Fake Instagram Account Detection using ML Algorithms
Dr.J.Jane Rubel Angelina, S.Sureshkumar, Ankit agarwal, Sable Ram kumar, Peddi Nikitha, Sriram kumar
Dept of computer science and engineering
kalasalingam academy of research and education
Virudhunagar,Tamilnadu,India
Abstract—Nowadays, the majority of people utilise social networking sites on a daily basis. Numerous people create profiles on social networking websites every day and connect with others there, regardless of their location or time. False identities are used in additional malicious operations in addition to playing a significant part in advanced persistent threats. Users of social networking sites can benefit from them, but they also have to worry about the security of their personal information. We must first determine the user's social network accounts before we can determine who is endorsing threats in these platforms. Social media usage has dramatically increased in recent years, according to statistics. Social networking sites have made things easier as they allow us to connect to people effortlessly and converse with them without the requirement for physical meetups. One of the major issues with Online Social Networks (OSNs) is fake interaction, which is used to artificially boost an account's popularity. Because phoney involvement causes businesses to lose money, inaccurate audience targeting in advertising, inaccurate product prediction systems, and an undesirable social network atmosphere, its detection is essential. Based on the classification, it is required to distinguish between real and phoney profiles on social media.Several categorization techniques have traditionally been used to identify phoney social media accounts. However, there are ways to improve social media's ability to detect phoney profiles. The suggested effort uses technology and machine learning to boost the percentage of predicted phoney profiles. Chi-square technique is used in feature selection models to find the best data. The several machine learning methods, including the Logistic Regression and Random Forest algorithms, are used in the classification approach. The classification outcome based on recall, sensitivity, specificity, f1-score, accuracy, and precision.
Keywords—Machine learning, online social networks, Instagram, Social media, Natural language process.
