Abstract
Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm.
Highlights
The growing popularity of social media platforms has benefitted the people and caught the attention of scammers
These results indicate that the different supervised machine learning models such as Support Vector Machine (SVM), and proposed hybrid SVM in predicting of identity deception by humans on Twitter
The usage of datasets with fake profiles efficiently eliminates the difficulty in finding fake profiles
Summary
The growing popularity of social media platforms has benefitted the people and caught the attention of scammers. The absence of any authentication process has made it easy for anyone to make a fake account This serves as an advantage for the scammers encouraging them to use fake account for illegal activities as there is a good chance that the account holder will not get caught. Owing to this the popularity of fake accounts has increased. Methods like Machine Learning can help in making the stratification process a lot easier and accurate [6] This project involves use of machine learning model to classify social media accounts as genuine or fake. In this research one of the key parts is spam profiles identification which improves the safety of real users
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