2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第8页

TipMe: Personalized advertising and aspect-based opinion mining for users and businesses TipMe:针对用户和企业的个性化广告和基于方面的意见挖掘

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809324

Dimitris Proios, M. Eirinaki, Iraklis Varlamis

{"title":"TipMe: Personalized advertising and aspect-based opinion mining for users and businesses","authors":"Dimitris Proios, M. Eirinaki, Iraklis Varlamis","doi":"10.1145/2808797.2809324","DOIUrl":"https://doi.org/10.1145/2808797.2809324","url":null,"abstract":"Online advertisements are a major source of profit and customer attraction for web-based businesses. In a successful advertisement campaign, both users and businesses can benefit, as users are expected to respond positively to special offers and recommendations of their liking and businesses are able to reach the most promising potential customers. The extraction of user preferences from content provided in social media and especially in review sites can be a valuable tool both for users and businesses. In this paper, we propose a model for the analysis of content from product review sites, which considers in tandem the aspects discussed by users and the opinions associated with each aspect. The model provides two different visualizations: one for businesses that uncovers their weak and strong points against their competitors and one for end-users who receive suggestions about products of potential interest. The former is an aggregation of aspect-based opinions provided by all users and the latter is a collaborative filtering approach, which calculates user similarity over a projection of the original bipartite graph (user-item rating graph) over a content-based clustering of users and items. The model takes advantage of the feedback users give to businesses in review sites, and employ opinion mining techniques to identify the opinions of users for specific aspects of a business. Such aspects and their polarity can be used to create user and business profiles, which can subsequently be fed in a clustering and recommendation process. We envision this model as a powerful tool for planning and executing a successful marketing campaign via online media. Finally, we demonstrate how our prototype can be used in different scenarios to assist users or business owners, using the Yelp challenge dataset.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114458161","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: 11

Using weak ties to understand resource usage behaviors in an online community of educators 利用弱联系了解在线教育社区的资源使用行为

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809420

Ogheneovo Dibie, T. Sumner

{"title":"Using weak ties to understand resource usage behaviors in an online community of educators","authors":"Ogheneovo Dibie, T. Sumner","doi":"10.1145/2808797.2809420","DOIUrl":"https://doi.org/10.1145/2808797.2809420","url":null,"abstract":"We show that weak ties offer a useful theoretical lens for understanding the sharing and usage of community-contributed resources amongst educators in a large urban school district. Community-contributed resources include a rich variety of teaching and learning resources such as lesson plans, presentation slides, animations and simulations. In this research, we consider whether the deduced relationships between members of the community constitute weak ties. A deduced relationship exists when two community members view or access the same resource. If these deduced relationships do constitute weak ties then other theorized network properties should also be manifest, namely homophily and triadic closures. Our findings support these theoretical conjectures. Firstly, results indicate that the strength of a tie is directly proportional to the level of similarity between users in the network (homophily property). Secondly, we found strong support for the triadic closure property as well; we developed a computational model to predict the formation of weak ties via triadic closures with an accuracy of 97.8%. Insights from our model can be used to improve a collaborative filtering approach for resource recommendation by predicting future similarity between users in the network.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125142070","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

Differentially private publication of social graphs at linear cost 以线性成本进行社交图谱的不同私人发布

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809385

H. H. Nguyen, Abdessamad Imine, M. Rusinowitch

引用次数: 18

Leveraging rating behavior to predict negative social ties 利用评价行为来预测负面的社会关系

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809402

L. Gauthier, Benjamin Piwowarski, P. Gallinari

引用次数: 1

Predicting community evolution based on time series modeling 基于时间序列模型的群落演化预测

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808913

N. Ilhan, Ş. Öğüdücü

{"title":"Predicting community evolution based on time series modeling","authors":"N. Ilhan, Ş. Öğüdücü","doi":"10.1145/2808797.2808913","DOIUrl":"https://doi.org/10.1145/2808797.2808913","url":null,"abstract":"Communities in real life are usually dynamic and community structures evolve over time. Detecting community evolution provides insight into the underlying behavior of the network. A growing body of study is devoted in studying the dynamics of communities in evolving social networks. Most of them provide an event-based framework to characterize and track the community evolution. A part of these studies take a step further and provide a predictive model of the events by exploiting community features. However, the proposed models require the community extraction and computing the community features relevant to the time point to be predicted. In this paper, we proposed a new approach for predicting events by estimating feature values related to the communities in a given network. An event-based framework is used to characterize community behavior patterns. Then, a time series ARIMA model is used to predict how particular community features will change in the following time period. Distinct time windows are examined in constituting and analyzing time series. Our proposed approach efficiently tracks similar communities and identifies events over time. Furthermore, community feature values are forecasted with an acceptable error rate. Event prediction using forecasted feature values substantially match up with actual events.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121591168","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: 29

MUMA: A multiplex network analysis library 一个多路网络分析库

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808804

Issam Falih, R. Kanawati

{"title":"MUMA: A multiplex network analysis library","authors":"Issam Falih, R. Kanawati","doi":"10.1145/2808797.2808804","DOIUrl":"https://doi.org/10.1145/2808797.2808804","url":null,"abstract":"Multiplex network model has been recently proposed as a mean to capture high level complexity in real-world interaction networks. This model, in spite of its simplicity, allows handling multi-relationnal, heterogeneous, dynamic and even attributed networks. However, it requiers redefining and adapting almost all basic metrics and algorithms generally used to analyse complex networks. In this work we present MUNA: a MUltiplex Network Analysis library that we have developed in both R and Python on top of igraph network analysis package. In its current version, MUNA provides primitives to build, edit and modify multiplex networks. It also provides a bunch of functions computing basic metrics on multiplex networks. However, the most interesting functionality provided by MUNA is probably the wide variety of available community detection algorithms. Actually, the library implements different approaches for community detection including: partition aggregation approaches, layer aggregation approaches and direct multiplex approaches such as the GenLouvain and MuxLicod algorithms. It also offers an extended list of multiplex community evaluation indexes.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122596489","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

Identifying disruptive events from social media to enhance situational awareness 从社交媒体中识别破坏性事件，以增强态势感知

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808879

Nasser Alsaedi, P. Burnap, O. Rana

{"title":"Identifying disruptive events from social media to enhance situational awareness","authors":"Nasser Alsaedi, P. Burnap, O. Rana","doi":"10.1145/2808797.2808879","DOIUrl":"https://doi.org/10.1145/2808797.2808879","url":null,"abstract":"Decision makers use information from a range of terrestrial and online sources to help underpin the processes through which they develop policies and react to events as they unfold. One such source of online information is social media. Twitter, as a form of social media, is a popular micro-blogging Web application serving hundreds of millions of users. User-generated content can be exploited as a rich source of information for identifying `real-world' disruptive events. In this paper, we present an in-depth comparison of three types of features that could be useful for identifying disruptive events: temporal, spatial and textual. We make several interesting observations: first, disruptive events are identifiable regardless of the \"influence of the user\" discussing them, and over a variety of topics. Second, temporal features are the best event identifiers and hence should not be disregarded or ignored. Third, a combination of optimum textual features with temporal and spatial features achieves best performance in the event detection task. We believe that these findings provide new insights for gathering information around real-world events as well as a useful resource for improving situational awareness and decision support.","PeriodicalId":371988,"journal":{"name":"2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131534827","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: 15

Time-aware egocentric network-based user profiling 基于时间感知的以自我为中心的基于网络的用户分析

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809415

C. Canut, Sirinya On-at, A. Péninou, F. Sèdes

引用次数: 16

Revealing censored information through comments and commenters in online social networks 通过在线社交网络上的评论和评论透露被审查的信息

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2809290

Giuseppe Cascavilla, M. Conti, D. Schwartz, I. Yahav

引用次数: 9

3D DynNetVis — A 3D visualization technique for dynamic networks 3D DynNetVis -一个动态网络的三维可视化技术

2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2015-08-25 DOI: 10.1145/2808797.2808798

Tilman Göhnert, Sabrina Ziebarth, Henrik Detjen, Tobias Hecking, H. Hoppe

引用次数: 3