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2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)最新文献

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Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? 用情感检测Twitter上的机器人:人类比机器人更固执己见吗?
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921650
John P. Dickerson, Vadim Kagan, V. S. Subrahmanian
{"title":"Using sentiment to detect bots on Twitter: Are humans more opinionated than bots?","authors":"John P. Dickerson, Vadim Kagan, V. S. Subrahmanian","doi":"10.1109/ASONAM.2014.6921650","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921650","url":null,"abstract":"In many Twitter applications, developers collect only a limited sample of tweets and a local portion of the Twitter network. Given such Twitter applications with limited data, how can we classify Twitter users as either bots or humans? We develop a collection of network-, linguistic-, and application-oriented variables that could be used as possible features, and identify specific features that distinguish well between humans and bots. In particular, by analyzing a large dataset relating to the 2014 Indian election, we show that a number of sentimentrelated factors are key to the identification of bots, significantly increasing the Area under the ROC Curve (AUROC). The same method may be used for other applications as well.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127184528","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: 260
Measuring UK crime gangs 衡量英国犯罪团伙
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921592
G. Oatley, Tom Crick
{"title":"Measuring UK crime gangs","authors":"G. Oatley, Tom Crick","doi":"10.1109/ASONAM.2014.6921592","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921592","url":null,"abstract":"This paper describes the output of a study to tackle the problem of gang-related crime in the UK; we present the intelligence and routinely gathered data available to a UK regional police force, and describe an initial social network analysis of gangs in the Greater Manchester area of the UK between 2000-2006. By applying social network analysis techniques, we attempt to detect the birth of two new gangs based on local features (modularity, cliques) and global features (clustering coefficient). Thus for the future, identifying the changes in these can help us identify the possible birth of new gangs (sub-networks) in the social system. Furthermore, we study the dynamics of these networks globally and locally, and have identified the global characteristics that tell us that they are not random graphs - they are small world graphs - implying that the formation of gangs is not a random event. However, we are not yet able to conclude anything significant about scale-free characteristics due to insufficient sample size.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121869625","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: 10
Social networks and quality of life: The national health interview survey 社会网络和生活质量:全国健康访谈调查
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921644
Azadeh Hemmati, K. S. Chung
{"title":"Social networks and quality of life: The national health interview survey","authors":"Azadeh Hemmati, K. S. Chung","doi":"10.1109/ASONAM.2014.6921644","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921644","url":null,"abstract":"Abundant studies document the importance of social support on quality of life (QOL). These studies mainly focus on individualistic and social capital perspectives. Studies showing how structural, dyadic and network level perspectives actually influence QOL remain lacking. In this study, we develop a theoretical model based on social network theories and the QOL model to understand how social support would influence QOL in the context of cancer patients. Using the U.S. National Health Interview Survey 2010, we (i) demonstrate how relational data is extracted for (ii) investigate the association between egocentric network properties (structure, position and relations) and overall QOL. Results show that there are significant differences in the network properties (density, degree, tie strength, efficiency and constraint) of those experiencing high and low QOL. These findings are critical to influencing interventions and policy development for enhanced QOL in cancer care.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126511693","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: 3
Social networks analysis and perception of social support of young mothers in the process of reconstruction: The social fabric of winter victims in the Colombian Caribbean (South America) 重建过程中年轻母亲社会支持的社会网络分析与感知:哥伦比亚加勒比地区(南美)冬季受害者的社会结构
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921601
Carolina S. Castro, Camilo A. Madariaga
{"title":"Social networks analysis and perception of social support of young mothers in the process of reconstruction: The social fabric of winter victims in the Colombian Caribbean (South America)","authors":"Carolina S. Castro, Camilo A. Madariaga","doi":"10.1109/ASONAM.2014.6921601","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921601","url":null,"abstract":"This study describes and analyzes social networks and perception of social support of a group of young mothers affected by winter 2010. The objective was to determine the relationship between the structural and functional characteristics of social networks measured by the questionnaire of Analysis of Personal Networks and social support of Maya Jariego, I. & Holgado, D. (2005). In the analysis of the results correlations of the R of Pearson were done in order to prove the relations between variables. The results of this study show a strong and statistically significant correlation between network measures and social support resources: High levels of support were correlated with moderate indicators of centrality of networks. Families are the most important sources of support for young mothers to recover from the disaster.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123025131","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
A backbone extraction method with Local Search for complex weighted networks 基于局部搜索的复杂加权网络主干提取方法
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921564
Zhan Bu, Zhiang Wu, Liqiang Qian, Jie Cao, Guandong Xu
{"title":"A backbone extraction method with Local Search for complex weighted networks","authors":"Zhan Bu, Zhiang Wu, Liqiang Qian, Jie Cao, Guandong Xu","doi":"10.1109/ASONAM.2014.6921564","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921564","url":null,"abstract":"The backbone is the natural abstraction of a complex network, which can help people to understand it in a more simplified form. Backbone extraction becomes more challenging as many networks are evolving into large scale and the weight distributions are spanning several orders of magnitude. Traditional filter-based methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency-the exhaustive search of all nodes or edges is often prohibitively expensive. In this work, we propose a Local Search based Backbone Extraction Heuristic (LS-BEH) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way. Experimental results on two real-life networks demonstrate the advantage of LS-BEH over the classic disparity filter method by either effectiveness or efficiency validity.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129429741","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: 4
Exploring user expertise and descriptive ability in community question answering 探索用户在社区问题回答中的专业知识和描述能力
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921604
Baoguo Yang, S. Manandhar
{"title":"Exploring user expertise and descriptive ability in community question answering","authors":"Baoguo Yang, S. Manandhar","doi":"10.1109/ASONAM.2014.6921604","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921604","url":null,"abstract":"The research on community question answering (CQA) has been paid increasing attention in recent years. In CQA, to reduce the number of unanswered questions and the time for askers to wait, it is very necessary to identify relevant experts or best answers for these questions. Generally, the experts' answers are more likely to be the best answers. Existing studies considered that user expertise is reflected by the voting scores of both answers and questions. However, voting scores of questions are not really related to user expertise. In this paper, we proposed a new probabilistic model to depict users' expertise based on answers and their descriptive ability based on questions. To exploit social information in CQA, the link analysis is also considered. Extensive experiments on the large Stack Overflow dataset demonstrate that our methods can achieve comparable or even better performance than the state-of-the-art models.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133338312","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: 23
Field selection for job categorization and recommendation to social network users 领域选择的工作分类和推荐给社会网络用户
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921646
E. Malherbe, M. Diaby, Mario Cataldi, E. Viennet, Marie-Aude Aufaure
{"title":"Field selection for job categorization and recommendation to social network users","authors":"E. Malherbe, M. Diaby, Mario Cataldi, E. Viennet, Marie-Aude Aufaure","doi":"10.1109/ASONAM.2014.6921646","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921646","url":null,"abstract":"Nowadays, in the Web 2.0 reality, one of the most challenging task for companies that aim to manage and recommend job offers is to convey this enormous amount of information in a succinct and intelligent manner such to increase the performances of matching operations against users profiles/curricula and optimize the time/space complexity of these processes. With this goal, this paper presents a novel method to formalize the textual content of job offers that aims at identifying the most relevant information and fields expressed by them and leverage this compact formalization for job recommendation and profile matching in social network environments. This method has been then developed and tested in the industrial environment represented by Multiposting and Work4, world leaders in digital solutions of e-recruitment problems. In this study three classes of documents are considered: job offers, job categories and social network user profiles (as potential job candidates); each class contains several fields with textual information. The proposed representation method permits to dynamically identify those text fields, for each class, that could help a cross-matching strategy in order to preserve, from one hand, the matching/recommendation performances and, on the other hand, reduce the cost of these operations (due to a straightforward dimensionality reduction mechanism). We then evaluated and compared the presented approach showing significant improvements on both categorization and recommendation tasks by also drastically reducing their computational costs.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133867263","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: 38
A unified modularity by encoding the similarity attraction feature into the null model 通过将相似吸引特征编码到零模型中实现统一的模块化
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921636
Xin Liu, T. Murata, Ken Wakita
{"title":"A unified modularity by encoding the similarity attraction feature into the null model","authors":"Xin Liu, T. Murata, Ken Wakita","doi":"10.1109/ASONAM.2014.6921636","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921636","url":null,"abstract":"Modularity is a widely used measure for evaluating community structure in networks. The definition of modularity involves a comparison between the observed network and a null model, which serves as a reference. To make the comparison significant, this null model should characterize some features of the observed network. However, the previously used null models are not good representations of real-world networks. A common feature of many real-world networks is similarity attraction, i.e., nodes that are similar have a higher chance of getting connected. We propose a new null model that captures this feature. Based on our null model, we create a unified measure Dist-Modularity, which incorporates the famous Newman-Girvan modularity as a special case. We use three examples to demonstrate that Dist-Modularity is useful in detecting 1) the multi-resolution communities and 2) the geographically dispersed communities.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134135890","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
Community-based cheater detection in location-based social networks 基于位置的社交网络中基于社区的作弊检测
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921698
Wenjie Fan, W. Fan, S. Liao, A. Yeung
{"title":"Community-based cheater detection in location-based social networks","authors":"Wenjie Fan, W. Fan, S. Liao, A. Yeung","doi":"10.1109/ASONAM.2014.6921698","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921698","url":null,"abstract":"Location-based social networks provide services that allow users to share their locations with friends. To attract users and keep them active, social networks or venue holders may offer some awards. But some users make fake check-ins to achieve these awards. These cheaters cause monetary loss and decrease the accuracy of venue recommendations. In this paper, we study users of Foursquare, a popular location-based social network. Behaviors of cheaters and normal users are discussed. Two types of connections are defined to construct graphs of these users. And we propose a method to find cheaters using community structure of the constructed graphs. Our results verify that this cheater detection method is effective and costs little.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134289634","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
OpenKN: An open knowledge computational engine for network big data OpenKN:面向网络大数据的开放式知识计算引擎
2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) Pub Date : 2014-08-17 DOI: 10.1109/ASONAM.2014.6921655
Yantao Jia, Yuanzhuo Wang, Xueqi Cheng, Xiaolong Jin, J. Guo
{"title":"OpenKN: An open knowledge computational engine for network big data","authors":"Yantao Jia, Yuanzhuo Wang, Xueqi Cheng, Xiaolong Jin, J. Guo","doi":"10.1109/ASONAM.2014.6921655","DOIUrl":"https://doi.org/10.1109/ASONAM.2014.6921655","url":null,"abstract":"With the coming of the era of big data, it is most urgent to establish the knowledge computational engine for the purpose of discovering implicit and valuable knowledge from the huge, rapidly dynamic, and complex network data. In this paper, we first survey the mainstream knowledge computational engines from four aspects and point out their deficiency. To cover these shortages, we propose the open knowledge network (OpenKN), which is a self-adaptive and evolutionable knowledge computational engine for network big data. To the best of our knowledge, this is the first work of designing the end-to-end and holistic knowledge processing pipeline in regard with the network big data. Moreover, to capture the evolutionable computing capability of OpenKN, we present the evolutionable knowledge network for knowledge representation. A case study demonstrates the effectiveness of the evolutionable computing of OpenKN.","PeriodicalId":143584,"journal":{"name":"2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124650710","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: 18
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