Comprehension of the statistical and structural mechanisms governing human dynamics
in online interaction plays a pivotal role in online user identification, online profile development,
and recommender systems. However, building a characteristic model of human
dynamics on the Internet involves a complete analysis of the variations in human activity
patterns, which is a complex process. This complexity is inherent in human dynamics and
has not been extensively studied to reveal the structural composition of human behavior.
A typical method of anatomizing such a complex system is viewing all independent interconnectivity
that constitutes the complexity. An examination of the various dimensions of
human communication pattern in online interactions is presented in this paper. The study
employed reliable server-side web data from 31 known users to explore characteristics of
human-driven communications. Various machine-learning techniques were explored.
The results revealed that each individual exhibited a relatively consistent, unique behavioral
signature and that the logistic regression model and model tree can be used to accurately
distinguish online users. These results are applicable to one-to-one online user
identification processes, insider misuse investigation processes, and online profiling in