Behavior analysis of low-literate users of a viral speech-based telephone service

Wang, Haohan; Raza, Agha Ali; Lin, Yibin; Rosenfeld, Roni

doi:10.1145/2537052.2537062

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…Wang et al [27] studied the evolution of user behavior over time, and found a behavior influence analysis of low-literate users of a viral speech based telephone service. Individual influence is based on user behavior.…”

Section: Related Workmentioning

confidence: 99%

A user behavior influence model of social hotspot under implicit link

Xiao

et al. 2017

Information Sciences

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In social networks, user behavior is affected by complex dynamic factors. Here, we investigate the internal and external factors that drive users to participate in social hotspots. By analyzing user behavior, we discover the differences between driving factors and quantify their driving strength. First, four factors that influence the user's behavior are proposed, including explicit links(E), implicit links (I), personal interest(P), and a random factor(R). In particular, based on a cloud model, an implicit link creation method is designed. This method can quantify the driving strength of the implicit relation between users, and avoid the multiple attribute weighting defects in subjective and objective aspects. Next, considering the maximum likelihood estimation theory, a user behavior influence model(EIPR) of a hotspot topic is proposed to measure the causes of user behavior behind the social hotspots. Experimental results show that the model can be used to find different dynamic factors of user behavior in social hot topics. Among these external factors, the implicit link plays an significantly important role in driving user behavior.

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Section: Related Workmentioning

confidence: 99%

A user behavior influence model of social hotspot under implicit link

Xiao

et al. 2017

Information Sciences

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“…The missing link problem has important ramifications because missing links can alter estimates of network-level statistics [4] , and our research focus is missing link prediction. Link prediction not only can help us understand the evolution mechanism of networks [5], but also has crucial application value in many fields [6][7][8][9]. Although link prediction has important research significance and positive research results have been achieved, some challenges still remain.…”

Section: Introductionmentioning

confidence: 99%

Correlations multiplexing for link prediction in multidimensional network spaces

Xiao

Liu

et al. 2018

Sci. China Inf. Sci.

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Uncovering the network evolution mechanism by link prediction SCIENTIA SINICA Physica, Mechanica & Astronomica 41, 816 (2011); Link prediction in social networks: the state-of-the-art SCIENCE CHINA Information Sciences 58, 011101 (2015); Exploring link generation in a temporal evolution network for human mobility SCIENCE CHINA Technological Sciences 59, 16 (2016); Network selection based on available link bandwidth in multi-access networks 数字通信和网络 2, 15 (2016); SCIENCE CHINA Information Sciences

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“…Thus, the discovered relationship is a spurious relationship between X and Y , which happens more than often in real-world. For H. Wang example, a biological process may be correlated with the temperature or humidity of when experiments are conducted [19], or a psychological reaction may be associated with cultural or demographic information of where data is collected [31]. Similar circumstances also apply to GWAS [21], where samples in the same batch presumably share some genotypic patterns or phenotype properties, resulting in the discovered association to be a spurious one.…”

Section: Introductionmentioning

confidence: 99%

Multiple Confounders Correction with Regularized Linear Mixed Effect Models, with Application in Biological Processes

Wang

Yang

2016

Preprint

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Abstract-In this paper, we inspect the performance of regularized linear mixed effect models, as an extension of linear mixed effect model, when multiple confounding factors coexist. We first review its parameter estimation algorithms before we introduce three different methods for multiple confounding factors correction, namely concatenation, sequence, and interpolation. Then we investigate the performance on variable selection task and predictive task on three different data sets, synthetic data set, semi-empirical synthetic data set based on genome sequences and brain wave data set connecting to confused mental states. Our results suggest that sequence multiple confounding factors corrections behave the best when different confounders contribute equally to response variables. On the other hand, when various confounders affect the response variable unevenly, results mainly rely on the degree of how the major confounder is corrected.

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Behavior analysis of low-literate users of a viral speech-based telephone service

Cited by 9 publications

References 20 publications

A user behavior influence model of social hotspot under implicit link

A user behavior influence model of social hotspot under implicit link

Correlations multiplexing for link prediction in multidimensional network spaces

Multiple Confounders Correction with Regularized Linear Mixed Effect Models, with Application in Biological Processes

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