  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.
