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Emergency incidents can trigger heated discussions on microblogging platforms, and a great number of tweets related to emergency incidents are retweeted by users. Consequently, social media big data related to the emergency incidents is generated from various social media platforms, which can be used to predict users’ retweeting behavior. In this paper, the characteristics of individuals’ retweeting behaviors in emergency incidents are analyzed, and then 11 important characteristics are extracted from recipient characteristics, retweeter characteristics, tweet content characteristics, and external media coverage. A back propagation neural network (BPNN) model called PRBBP is used to predict retweeting behavior in such emergency incidents. Based on PRBBP, an algorithm called PRABP is proposed to predict the number of retweets in emergency incidents. The experiments are performed on a large-scale dataset crawled from Sina weibo. The simulation results show that both the PRBBP model and the PRABP algorithm proposed by this paper have excellent predictive performance.

Microblogging platforms like Twitter, Tumblr and Plurk have radically changed our lives. The presence of millions of people has made these platforms a preferred channel for communication. A large amount of User Generated Content, on these platforms, has attracted researchers and practitioners to mine and extract information nuggets. For information extraction, clustering is an important and widely used mining operation. This paper addresses the issue of clustering of micro-messages and corresponding users based on the text content of micro-messages that reflect their primitive interest. In this paper, we performed modification of the Similarity Upper Approximation based clustering algorithm for clustering of micro-messages. We compared the performance of the modified Similarity Upper Approximation based clustering algorithm with state-of-the-art clustering algorithms such as Partition Around Medoids, Hierarchical Agglomerative Clustering, Affinity Propagation Clustering and DBSCAN. Experiments were performed on micro-messages collected from Twitter. Experimental results show the effectiveness of the proposed algorithm.

Microblogging is a type of blog used by people to express their opinions, attitudes, and feelings toward entities with a short message and this message is easily shared through the network of connected people. Knowing their sentiments would be beneficial for decision-making, planning, visualization, and so on. Grouping similar microblogging messages can convey some meaningful sentiments toward an entity. This task can be accomplished by using a simple and fast clustering algorithm, $K$-means. As the microblogging messages are short and noisy they cause high sparseness and high-dimensional dataset. To overcome this problem, term frequency–inverse document frequency (tf–idf) technique is employed for selecting the relevant features, and singular value decomposition (SVD) technique is employed for reducing the high-dimensional dataset while still retaining the most relevant features. These two techniques adjust dataset to improve the $K$-means efficiently. Another problem comes from $K$-means itself. $K$-means result relies on the initial state of centroids, the random initial state of centroids usually causes convergence to a local optimum. To find a global optimum, artificial bee colony (ABC), a novel swarm intelligence algorithm, is employed to find the best initial state of centroids. Silhouette analysis technique is also used to find optimal $K$. After clustering into $K$ groups, each group will be scored by SentiWordNet and we analyzed the sentiment polarities of each group. Our approach shows that combining various techniques (i.e., tf–idf, SVD, and ABC) can significantly improve $K$-means result (41% from normal $K$-means).

This tutorial presents topic models for organizing and comparing documents. The technique and corresponding discussion focuses on analysis of short text documents, particularly micro-blogs. However, the base topic model and R implementation are generally applicable to text analytics of document databases.

In two articles published in 2003, I developed a preliminary assessment of the relationship between the internet and civil society in China. On the basis of analyses of data from the mid-1990s to the early 2000s, I made the following propositions:

• The internet influences civil society development by fostering public debate, enhancing civic organizing, and facilitating popular protest (Yang, 2003a).

• Civil society facilitates the development of the internet by providing the necessary social basis for communication and interaction (Yang, 2003a).

• The internet and civil society have an interdependent relationship and shape each other in a co-evolutionary trajectory (Yang, 2003b).

Almost ten years have passed. What has changed and what has not? In this chapter, I reassess the development of the internet and civil society in China in light of new developments since 2003. I argue that the internet and civil society have continued to follow their co-evolutionary path and that despite setbacks, both the internet and civil society have expanded and their influences have grown. Paradoxically, because of their growing social impact, both are also subject to new forms of political control and commercial manipulation.