Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain some of the irrational behaviors associated with financial decisions to help explain some of the fluctuations. In this study we argue that social media users who demonstrate an interest in finance can offer insights into ways in which irrational behaviors may affect a stock market. To test this, we analyzed all the data collected over a 3‐month period in 2011 from Tencent Weibo (one of the largest microblogging websites in China). We designed a social influence (SI)‐based Tencent finance‐related moods model to simulate investors' irrational behaviors, and designed a Tencent Moods‐based Stock Trend Analysis (TM_STA) model to detect correlations between Tencent moods and the Hushen‐300 index (one of the most important financial indexes in China). Experimental results show that the proposed method can help explain the data fluctuation. The findings support the existing behavioral financial theory, and can help to understand short‐term rises and falls in a stock market. We use behavioral financial theory to further explain our findings, and to propose a trading model to verify the proposed model.

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.

Text mining has been widely used in multiple types of user‐generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic‐level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1‐measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.

This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor, user opinions and social influence in a unified probabilistic model with two stages learning processes. In the first stage, topic factor and user influence are integrated to generate users' influential relationship based on different topics; in the second stage, users' historical messages and social interaction records are leveraged by TOIM to construct their historical opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topics. We evaluate our TOIM on a large-scaled dataset from Tencent Weibo, one of the largest microbloggings website in China. The experimental results show that TOIM can better predict users' opinion than other baseline methods.

In microblog like Twitter, popular tweets are usually retweeted by many users. For different tweets, their lifespans (i.e., how long they will stay popular) vary. This paper presents a simple yet effective approach to predict the lifespans of popular tweets based on their static characteristics and dynamic retweeting patterns. For a potentially popular tweet, we generate a time series based on its first-hour retweeting information, and compare it with those of historic tweets of the same author and post time (at the granularity of hour). The top-k historic tweets are identified, whose mean lifespan is estimated as the lifespan of the new tweet. Our experiments on a three-month real data set from Tencent Microblog demonstrate the effectiveness of the approach.