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With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods. [ABSTRACT FROM AUTHOR]

It has been demonstrated that online consumer reviews are an important source of information that affect individuals' purchase decision making. To understand the influence of online reviews, this study extends prior research on information adoption by incorporating the perspective of herd behavior. We develop and empirically test a research model using data collected from an existing book review site. We report 2 major findings. First, argument quality and source credibility predict information usefulness, which affects the adoption of online reviews. Second, we determine that the adoption of online reviews is also influenced by 2 herd factors, namely, discounting own information and imitating others. We further identify the key determinants of these herd factors, including background homophily and attitude homophily. The theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]