Advances in Brain-Computer Interfaces and Neural Integration
Fake Social Media Profile Detection Using Ml Algorithms
Abstract
M. Saravanapriya, Keren Lois Daniel, A. Tressa Bernice, V. Gunasekaran and A. Anbumani
An analysis of the completeness of profiles, image searches, usernames, profile descriptions, badge verification, activity metrics, engagement rate thresholds, geographical searches, and account lifespans are some of the methods used in this abstract to identify fraudulent social media accounts. The system uses Google Custom Search to perform reverse image searches on profile photographs and a Naive Bayes classifier to determine whether a profile is complete. It looks for oddities in usernames, assesses profile descriptions for copycat material, and validates profiles using platform badges. Engagement rate criteria are set to detect outliers, and activity measurements are examined for anomalies. Account holders’ geographic regions are checked for anomalies, and account lifespans are scrutinized for questionable trends. By automating the identification process, our method upholds user security and confidence while offering an efficient and scalable way to counter false profiles on social media platforms. This extra level of analysis contributes to a more omprehensive plan for combating bogus social media profiles and increases the effectiveness of the detection process even further.