2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)最新文献_第2页

Activity-based sampling of Twitter users for temporal prediction models 基于活动的Twitter用户抽样，用于时间预测模型

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804474

S. Aghababaei, E. Gultepe, Iuliia Chepurna, M. Makrehchi

{"title":"Activity-based sampling of Twitter users for temporal prediction models","authors":"S. Aghababaei, E. Gultepe, Iuliia Chepurna, M. Makrehchi","doi":"10.1109/BESC.2016.7804474","DOIUrl":"https://doi.org/10.1109/BESC.2016.7804474","url":null,"abstract":"Increasingly more applications rely on crowd-sourced data from social media. Some of these applications are concerned with real-time data streams, while others are more focused on acquiring temporal footprints from historical timelines of users. Nevertheless, determining the subset of \"credible\" users is crucial. While the majority of sampling approaches focus on individuals' static networks, dynamic user activity over time is usually not considered, which may result in activity gaps in the collected data. Models based on noisy and missing data can significantly degrade in performance. In this study, we demonstrate how to sample Twitter users in order to produce more credible data for temporal prediction models. We present an activity-based sampling approach where users are selected based on their historical activities in Twitter. The predictability of the collected content from activity-based and random sampling is compared in a user-centric temporal model. The results indicate the importance of an activity-oriented sampling method for the acquisition of more credible content for temporal models.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123473753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trading network and systemic risk in the energy market 能源市场的交易网络与系统性风险

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804485

Germán G. Creamer

引用次数: 3

Research on influence of Chinese urbanization on hotel industry development 中国城市化对酒店业发展的影响研究

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804472

H. Yin

引用次数: 0

Employment status and cybersecurity behaviors 就业状况与网络安全行为

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804493

Mohd Anwar, Wu He, Xiaohong Yuan

引用次数: 2

Can social contagion spread without key players? 没有关键角色，社会传染病会蔓延吗?

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804473

Gizem Korkmaz, C. Kuhlman, F. Vega-Redondo

{"title":"Can social contagion spread without key players?","authors":"Gizem Korkmaz, C. Kuhlman, F. Vega-Redondo","doi":"10.1109/BESC.2016.7804473","DOIUrl":"https://doi.org/10.1109/BESC.2016.7804473","url":null,"abstract":"Contagion models have been used to study the spread of social behavior among agents of a population, such as information diffusion, social influence, and participation to collective action (e.g., protests). Key players, which are typically high-degree, -k-core or -centrality agents in a networked population, are considered important for spreading social contagions. In this paper, we ask whether contagions can propagate through a population that is void of key players. We use Erdos-Renyi random graphs as a representation of unstructured populations that lack key players, and investigate whether complex contagions - those requiring reinforcement - can spread on them. We demonstrate that two game-theoretic contagion models that utilize common knowledge for collective action can readily spread such contagions, which is a significant difference from classic complex contagion models. We compare contagion dynamics results on unstructured networks to those on more typically-studied, structured social networks to understand the role of network structure. We test a total of 14 networks. The two common knowledge models are also contrasted to understand the effects of different modeling assumptions on dynamics. We show that under a wide range of conditions, these two models produce markedly different results.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"372 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121747702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Q-matrix learning and DINA model parameter estimation q -矩阵学习和DINA模型参数估计

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804471

Yuan Sun, Shiwei Ye, Guiping Su, Yi Sun

引用次数: 2

Deployment of churn prediction model in financial services industry 客户流失预测模型在金融服务行业的应用

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804486

Charles Chu, Guandong Xu, J. Brownlow, Bin Fu

引用次数: 5

Applying client churn prediction modeling on home-based care services industry 客户流失预测模型在居家护理服务行业的应用

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804503

Raul Manongdo, Guandong Xu

{"title":"Applying client churn prediction modeling on home-based care services industry","authors":"Raul Manongdo, Guandong Xu","doi":"10.1109/BESC.2016.7804503","DOIUrl":"https://doi.org/10.1109/BESC.2016.7804503","url":null,"abstract":"Client churn prediction model is widely acknowledged as an effective way of realizing customer life-time value especially in service-oriented industries and under a competitive business environment. Churn model allows targeting of clients for retention campaigns and is a critical component of customer relationship management(CRM) and business intelligence systems. There are numerous statistical models and techniques applied successfully on data mining projects for various industries. While there is literature for prediction modeling on hospital health care services, non-exist for home-based care services. In this study, logistic regression, random forest and C5.0 decision tree were the models used in building a binary client churn classifier for a home-based care services company based in Australia. All models yielded prediction accuracies over 90% with tree based classifiers marginally higher and C5.0 model found to be suitable for use in this industry. This study also showed that existing client satisfaction measures currently in use by the company does not adequately contribute to churn analysis.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127448491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fractal analysis of complex power load variations through adaptive multiscale filtering 基于自适应多尺度滤波的复杂电力负荷变化分形分析

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804502

Aihua Jiang, Jianbo Gao

{"title":"Fractal analysis of complex power load variations through adaptive multiscale filtering","authors":"Aihua Jiang, Jianbo Gao","doi":"10.1109/BESC.2016.7804502","DOIUrl":"https://doi.org/10.1109/BESC.2016.7804502","url":null,"abstract":"Power load analysis is important for optimizing resource allocation, planning the production of electricity, and predicting power markets. Yet, it is challenging, since load data exhibit both periodic and stochastic features, and is affected by a multitude of factors including social, economic, political, and climatic factors, as well as industrial structure, living standards, and user behaviors. In this paper, we employ a multiscale framework to systematically analyze load data from two electric utilities in two cities of different size in China. The low frequency trend signals in both load data sets are quite irregular. The detrended data of the load time series are further denoised to remove high frequency noise. Fourier spectral analysis of the original and filtered data shows that the load time series has very strong spectral peaks corresponding to a period of one day. Using adaptive fractal analysis, which can best extract fractal behaviors from signals with strong oscillatory trends, we further show that load time series has long-range correlations. Amazingly, maxima of the temporal variations of the long-range correlations correspond well with temperature minima, highlighting that long-range correlations are stronger in winter than in summer.","PeriodicalId":225942,"journal":{"name":"2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134480290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

DGWC: Distributed and generic web crawler for online information extraction DGWC:用于在线信息提取的分布式通用网络爬虫

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Pub Date : 2016-11-01 DOI: 10.1109/BESC.2016.7804487

Lu Zhang, Zhan Bu, Zhiang Wu, Jie Cao

引用次数: 8