![]() The detection of Twitter fake accounts are studied in using support vector machines and logistic regression, in using graph based methods, in In the literature, there are some works and released datasets about the detection of fake engagement activity itself and the detection of users who engages inorganic activity in OSNs like Facebook and Twitter. The unevenness problem in the fake dataset, Smote-nc algorithm is implemented.įor the automated and fake account detection problem, 86 ![]() Logistic regression, support vector machines and neural networks are applied.Īdditionally, for the detection of automated accounts, cost sensitive geneticĪlgorithm is applied because of the unnatural bias in the dataset. For theĭetection of these accounts, machine learning algorithms like Naive Bayes, ![]() For this purpose, two datasets haveīeen generated for the detection of fake and automated accounts. As far as we know, there is no publicly availableĭataset for fake and automated accounts. Related with the detection of fake and automated accounts which leads to fakeĮngagement on Instagram. Predictions systems, and unhealthy social network environment. Money for businesses, wrong audience targeting in advertising, wrong product ![]() The detection of fake engagement is crucial because it leads to loss of (OSNs) which is used to increase the popularity of an account in an inorganic Fake engagement is one of the significant problems in Online Social Networks ![]()
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