An Empirical Study of a Chinese Online Social Network--Renren

Niu, Jianwei; Peng, Jing; Shu, Lei; Tong, Chao; Liao, Wanjiun

doi:10.1109/mc.2013.1

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…Pentland (2014) describes such methods as "social physics" which use mathematical models to understand how ideas flow through networks. Online social network analysis, sentiment analysis, data mining, and other computation research methods have vastly expanded our understanding of the circumstances, characteristics of users, and messages that contribute or disrupt collective action and information flows traveling through social networks (see, for example, Niu et al 2013, Wang et al 2014. The specific circumstances and characteristics of users and messages that shape social change, evolution, and learning have yet to be studied more in depth for China.…”

Section: Insert Figures 1 and 2 About Herementioning

confidence: 99%

Towards Area-Smart Data Science: Critical Questions for Working with Big Data from China

Stockmann

2016

SSRN Journal

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Section: Insert Figures 1 and 2 About Herementioning

confidence: 99%

Towards Area-Smart Data Science: Critical Questions for Working with Big Data from China

Stockmann

2016

SSRN Journal

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“…There is a huge amount of research literature dealing with diverse aspects of the analysis of on-line social networks (OSN). Classical research efforts are devoted to identify communities within the network [ 14 , 39 , 64 ], finding influencers or key members of the virtual community [ 6 , 10 , 29 , 31 , 34 , 47 , 63 , 72 ], or describing the evolution of specific networks [ 25 , 54 , 62 ]. There is, however, very little or no work on the actual decision process conducting a user to publish some content in the OSN, e.g., posting a message in a forum of a virtual community of practice (VCoP).…”

Section: Introductionmentioning

confidence: 99%

Neuro-semantic prediction of user decisions to contribute content to online social networks

Cleveland

Ríos

Aguilera

et al. 2022

Neural Comput & Applic

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Understanding at microscopic level the generation of contents in an online social network (OSN) is highly desirable for an improved management of the OSN and the prevention of undesirable phenomena, such as online harassment. Content generation, i.e., the decision to post a contributed content in the OSN, can be modeled by neurophysiological approaches on the basis of unbiased semantic analysis of the contents already published in the OSN. This paper proposes a neuro-semantic model composed of (1) an extended leaky competing accumulator (ELCA) as the neural architecture implementing the user concurrent decision process to generate content in a conversation thread of a virtual community of practice, and (2) a semantic modeling based on the topic analysis carried out by a latent Dirichlet allocation (LDA) of both users and conversation threads. We use the similarity between the user and thread semantic representations to built up the model of the interest of the user in the thread contents as the stimulus to contribute content in the thread. The semantic interest of users in discussion threads are the external inputs for the ELCA, i.e., the external value assigned to each choice.. We demonstrate the approach on a dataset extracted from a real life web forum devoted to fans of tinkering with musical instruments and related devices. The neuro-semantic model achieves high performance predicting the content posting decisions (average F score 0.61) improving greatly over well known machine learning approaches, namely random forest and support vector machines (average F scores 0.19 and 0.21).

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“…Some scholars have proposed that we need to understand the scale-free properties and evolutionary origins of complex networks from a new perspective [16][17][18] . While the tail of the degree distribution may be approximated as a power law for many real-world networks [19][20][21][22][23][24][25] , the bulk (the small-degree end) tends to bend off in various networks such as Facebook (friendships network), Google (informational network), the patent network of USA (technological network), etc 17,26 . In this work, we will propose a model for network evolution with an emergent degree distribution that fits observations throughout the degree range, including both the tail and the bulk.…”

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confidence: 99%

The nature and nurture of network evolution

Zhou,

Holme,

Gong

et al. 2023

Nat Commun

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Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node’s weight, reflecting its innate attractiveness (nature), and the node’s degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks.

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An Empirical Study of a Chinese Online Social Network--Renren

Cited by 12 publications

References 11 publications

Towards Area-Smart Data Science: Critical Questions for Working with Big Data from China

Towards Area-Smart Data Science: Critical Questions for Working with Big Data from China

Neuro-semantic prediction of user decisions to contribute content to online social networks

The nature and nurture of network evolution

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