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什么阻止社会流行?

What Stops Social Epidemics?
课程网址: http://videolectures.net/icwsm2011_steeg_epidemics/  
主讲教师: Greg Ver Steeg
开课单位: 加州理工学院
开课时间: 2011-08-18
课程语种: 英语
中文简介:
在理解图表传播过程动力学方面的理论进展表明,存在一个流行病阈值,低于该阈值,没有流行病形成,高于该阈值,流行病传播到图表的一个重要部分。我们已经观察到社交媒体网站digg上的信息层叠,其传播速度足以让一个最初的传播者感染数百人,但最终只影响整个网络的0.1%。我们发现,两种效应,先前孤立地研究,结合起来,极大地限制了最后规模的级联对迪格。首先,由于digg网络的高度集群结构,大多数了解一个故事的人都通过多个朋友接触到它。这种结构降低了流行阈值,同时适度减缓了级联的整体增长。此外,我们发现,digg上的社会传染机制指出了信息传播和其他传染过程之间的根本区别:尽管在一个社会群体中存在多种感染机会,但人们不太可能成为反复接触的信息传播者。对于更多的聚集图,这种机制的结果更加明显。最终,这一效应严重缩减了Digg的社会流行病规模。
课程简介: Theoretical progress in understanding the dynamics of spreading processes on graphs suggests the existence of an epidemic threshold below which no epidemics form and above which epidemics spread to a significant fraction of the graph. We have observed information cascades on the social media site Digg that spread fast enough for one initial spreader to infect hundreds of people, yet end up affecting only 0.1% of the entire network. We find that two effects, previously studied in isolation, combine cooperatively to drastically limit the final size of cascades on Digg. First, because of the highly clustered structure of the Digg network, most people who are aware of a story have been exposed to it via multiple friends. This structure lowers the epidemic threshold while moderately slowing the overall growth of cascades. In addition, we find that the mechanism for social contagion on Digg points to a fundamental difference between information spread and other contagion processes: despite multiple opportunities for infection within a social group, people are less likely to become spreaders of information with repeated exposure. The consequences of this mechanism become more pronounced for more clustered graphs. Ultimately, this effect severely curtails the size of social epidemics on Digg.
关 键 词: 社会新闻网站; 级联反应; 高度聚集结构; 集群图
课程来源: 视频讲座网
最后编审: 2021-02-10:nkq
阅读次数: 67