RMCL: A deep learning based recursive malicious context learner in social networks
Devisha Arunadevi Tiwari · Computational Intelligence · 2022
Abstract Propagation of malicious content and its promotion is a recurrent problem prominent in social networks. Detection and interactive labeling of malicious context promotion on‐the‐fly is a very challenging and difficult task due to the lack of complete knowledge about the underlying context. Modern research in this field use neural networks and text encoders to analyze the information dynamically which is inefficient, in terms of time‐space consumed in the overall process. This article proposes an online active learning system to label the data streams on‐the‐fly by sampling them in the form of tweet‐retweet‐follow (trf) sequences in social networks. A heuristically pretrained recursive malicious context learner is fixed for knowledge acquisition, data accommodation and pseudonymization in the form of a transformer in the tree structured recursive neural network. The data stream is trained using recursive bidirectional training to capture long‐term dependencies. The COVID‐19 data streams are selectively sampled on‐the‐fly using psycho linguistic words in the proposed experiment and are labeled deceptive/nondeceptive based on knowledge learned during pretraining. The proposed recursive malicious context learner successfully resolves the problem of on‐the‐fly‐interactive labeling of a dynamically changing data stream.