Quality Enhancement for Uninvited Content of Social Media Using Support Vector Machine and Alexnet

Abstract

The objective of the work is to identify the uninvited content like Spam and Ham data in Reddit using Support Vector Machine over AlexNet. To acquire the accuracy, an innovative SVM Classifier function was used. The assortment of information and its pre-processing are examined. For the review, almost 20 samples were taken and 10 for each group to assess, look at and figure out the exactness of proposed calculations. For accuracy expectation, a G power of 80 % and the parameters Confidence Interval of 0.95, alpha 0.05 and beta 0.2 is utilized. From observation, the support vector machine with accuracy of 95.72 % is inferred to have higher accuracy in identifying the unsolicited content and avoid data theft, and its threshold is higher than the AlexNet method with an accuracy of 93.47 %, and with a statistical significance of p is 0.003 (p < 0.05), it is statistically significant. The social media platform was made so that people could talk to each other and share how they feel. But a lot of information is being stolen. A lot of the scams make profiles and collect information about other people. This research shows how important it is to be able to spot fraudsters on social media. Scammers were found using two different algorithms, SVM and AlexNet. Among those, the suggested SVM did well and figured out the unsolicited content and ham data to avoid data.