: Tag accounts or comments where the percentage of unique words is exceptionally low (e.g., < 30%), a common indicator of automated spam.
: Calculate metrics like word density, character counts, and punctuation frequency to distinguish between legitimate users and bots. spammer.py
: Use libraries like NLTK to tokenize sentences and analyze the POS (Part-of-Speech) tags of suspected spam messages to find structural anomalies. Network Security and Malware Research : Tag accounts or comments where the percentage
In academic papers regarding network intrusion, similar naming conventions are used for tools that test system vulnerabilities: : Calculate metrics like word density