政治模因在社交媒体中的传播:主旨摘要

PLEAD '12 Pub Date : 2012-11-02 DOI:10.1145/2389661.2389663
F. Menczer
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引用次数: 4

摘要

本讲座介绍了正在进行的关于社会媒体中信息传播的研究工作,特别侧重于Twitter微博网络中的政治传播。社交媒体平台在塑造美国和世界各地的政治话语方面发挥着重要作用。truthy.indiana.edu的基础设施使我们能够挖掘和可视化与政治主题相关的大量社交媒体数据流。本主题的分析涉及社会媒体用户在线政治活动中的两极分化和跨意识形态交流,以及党派不对称。机器学习工作可以成功地利用模因扩散网络的结构来检测模拟基层运动的精心策划的astroturf攻击,并预测活跃用户的政治派别。转发网络将个人划分为两个截然不同的同质社区,即左倾和右倾用户。提及网络并没有表现出这种隔离,而是形成了一个信息在这两个党派社区之间流动的沟通桥梁。我们提出了一种作用机制来解释这些不同的拓扑结构,并提供了支持这一假设的统计证据。与政治传播相关的是关于网络社会运动诞生的问题。社交媒体数据为寻找捕捉这些重大事件的签名提供了机会。最后,我将介绍一个在社交媒体上争夺注意力的模型。在这一过程中出现了一种动态的信息传播,其中一些想法会像病毒一样传播,而大多数则不会。我将表明,不同话题的相对流行,我们所接触到的信息的多样性,以及我们对特定模因的集体兴趣的消退,都可以解释为对有限注意力的竞争和社会网络结构之间的结合。令人惊讶的是,一个人可以在不需要假设这些想法的内在价值不同的情况下,再现出这些想法的受欢迎程度和持久性的巨大异质性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The diffusion of political memes in social media: keynote abstract
This talk presents ongoing work on the study of information diffusion in social media, focusing in particular on political communication in the Twitter microblogging network. Social media platforms play an important role in shaping political discourse in the US and around the world. The truthy.indiana.edu infrastructure allows us to mine and visualize a large stream of social media data related to political themes. The analyses in this keynote address polarization and cross-ideological communication, and partisan asymmetries in the online political activities of social media users. Machine learning efforts can successfully leverage the structure of meme diffusion networks to detect orchestrated astroturf attacks that simulate grassroots campaigns, and to predict the political affiliation of active users. The retweet network segregates individuals into two distinct, homogenous communities of left- and right-leaning users. The mention network does not exhibit this kind of segregation, instead forming a communication bridge across which information flows between these two partisan communities. We propose a mechanism of action to explain these divergent topologies and provide statistical evidence in support of this hypothesis. Related to political communication are questions about the birth of online social movements. Social media data provides an opportunity to look for signatures that capture these seminal events. Finally, I will introduce a model of the competition for attention in social media. A dynamic of information diffusion emerges from this process, where a few ideas go viral while most do not. I will show that the relative popularity of different topics, the diversity of information to which we are exposed, and the fading of our collective interests for specific memes, can all be explained as deriving from a combination between the competition for limited attention and the structure of social networks. Surprisingly, one can reproduce the massive heterogeneity in the popularity and persistence of ideas without the need to assume different intrinsic values among those ideas.
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