Online Social Network In China Research Articles

Unveiling Qzone: A measurement study of a large-scale online social network

Nowadays, Online Social Network (OSN) is very important in maintaining relationships and disseminating information. Qzone, as one of the most popular OSNs in China, lacks detailed quantitative measurement and analysis. To the best of our knowledge, we are the first to conduct a large-scale measurement on Qzone. Firstly, we develop a dedicated Qzone Crawler System (Qzone-CS) and collect over a billion user and topology data. Subsequently, we measure user behavior and network topology characteristics by analyzing their posting and interactive behaviors. The results show that: 1) the temporal distribution of user behavior follows the periodic distribution, while user posting behavior follows the segmented power-law distribution. Among all the user behaviors on Qzone, the probability of posting Shuoshuo is the highest. Meanwhile, users prefer to comment on others’ blogs than to forward them. Unlike other OSNs, Qzone is a relatively closed system, which is not conducive to the dissemination of information. 2) Qzone is a small-world and scale-free community; the friendship network has higher clustering characteristic compared with the interactive network; out-degree in the interactive network has an obvious correlation with in-degree; and unlike other OSNs, Qzone is a heterogeneous network. Our results can help researchers better understand the user behavior and network topology characteristics of Qzone.

Examining online social behavior changes after migration: An empirical study based on OSN big data

With fast urbanization and decreasing transportation cost, migration becomes more common. Previous studies have shown the important role of social networks in the process of migration, but little is known about the effect of migration on social networks. To fill the research gap, this study examines the effect of migration on online social behaviors (in terms of network evolution and social interaction), as well as the moderating effect of migrants' characteristics. We collect a four-month big dataset with 2.29 million records from one of the largest online social networks in China. We apply the propensity score matching combined with the difference-in-differences method to compare online social behavior changes after migration. Our results show that, for network evolution behavior, migration positively impacts on the number of tie formation, but non-significantly impacts on the number of tie decay; for social interaction behavior, migration increases the number of contacts but decreases the number of messages. We also find some moderating effects of migrants’ characteristics, including gender, age, and degree. This study provides big data empirical evidence and some new insights to our understandings of the impact of migration on online social network behavior.

On Power Law Growth of Social Networks

What is the growth dynamics of social networks, like Facebook or WeChat? Does it truly exhibit exponential early-growth, as predicted by the celebrated models, like the Bass model? How about the dynamics of links, for which there are few published models? For the first time, we examine the growth of WeChat which is the largest online social network in China, together with several other real social networks. We observe Power-Law growth dynamics for both nodes and links, a fact that breaks the textbook models featuring Sigmoid curves. We propose NetTide , along with differential equations for the growth of nodes and links. Our model fits the growth dynamics of real social networks well; it encompasses many traditional growth dynamics as special cases, while remaining parsimonious in parameters. The NetTide for link growth is the first one of its kind, accurately fitting real data, and capturing densification phenomenon. We further formulate two stochastic generators, which interpret the growth of nodes and links through survival analysis and micro-level interactions within a social network, respectively. The proposed generators reproduce realistic growth dynamics of social networks. When applied on the WeChat data, our NetTide forecasted $\geq$ 730 days ahead with 3 percent error.

Exploring the diversity of retweeting behavior patterns in Chinese microblogging platform

Information regarding retweeting behavior patterns is crucial for understanding online information diffusion, product promotion, and other social contagion dynamics. In this study, we explored the retweeting behavior patterns of influential microblogs according to detailed retweeting behavior data gathered from Sina Weibo, a Twitter-like online social network in China. We devised an innovative time series reduction method to capture retweeting time series features at the structural level, and observed three types of daily representative temporal patterns and six types of hourly representative temporal patterns. We then conducted comprehensive analyses of each pattern, including the statistical features of the time series and other factors influencing the formation of patterns (e.g., posting users, content, and publication time) of microblogs. Based on readily available social-influential, topical, and temporal factors, we also established a classification model that relatively reliably predicts the temporal class of an original microblog’s retweeting time series. The results presented here may offer insight into common temporal patterns of retweeting behavior on content-based online social networks.

Big Data Analytics of Multi-Relationship Online Social Network Based on Multi-Subnet Composited Complex Network

Online social networks such as Twitter and Facebook are becoming popular form of social information networks. There are frequently many kinds of relationships in an online social network. Complex Network acting as one kind of big data technologies is often used to analyze users' social activities. By studying the Douban network, which is a representative multi-relationship online social network in China, big data of friendship relationship and book comments similar relationship are crawled through network topology measurement software, from the perspective of topological characteristics of complex network, the basic topologies of the two relationship networks constructed individually by the two relationships are analyzed. Based on these, a multi-relationship online social network based on Multi-subnet Composited Complex Network Model is constructed through loading book comments similar relationship subnet to follower relationship subnet, and accurate understanding of topologies of Douban multirelationship network is obtained. These findings provide a deep understanding on the evolution of multi-relationship online social network, and can provide guidelines on how to build an efficient multi-relationship online social network evolution model.

Forecasting Popularity of Videos Using Social Media

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.

Understanding latent interactions in online social networks

Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions , that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.

Information Propagation in Online Social Network Based on Human Dynamics

We investigate the impact of human dynamics on the information propagation in online social networks. First, statistical properties of the human behavior are studied using the data from “Sina Microblog,” which is one of the most popular online social networks in China. We find that human activity patterns are heterogeneous and bursty and are often described by a power-law interevent time distributionP(τ)~τ−α. Second, we proposed an extended Susceptible-Infected (SI) propagation model to incorporate bursty and limited attention. We unveil how bursty human behavior and limited attention affect the information propagation in online social networks. The result in this paper can be useful for optimizing or controlling information propagation in online social networks.