Subjects: Psychology >> Applied Psychology submitted time 2019-01-22
Abstract： With the development of the Internet, more and more attention has been paid to online verbal aggressive and prosocial behavior, both of which are essentially the expression of individual emotions and are closely related to the changes of individual emotions. In this paper, we crawled the data from Microblog the most popular social software in China, obtaining the word frequency data of aggressive and prosocial behavior, and analyzed the time trend with seasonal differences of these two behaviors. The results show that the time trends of aggressive and prosocial behavior are highly consistent in a year. The frequency of aggressive words in Microblog is significantly different in different seasons (F=2.935, P= 037), in which the frequency of words in winter is significantly higher than that in autumn; the frequency of prosocial words is also significantly different in different seasons (F=14.51, P<0.05), in which the frequency of words in winter is significantly higher than that in other seasons. "
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2019-01-22
Abstract： Players preferences for different types of games are influenced by their own characteristics. The number of players determines that the mode of single player game is more independent, while the mode of online game mode is more collaborative. Given that individualism individuals tend to emphasize independence, collectivism individuals emphasize collaboration. We hypothesized that players’ individualism-collectivism tendency may affect their preference for single player game or online game. This study used Weibo user’s data to explore whether there was a difference in individualism-collectivism words expressions between single player game players and online game players. Then we used these features to predict players single player or online game preferences. The result showed that single player game players expressed more individualism words in Weibo, while online game players expressed more collectivism words. Using machine learning method, individualism-collectivism words expressions could predict players type, but accuracy of the model was low. This study provided preliminary evidence for using Weibo data to identify users preference for games, thus had certain application value.
Peer Review Status:Awaiting Review
Hits
Hits
Downloads
Comment