Online social networking sites have shown an unbelievable widening in the last decade. Spammers utilise social networking sites to unroll spam messages due to its fame and use various procedures to spread spam. Consequently, the identification of spam must be well fortified enough to detect unsolicited messages and deter spammers. Though various spam identification procedures are obtainable, to improve the accuracy for spam identification is inevitable. In this work, a method to detect unsolicited messages is proposed to recognise and avert spam messages. The social context parameters such as trust and strength as well as spam template matching are also considered along with basic classifiers for effective spam classification. The intercommunication factors between the users are used for strength calculation. Spam template generation is performed based on the majority merge operation on the spam messages during the training time, and spam templates comparison is performed with the incoming messages during the testing time. Trust value updation is performed after the message classification. Experimental results demonstrate that the proposed model with SVM-Polynomial Radial Basis kernel which provides better accuracy in spam classification and outperforms all the state-of-the-art methods. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.