous social media applications like Instagram, Facebook, Twitter and Linked In. 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

the detection of fake profiles. This study presents a survey of the existing and latest technical work on fake profile detection. 3. EXISTING SYSTEM Sarcode and Mishra proposed a different approach which is a sequence of steps to detect fake profiles. They used the Twitter graph API tool to gain access to

Mar 6, 2025 · Machine learning algorithms keep improving their accuracy in spotting and stopping false profile proliferation across digital platforms by combining several data sources, applying ensemble learning techniques, and using advanced feature extraction methods.

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.

On this paper we presented a machine learning and natural language processing system to observe the false profiles in online social networks. Moreover, we are adding the five algorithms such that model Support Vector Machine (SVM), Random Forest classifier, Gradient Boost classifier, Naïve Bayes, and Logistic Regression algorithm to increase ...