Senevirathna Chathurani, Gunaratne Chathika, Rand William, Jayalath Chathura, Garibay Ivan
Complex Adaptive Systems Lab, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA.
Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Entropy (Basel). 2021 Jan 28;23(2):160. doi: 10.3390/e23020160.
Influence cascades are typically analyzed using a single metric approach, i.e., all influence is measured using one number. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. In this paper, we present a novel method for tracking these influence relationships over time, which we call influence cascades, and present a visualization technique to better understand these cascades. We investigate these influence patterns within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users.
影响级联通常使用单一指标方法进行分析，即所有影响都用一个数字来衡量。然而，社会影响并非单一的；不同用户以不同方式施加不同影响，且影响与用户和内容特定属性相关。这样一种属性可能是该行为是发起新帖子、对帖子的贡献还是分享现有帖子。在本文中，我们提出一种随时间跟踪这些影响关系的新方法，我们称之为影响级联，并提出一种可视化技术以更好地理解这些级联。我们使用实证数据并与作为零模型的无标度网络进行比较，来研究在线社交媒体平台内部和之间的这些影响模式。我们的结果表明，影响级联的特征和影响模式实际上受到平台和用户社区的影响。
