# Instagram Fake and Automated Account Detection

arXiv: https://arxiv.org/abs/1910.03090 · HTML: https://mlsanity.com/paper/1910.03090/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets](https://mlsanity.com/paper/2101.09345/) (2021)
- [Social Honeypot for Humans: Luring People through Self-managed Instagram Pages](https://mlsanity.com/paper/2303.17946/) (2023)
- [Public interest in science or bots? Selective amplification of scientific articles on Twitter](https://mlsanity.com/paper/2410.01842/) (2024)

## Abstract (arXiv)

Fake engagement is one of the significant problems in Online Social Networks (OSNs) which is used to increase the popularity of an account in an inorganic manner. The detection of fake engagement is crucial because it leads to loss of money for businesses, wrong audience targeting in advertising, wrong product predictions systems, and unhealthy social network environment. This study is related with the detection of fake and automated accounts which leads to fake engagement on Instagram. Prior to this work, there were no publicly available dataset for fake and automated accounts. For this purpose, two datasets have been published for the detection of fake and automated accounts. For the detection of these accounts, machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines and Neural Networks are applied. Additionally, for the detection of automated accounts, cost sensitive genetic algorithm is proposed to handle the unnatural bias in the dataset. To deal with the unevenness problem in the fake dataset, Smote-nc algorithm is implemented. For the automated and fake account detection datasets, 86% and 96% classification accuracies are obtained, respectively.