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 various areas.
The sexual identity of 65 Malaysian male medical students was investigated by anonymous questionnaire. Of these students, 40% were aware of homosexual feelings prior to age 15 years, and 16% were so aware currently. There were correlations between current homosexual feelings and feminine sex dimorphic behavior during childhood and between current homosexual feelings and feminine gender identity. The results are discussed in light of results of a similar questionnaire completed by 138 male medical students in Sydney, Australia.
Study site: National University of Malaysia (Universiti Kebangsaan Malaysia), Kuala Lumpur, Malaysia