Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation.

Internet of People (IoP), which focuses on personal information collection by a wide range of the mobile applications, is the next frontier for Internet of Things (IoT). Nowadays, people become more and more dependent on the Internet, increasingly receiving and sending information on social networks (e.g., Twitter, etc.); thus social networks play a decisive role in IoP. Therefore, community discovery has emerged as one of the most challenging problems in social networks analysis. To this end, many algorithms have been proposed to detect communities in static networks. However, microblogging social networks are extremely dynamic in both content distribution and topological structure. In this paper, we propose a model EEUICD (Efficient Evolutionary User Interest Community Discovery) which employs a nature-inspired genetic algorithm to improve the quality of community discovery. Specifically, a preprocessing method based on HITS (Hypertext Induced Topic Search) improves the quality of initial users and posts, and a label propagation method is used to restrict the conditions of the mutation process to further improve the efficiency and effectiveness of user interest community detection. Finally, the experiments on the real datasets validate the effectiveness of the proposed model.

Online social networks (OSNs) have emerged as a major platform for sharing information through social relationships and are one of the major sources of big data. Social networks can even accommodate sharing of live streaming data among the connected users. However, social information on social networks is often locally exploited rather than capturing the changes in the entire network over time. Obtaining user's influence statistics is limited only in their local vicinity, which may not facilitate capturing the changes in the user and post influences across the entire network, thereby resulting in lower accuracy while measuring user's topical influence. Moreover, low-influence users always exist in the network publishing low-quality posts. With the objectives of accurately capturing highly influential users and posts, this article proposes a novel dynamic social sensing model, named dynamic PageRank (DPRank) model, to evaluate the dynamic topical influence of the users of social information on social networks during the social information evolution. We deploy our proposed model to real-world Twitter data sets, which demonstrates the effectiveness of our proposed model against notable existing methods while identifying the true influence of users and posts in a dynamically evolving social network.

Recommendation systems are gaining popularity with the proliferation of the Internet of People (IoP). The popularity and use of online social networks facilitate integrating these social relationships with recommender systems under a single framework of IoP. This article proposes a new approach for item recommendation based on the diffusion method that combines user relationships in social networks with user–item relationships derived from the IoP. Especially, a resource redistribution process is explored in the user–object network that gives mass diffusion a higher recommendation accuracy and heat conduct a greater diversity by considering the social degree of users whilst calculating the user degree in the network. A tuning parameter is introduced to adjust the weight of resources that the objects finally receives from users based on their social relationships. Finally, extensive experiments conducted on the real-world datasets which contain friendship relationships, demonstrate the efficiencies of our proposed method in achieving notable performance improvements in terms of the recommendation accuracy, service diversity, and practical dependability.

There has been much interest in link prediction research with significant studies on how to predict missing links or future links in a network based on observed information. However, the key solution to tackle the link prediction problem is how to measure the similarity between the nodes in a network with higher accuracy. Several techniques have been proposed that utilize the similarity between nodes to estimate their proximity in the network. In this paper, an efficient link prediction algorithm that predicts relationships between links using the network structure is proposed. This algorithm uses common neighbors in addition to the degree distribution of the nodes to estimate the possibility of the presence of a link between two nodes in a network based on local information. Extensive experiments are carried out and compared with 12 standard similarity‐based methods using seven real‐world datasets. The experimental results show that our method has higher prediction accuracy compared with most of the local information based methods like the Common Neighbor and Preferential Attachment. It is also competitive with the quasi‐local indicators such as LP and global indicators like Katz, with a lower computational complexity than the two.

The social network is a huge source of information, which plays an increasingly crucial role in people’s daily lives. As a form of online social network management, much information can be discovered via posts, which allows people to exchange and propagate real-life events. Multi-source event propagation involves relevant posts of interesting topics from some key users to others in microblogging network users for network management. However, there are many noisy data in traditional microblogging network management. Meanwhile few people study the spontaneous transmission of events in microblogging network management, as well as the cooperation and competition among multiple event sources. To this end, the event detection and multi-source propagation model, is established. Specifically, for efficient and accurate result of the hot event detection and propagation, we obtain the information of previous event detection and propagation to create some experience sets for the intelligent event propagation. And a multi-source events propagation model based on individual interest is established to describe the process of multi-source event information detection and dissemination, and to describe the key role of users and information characteristics in the process of communication and network management. Meanwhile, the experimental results show that the proposed intelligent multi-source events detection and propagation model can learn from previous propagation to better discover and propagate the hot events under users’ changing interest. Besides, the interaction broadens the influence scope of hot events. This helps to explain the formation of microblogging hot events dissemination, to provide a theoretical basis for the research and network management of the guiding strategy.