Empirical Research On Online Collective Behavior Based On Network Statistical Analysis

Collective behavior refers people respond and act spontaneously together under particular situation.More than one hundred years ago,G.Lebon firstly proposed and researched collective behavior's influence on human society.Nowadays,online collective behavior speeds up social and economic prosperity and improves life quality.Scientific understanding about social online collective behavior will facilitate us using internet to serve people's life and promote social development.We offer an interdisciplinary study of computer science and social science.We constructed a mass scale internet hot events database,analyzed internet influence on different types of hot events,and tried to find different types of social collective behavior pattern.Our major findings are as follows:1,We continuously followed and recorded data every 10 minutes for 10 months from September 14,2012 to July 11,2013,and collected over 14 million “hot” posts from Sina Weibo,the largest microblogging provider in China.After removing spammers and noises,we developed a database with more than 10 million of threads.2,We classified the total events into three types,political events,social events and non-public event.We analyzed Chinese collective behavior on these different types of events with the fluctuation scaling method and affinity propagation method.We found that people show obvious collective behavior for online events.The threshold of collective behavior varies for different types of events.For the social events that have closely relate to people's interest and benefit,people are easily to be influenced by others.But the result does not mean we should ignore the impact of political collective events on China society.3,We generated life curves for online events with The collective events life curves show for there is higher percentage of political events in the quick growth life curve.Chinese may collect very quickly on some special political events.Both social events and some kinds of political events may arouse serious collective behavior and have potential impact on China's society.4,We used different methods to predict the popularity of the three types of events.Based on the intrinsic characteristics of the three event types,this paper creates an effective method to predict such events.We found that people's online behavior regarding event types varies in terms of follow-up statistics and the predictability of events.The Chinese are,typically,quite concerned with social affairs that relate most closely to their personal interests and preferences.People tend to cluster around political events more often than social events and non-public events.This is demonstrated by an algorithm embedded with a clustering growth pattern of events,which predicts the popularity of online political events above others.The statistical findings are justified by Habermas' public sphere theory and the theory of vertical/horizontal collectivism/individualism.This research provides an interesting piece of computational social science work to assist in the analysis of incentives concerning China's collective events.In one word,Chinese online collective behavior has regularity to conform to.Personal interest is the incentive for online collective events,but other factors beyond interest also hold the potential to drive online collective behavior.More attention should be paid to online political collective behavior research.