Fake Profile Detection on Social Networking Websites using Machine Learning

IEEE BASE PAPER TITLE:

Fake Profile Detection on Social Networking Websites using Machine Learning

(or)

OUR PROPOSED PROJECT TITLE:

Instagram Fake Account Detection using Machine Learning

IEEE BASE PAPER ABSTRACT:

These days, social media has a significant impact on everyone’s life. Most people frequently utilize social media platforms. Each of these social media platforms offers benefits and drawbacks, as well as security risks for our information. To determine who poses threats on these platforms, it is necessary to distinguish between the real and fake social media profiles. There are traditionally used various methods for identifying fake social media accounts. But these platforms need to be better at identifying phoney accounts. The accuracy rate of identifying fake accounts utilising timestamp data types is improved in this proposed work employing high gradient boosting algorithms and Natural Language Processing. In order to investigate the relationship between various machine learning methods and multi-features in time series, this study employs a variety of machine learning techniques.

PROJECT OUTPUT VIDEO:

ALGORITHM MODEL USED:

Random Forest Classifier & Decision Tree Classifier.

OUR PROPOSED PROJECT ABSTRACT:

In an age where social media has become an integral part of our lives, the challenge of detecting fake accounts on platforms like Instagram has gained significant importance. This project, titled “Instagram Fake Account Detection using Machine Learning” employs Python as its primary tool to tackle this problem. It leverages two powerful machine learning algorithms, the Random Forest Classifier and the Decision Tree Classifier, to accomplish this task.

The Random Forest Classifier demonstrates remarkable performance, achieving a 100% accuracy on the training dataset and an impressive 93% accuracy on the test dataset. Meanwhile, the Decision Tree Classifier exhibits its effectiveness with a training accuracy of 92% and a test accuracy of 92%.

The dataset employed in this project is composed of 576 records, each characterized by 12 distinct features. These features encompass critical aspects of Instagram profiles, including the presence of a profile picture, the ratio of numerical characters in usernames, the breakdown of full names into word tokens, the ratio of numerical characters in full names, the equality between usernames and full names, the length of user bios, the existence of external URLs, the privacy status of accounts, the number of posts, the count of followers, the number of accounts followed, and the ultimate classification of an account as “Fake” or “Not.”

By harnessing the capabilities of Python and these advanced machine learning models, this project endeavors to provide a robust and efficient solution for the identification of fake Instagram accounts. In doing so, it contributes to the preservation of the platform’s integrity and the security of its users.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 500 GB.
• Monitor : 15’’ LED.
• Input Devices : Keyboard, Mouse.
• Ram : 8 GB.

SOFTWARE REQUIREMENTS: 

• Operating system : Windows 10 / 11.
• Coding Language : Python 3.10.9.
• Web Framework : Flask.
• Frontend : HTML, CSS, JavaScript.

REFERENCE:

Dr. M. Sirish Kumar, Dr Jasmine Sabeena, Konduru Manasa Veena, Kummari Pavan, Malepati Sukavya, Kundavaram Sravanthi, “Fake Profile Detection on Social Networking Websites using Machine Learning”, 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), IEEE Conference, 2023.

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