This document discusses two approaches for identifying malicious data in social media: Shannon entropy and power law distribution. The Shannon entropy approach calculates the entropy of features like source/destination IP addresses and port numbers to detect anomalous network traffic patterns. The power law distribution approach models malware propagation across networks and finds that malware distribution transitions from exponential to power law over time. Experimental results on social media datasets found the Shannon entropy approach could detect malware based on the number of applications installed, while power law distribution identified good and malicious files shared between users. Both techniques aim to improve detection of malicious content shared over social networks.