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We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference
among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement
results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection
component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an
unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of
the proposed classification system