The physical, achievement, and personality characteristics of Chinese schoolchildren are evaluated. The results of two surveys of Chinese schoolchildren, conducted in Beijing and Jilin Province, are presented. In the Beijing sample, the differences between only children and others were small but statistically significant, with the only children having higher scores in achievement and physical size. In the Jilin Province sample, the differences between only children and others were also small but significant. These differences were found entirely in physical characteristics, with only children being smaller, perhaps because the only children in the Jilin sample were much younger than the non-only children. Analyses of the combined sample of Beijing and Jilin schoolchildren indicated that the only-child advantages in achievement were found among children from urban families, not rural peasant families. While significant differences in height and weight were found in all three samples, none of the results indicated a significant difference in the proportion of body fat. None of the analyses indicated that only children had undesirable personalities, as judged by teachers and mothers.
User influence has always been a major topic in the field of social networking. At present, most of the research focuses on three aspects: topological structure, social-behavioral dimension, and topic dimension and most of them ignore the difference between the audience. These models do not consider the impact of personality differences on user influences. To meet this need, this paper introduces the personality traits factor and proposes a user influence model which integrates personality traits (IPUIM) under a strong connection. The user influence measurement is constructed through the information dimension, structural dimension, and user behavioral dimension. The personality report of the user group is obtained by means of NEO-PI-R (The big five personality inventory, Chinese edition) and machine learning method, and it is integrated into the user influence model. The experiment proves that the model proposed in this paper has good accuracy and applicability in measuring user influence, and can effectively identify the key opinion leaders of different personality trait clusters.
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