2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing最新文献_第6页

QLIM -- A Tool to Support Collective Intelligence QLIM——支持集体智慧的工具

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SocialCom-PASSAT.2012.56

Yann Veilleroy, F. Hoogstoel, L. Lancieri

引用次数: 1

How Charisma Is Perceived from Speech: A Multidimensional Approach 如何从言语中感知魅力:一个多维的方法

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SOCIALCOM-PASSAT.2012.68

Rosario Signorello, Francesca D’Errico, I. Poggi, Didier Demolin

引用次数: 35

Classifying Trust/Distrust Relationships in Online Social Networks 在线社交网络中信任/不信任关系的分类

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SOCIALCOM-PASSAT.2012.115

G. Bachi, M. Coscia, A. Monreale, F. Giannotti

{"title":"Classifying Trust/Distrust Relationships in Online Social Networks","authors":"G. Bachi, M. Coscia, A. Monreale, F. Giannotti","doi":"10.1109/SOCIALCOM-PASSAT.2012.115","DOIUrl":"https://doi.org/10.1109/SOCIALCOM-PASSAT.2012.115","url":null,"abstract":"Online social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124744188","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: 48

Some Suggestions for the Study of Stance in Communication 对交际立场研究的几点建议

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SocialCom-PASSAT.2012.89

Massimo Chindamo, J. Allwood, E. Ahlsén

引用次数: 43

A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks 城市随机漫步:基于位置的社交网络中的新地点推荐

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SocialCom-PASSAT.2012.70

A. Noulas, S. Scellato, N. Lathia, C. Mascolo

{"title":"A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks","authors":"A. Noulas, S. Scellato, N. Lathia, C. Mascolo","doi":"10.1109/SocialCom-PASSAT.2012.70","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.70","url":null,"abstract":"The popularity of location-based social networks available on mobile devices means that large, rich datasets that contain a mixture of behavioral (users visiting venues), social (links between users), and spatial (distances between venues) information are available for mobile location recommendation systems. However, these datasets greatly differ from those used in other online recommender systems, where users explicitly rate items: it remains unclear as to how they capture user preferences as well as how they can be leveraged for accurate recommendation. This paper seeks to bridge this gap with a three-fold contribution. First, we examine how venue discovery behavior characterizes the large check-in datasets from two different location-based social services, Foursquare and Go Walla: by using large-scale datasets containing both user check-ins and social ties, our analysis reveals that, across 11 cities, between 60% and 80% of users' visits are in venues that were not visited in the previous 30 days. We then show that, by making constraining assumptions about user mobility, state-of-the-art filtering algorithms, including latent space models, do not produce high quality recommendations. Finally, we propose a new model based on personalized random walks over a user-place graph that, by seamlessly combining social network and venue visit frequency data, obtains between 5 and 18% improvement over other models. Our results pave the way to a new approach for place recommendation in location-based social systems.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129824145","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: 200

Inferring Realistic Intra-hospital Contact Networks Using Link Prediction and Computer Logins 使用链接预测和计算机登录推断现实的医院内联系网络

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SocialCom-PASSAT.2012.113

Ted Herman, M. Monsalve, S. Pemmaraju, P. Polgreen, Alberto Maria Segre, Deepti Sharma, G. Thomas

{"title":"Inferring Realistic Intra-hospital Contact Networks Using Link Prediction and Computer Logins","authors":"Ted Herman, M. Monsalve, S. Pemmaraju, P. Polgreen, Alberto Maria Segre, Deepti Sharma, G. Thomas","doi":"10.1109/SocialCom-PASSAT.2012.113","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.113","url":null,"abstract":"Disease spread in hospital settings is a common and important problem in health care. Knowing the network of contacts between health care workers and patients can be very helpful in mitigating disease spread. In this work, we address the problem of inferring the contact network of health care workers at the University of Iowa Hospital and Clinics facilities by integrating two sources of data: hospital-wide computer login data and proximity data obtained from direct measurement in the Medical Intensive Care Unit using a wireless sensor network. We treat this problem as a variant of the network completion problem, where one small portion of the network is well known while the rest is sparingly sampled, and we want to complete the network. In this case, we want to transform the login network, where an edge connects two people who logged into computers within some time and distance, of the hospital into a contact network. We solve this problem by borrowing techniques from link prediction. We train and evaluate these techniques on synthetic login networks and contact networks obtained from the sensor data. Our results are promising in that we can predict contact networks from login networks with accuracies mostly between 70% and 90%.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121658916","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: 9

Non-linear Dimensionality Reduction for Privacy-Preserving Data Classification 保护隐私数据分类的非线性降维方法

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SocialCom-PASSAT.2012.76

Khaled F. Alotaibi, V. J. Rayward-Smith, Wenjia Wang, B. Iglesia

{"title":"Non-linear Dimensionality Reduction for Privacy-Preserving Data Classification","authors":"Khaled F. Alotaibi, V. J. Rayward-Smith, Wenjia Wang, B. Iglesia","doi":"10.1109/SocialCom-PASSAT.2012.76","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.76","url":null,"abstract":"Many techniques have been proposed to protect the privacy of data outsourced for analysis by external parties. However, most of these techniques distort the underlying data properties, and therefore, hinder data mining algorithms from discovering patterns. The aim of Privacy-Preserving Data Mining (PPDM) is to generate a data-friendly transformation that maintains both the privacy and the utility of the data. We have proposed a novel privacy-preserving framework based on non-linear dimensionality reduction (i.e. non-metric multidimensional scaling) to perturb the original data. The perturbed data exhibited good utility in terms of distance-preservation between objects. This was tested on a clustering task with good results. In this paper, we test our novel PPDM approach on a classification task using a k-Nearest Neighbour (k-NN) classification algorithm. We compare the classification results obtained from both the original and the perturbed data and find them to be much same particularly for the few lower dimensions. We show that, for distance-based classification, our approach preserves the utility of the data while hiding the private details.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125847772","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: 22

Credibility in Context: An Analysis of Feature Distributions in Twitter 语境中的可信度:Twitter特征分布分析

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SocialCom-PASSAT.2012.128

J. O'Donovan, Byungkyu Kang, Greg Meyer, Tobias Höllerer, Sibel Adali

{"title":"Credibility in Context: An Analysis of Feature Distributions in Twitter","authors":"J. O'Donovan, Byungkyu Kang, Greg Meyer, Tobias Höllerer, Sibel Adali","doi":"10.1109/SocialCom-PASSAT.2012.128","DOIUrl":"https://doi.org/10.1109/SocialCom-PASSAT.2012.128","url":null,"abstract":"Twitter is a major forum for rapid dissemination of user-provided content in real time. As such, a large proportion of the information it contains is not particularly relevant to many users and in fact is perceived as unwanted 'noise' by many. There has been increased research interest in predicting whether tweets are relevant, newsworthy or credible, using a variety of models and methods. In this paper, we focus on an analysis that highlights the utility of the individual features in Twitter such as hash tags, retweets and mentions for predicting credibility. We first describe a context-based evaluation of the utility of a set of features for predicting manually provided credibility assessments on a corpus of microblog tweets. This is followed by an evaluation of the distribution/presence of each feature across 8 diverse crawls of tweet data. Last, an analysis of feature distribution across dyadic pairs of tweets and retweet chains of various lengths is described. Our results show that the best indicators of credibility include URLs, mentions, retweets and tweet length and that features occur more prominently in data describing emergency and unrest situations.","PeriodicalId":129526,"journal":{"name":"2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128252181","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: 112

Approximating Shortest Paths in Spatial Social Networks 空间社会网络中的最短路径逼近

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SocialCom-PASSAT.2012.132

C. Ratti, Christian Sommer

引用次数: 2

Mimicking Expressiveness of Movements by Autistic Children in Game Play 自闭症儿童在游戏中对动作表达的模仿

2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing Pub Date : 2012-09-03 DOI: 10.1109/SocialCom-PASSAT.2012.100

D. Tetteroo, A. Shirzad, M. Pereira, M. J. Zwinderman, Duy Le, E. Barakova

引用次数: 7