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From Information Cascade to Knowledge Transfer: Predictive Analyses on Social Networks.

机译：从信息层叠到知识转移：社交网络的预测分析。

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摘要

As social media continues to influence our daily life, much research has focused on analyzing characteristics of social networks and tracking how information flows in social media. Information cascade originated from the study of information diffusion which focused on how decision making is affected by others depending on the network structure. An example of such study is the SIR (Susceptible, Infected, Removed) model. The current research on information cascade mainly focuses on three open questions: diffusion model, network inference, and influence maximization. Different from these studies, this dissertation aims at deriving a better understanding to the problem of who will transfer information to whom. Particularly, we want to investigate how knowledge is transferred in social media.;The process of transferring knowledge is similar to the information cascade observed in other social networks in the way that both processes transfer particular information from information container to users who do not have the information. The study first works on understanding information cascade in term of detecting information outbreak in Twitter and the factors affecting the cascades. Then we analyze how knowledge is transferred in the sense of adopting research topic among scholars in the DBLP network. However, the knowledge transfer is not able to be well modeled by scholars' publications since a "publication" action is a result of many complicated factors which is not controlled by the knowledge transfer only.;So, we turn to Q&A forum, a different type of social media that explicitly contain the process of transferring knowledge, where knowledge transfer is embodied by the question and answering process. This dissertation further investigates Stack-Overflow, a popular Q&A forum, and models how knowledge is transferred among StackOverflow users. The knowledge transfer includes two parts: whether a question will receive answers, and whether an answer will be accepted. By investigating these two problems, it turns out that the knowledge transfer process is affected by the temporal factor and the knowledge level, defined as the combination of the user reputation and posted text. Take these factors into consideration, this work proposes TKTM (Time based Knowledge Transfer Modeling) where the likelihood of a user transfers knowledge to another is modeled as a continuous function of time and the knowledge level being transferred. TKTM is applied to solve several predictive problems: how many user accounts will be involved in the thread to provide answers and comments over time; who will provide the answer; and who will provide the accepted answer. The result is compared to NetRate, QLI, and regression methods such as RandomForest, linear regression. In all experiments, TKTM outperforms other methods significantly.
机译：随着社交媒体继续影响我们的日常生活，许多研究都集中在分析社交网络的特征并跟踪信息在社交媒体中的流动方式。信息级联源自对信息传播的研究，该研究关注于决策如何受网络结构的影响。这种研究的一个例子是SIR（易感，感染，去除）模型。当前关于信息级联的研究主要集中在三个开放性问题上：扩散模型，网络推理和影响最大化。与这些研究不同，本论文旨在对谁将信息传递给谁的问题有一个更好的理解。特别是，我们要研究如何在社交媒体中传递知识。知识传递的过程类似于在其他社交网络中观察到的信息级联，其方式是这两个过程都将特定信息从信息容器传递给没有信息的用户。信息。该研究首先从检测Twitter中的信息爆发及其影响因素的角度来理解信息级联。然后，我们从DBLP网络中的学者之间采用研究主题的角度分析了知识是如何转移的。但是，知识转移不能由学者的出版物很好地建模，因为“发布”动作是许多复杂因素的结果，这些因素并不仅仅受知识转移的控制。因此，我们转向问答论坛，这是一个不同的问题。明确包含知识转移过程的社交媒体类型，其中知识转移由问答过程体现。本文进一步研究了一个流行的问答论坛Stack-Overflow，并建立了如何在StackOverflow用户之间转移知识的模型。知识转移包括两个部分：一个问题是否会收到答案，以及一个问题是否会被接受。通过研究这两个问题，可以发现知识转移过程受时间因素和知识水平的影响，时间因素和知识水平被定义为用户信誉和张贴文本的组合。考虑到这些因素，这项工作提出了TKTM（基于时间的知识转移建模），其中用户将知识转移到另一个人的可能性被建模为时间和知识水平的连续函数。 TKTM用于解决几个预测性问题：线程中将涉及多少个用户帐户，以便随着时间的推移提供答案和评论；谁来提供答案；以及谁将提供接受的答案。将结果与NetRate，QLI和回归方法（如RandomForest，线性回归）进行比较。在所有实验中，TKTM均明显优于其他方法。

著录项

  • 作者

    Cui, Biru.;

  • 作者单位

    Rochester Institute of Technology.;

  • 授予单位 Rochester Institute of Technology.;
  • 学科 Computer science.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 138 p.
  • 总页数 138
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 公共建筑;
  • 关键词

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