Many applications—including political campaigns, public health, and marketing—depend on an awareness of social media patterns in connections. The complexity and volume of data in these exchanges demand for advanced machine learning techniques. Conventional methods produce less than perfect outcomes in test prediction since they fail to capture the intricate, temporal dynamics of social media activity. This work provides a novel approach combining residual learning for feature extraction with recurrent neural networks (RNNs) for classification. RNNs capture the sequential aspect of social media interactions; residual networks (ResNets) efficiently extract hierarchical information from the input. The proposed approach was tested on a large-scale dataset of social media interactions with an accuracy of 92.4%, precision of 91.7%, recall of 93.1%, and an F1-score of 92.4%. With this approach, F1-score improved by 5% and accuracy increased by 7% above existing methods.