Instagram Fake Account Detection

Instagram Fake Account Detetction

Jupyter Notebook implementation to detect Instagram's fake accounts.

Machine Learning Project - Classification Algorithms

📚 Table of Contents

Table of Contents
About The Project
Getting Started
- Installation
- Updates
Datasets
Classification Report
- Report for Private Accounts
- Report for Public Accounts
License
Contributors

👨‍💻 About The Project

Insta Fake Account Detection is Machine Learning Project developed for the Big Data and Business Intelligence course of @Università di Parma. The objective of this project is the automated recognition of fake Instagram accounts, using some Classification Algorithms.

The projects has 2 dataset: the first with 11 feature is used for the recognition of private accounts, which due to their privacy have a limited amount of informations to share, the second with 14 features is used with the public accounts, which thanks to their privacy have more informations to work with, such as the date of the post published, which gave the algorithms some informations about the index of activity of the account.

Every account's feature has been scraped using an Instagram Web Scraper.

Then the two dataset have been subject to a Preprocessing Phase. This phase consists of the standardization and the normalization of the two datasets. In this have been done the Feature Importance Forest of Trees and the Feature Selection analysis. In the Feature Selection has been used four algorithms:

L1 Based
Tree Based
Removing Features with Low Variance
Univariate Select Best (K = 4)

The features, now preprocessed, are taken and given to this Machine Learning Classifier Algorithms:

AdaBoost
Decision tree
K-Nearest Neighbours (KNN)
Logistic Regression
Multi-Layer Perceptron
Random Forest
Stochastic Gradient Descent (SGD)
Stochastic Gradient Descent (SGD)
Support Vector Machine (SVM)

For every algorithm, in addition to the trainining and the testing phase, has been calculated:

Cross Validation
Confusion Matrix
Receiver Operating Characteristic / ROC Curve
Classification Report

Eventually, we created a telegram bot with the best algorithm of the two datasets. We embodied it in the bot and run the script. Once started you only have to send it a username and it will verify the authenticity of the account and send you the reply with the response you search.

Check 👉 Insta Fake Detector Bot 👈 to see the relative telegram-bot project. Try the efficiency of the AdaBoost algorithm on detecting the fake accounts and ask the bot the account to detect.

🔨 Getting Started

Installation

You can just clone this repository and install the requirements by running:

$ pip install igramscraper

$ pip install sklearn

Then start the notebook files in the relative folders to see the results.

Updates

Pull this repository for updates.

🗂 DATASETS

Is possible to find the datasets in the resources folder.

Dataset for Private Accounts

Profile Pic	Nums / Length Username	Full Name Words	Bio Length	External URL	Is Private	Is Verified	Is Business	# Post	# Followers	# Following

Dataset for Public Accounts

Profile Pic	Nums / Length Username	Fake Account	Full Name Words	Bio Length	External URL	Is Verified	Is Business	# Post	# Followers	# Following	Last Post Recent	% Post Single Day	Index of Activity	Average of Likes

Features Description

Profile Pic boolean value. 0 if the user doesnt'have the profile pic, 1 otherwise.
Nums / Length Username double value. How many special characters of numeric characters the username has on its full length.
Full Name Words numeric value. How many words in the full name.
Bio Length numeric value. How many characters in the biography of the account.
External URL boolean value. 0 if the user doesnt'have the an external URL in the biography, 1 otherwise.
Is Private boolean value. 0 if the user doesnt'have a private account, 1 otherwise.
Is Verified boolean value. 0 if the user doesnt'have the verified badge , 1 otherwise.
Is Business boolean value. 0 if the user doesnt'have a business account, 1 otherwise.
# Post numeric value. The number of the post published by the account.
# Followers numeric value. The number of the followers of the account.
# Following numeric value. The number of the following of the account.
Last Post Recent boolean value. 0 if the user doesnt'have a post publisched withing 6 months, 1 otherwise.
% Post Single Day double value. How many post has been published in the same same day on the total number of the posts.
Index of Activity double value. How many post in average the account publishes every month.
Average of Likes double value. Average of the likes of a post of the account.

📊 CLASSIFICATION REPORT

Report for Private Accounts

Algorithm	Accuracy	Precision	Recall	F-Score
AdaBoost	96%	96%	96%	96%
Decision Tree	96%	96%	96%	96%
KNN Classifier	95%	96%	95%	95%
Logistic Regression	94%	94%	94%	94%
Multi-Layer Perceptron	96%	96%	96%	96%
Random Forest	94%	94%	94%	94%
SGD Classifier	95%	95%	95%	95%
SVM Classifier	94%	94%	94%	94%

Report for Public Accounts

Algorithm	Accuracy	Precision	Recall	F-Score
AdaBoost	97%	97%	97%	97%
Decision Tree	97%	97%	97%	97%
KNN Classifier	95%	95%	95%	95%
Logistic Regression	95%	95%	95%	95%
Multi-Layer Perceptron	97%	97%	97%	97%
Random Forest	98%	99%	98%	98%
SGD Classifier	95%	95%	95%	95%
SVM Classifier	95%	95%	95%	95%