基于腾讯微博的话题级舆论影响力模型（TOIM）研究
Topic-Level Opinion Influence Model(TOIM): An Investigation Using Tencent Micro-Blogging
摘要：
从微博客中挖掘用户意见已经在美国最流行的社交网站如Twitter和Facebook上得到了广泛的研究，但在其他国家（如中国），对微博客网站的研究却很少。本文通过对中国最大的微博客网站之一腾讯微博客的社会舆论影响力进行分析，试图揭示中国微博客用户的行为模式。本文提出了一个主题级的意见影响力模型（TOIM），同时将主题因素和社会的直接影响力在一个统一的概率框架。基于TOIM，提出了两种考虑间接影响的话题级观点影响传播和聚合算法：CP（Conservative Propagation）和NCP（NonConservative Propagation）。TOIM利用用户的历史社交互动记录，通过统计学习过程来构建用户的进步意见和邻居的意见影响力，这些意见可以进一步用于预测用户对某些特定主题的未来意见。为了评估和测试该模型，设计了一个实验，并使用腾讯微博客的子数据集。实验结果表明，TOIM在预测用户意见方面优于基线方法。CP和NCP的应用无显著差异，但能显著提高TOIM的回忆率和F1-测量。
英文摘要：
Mining user opinion from Micro-Blogging has been extensively studied on the most popular social networking sites such as Twitter and Facebook in the U.S., but few studies have been done on Micro-Blogging websites in other countries (e.g. China). In this paper, we analyze the social opinion influence on Tencent, one of the largest Micro-Blogging websites in China, endeavoring to unveil the behavior patterns of Chinese Micro-Blogging users. This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor and social direct influence in a unified probabilistic framework. Based on TOIM, two topic level opinion influence propagation and aggregation algorithms are developed to consider the indirect influence: CP (Conservative Propagation) and NCP (None Conservative Propagation). Users' historical social interaction records are leveraged by TOIM to construct their progressive opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topics. To evaluate and test this proposed model, an experiment was designed and a sub-dataset from Tencent Micro-Blogging was used. The experimental results show that TOIM outperforms baseline methods on predicting users' opinion. The applications of CP and NCP have no significant differences and could significantly improve recall and F1-measure of TOIM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
