Proceedings of the Ninth ACM International Conference on Web Search and Data Mining最新文献_第8页

Equality and Social Mobility in Twitter Discussion Groups Twitter讨论组中的平等和社会流动性

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835814

Katherine Ellis, M. Goldszmidt, Gert R. G. Lanckriet, Nina Mishra, Omer Reingold

{"title":"Equality and Social Mobility in Twitter Discussion Groups","authors":"Katherine Ellis, M. Goldszmidt, Gert R. G. Lanckriet, Nina Mishra, Omer Reingold","doi":"10.1145/2835776.2835814","DOIUrl":"https://doi.org/10.1145/2835776.2835814","url":null,"abstract":"Online groups, including chat groups and forums, are becoming important avenues for gathering and exchanging information ranging from troubleshooting devices, to sharing experiences, to finding medical information and advice. Thus, issues about the health and stability of these groups are of particular interest to both industry and academia. In this paper we conduct a large scale study with the objectives of first, characterizing essential aspects of the interactions between the participants of such groups and second, characterizing how the nature of these interactions relate to the health of the groups. Specifically, we concentrate on Twitter Discussion Groups (TDGs), self-organized groups that meet on Twitter by agreeing on a hashtag, date and time. These groups have repeated, real-time meetings and are a rising phenomenon on Twitter. We examine the interactions in these groups in terms of the social equality and mobility of the exchange of attention between participants, according to the @mention convention on Twitter. We estimate the health of a group by measuring the retention rate of participants and the change in the number of meetings over time. We find that social equality and mobility are correlated, and that equality and mobility are related to a group's health. In fact, equality and mobility are as predictive of a group's health as some prior characteristics used to predict health of other online groups. Our findings are based on studying 100 thousand sessions of over two thousand discussion groups over the period of June 2012 to June 2013. These finding are not only relevant to stakeholders interested in maintaining these groups, but to researchers and academics interested in understanding the behavior of participants in online discussions. We also find the parallel with findings on the relationship between economic mobility and equality and health indicators in real-world nations striking and thought-provoking.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79518160","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

Session details: Social Events 会话细节:社交事件

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/3253882

Brian D. Davison

引用次数: 0

Scaling up Link Prediction with Ensembles 利用集成扩展链路预测

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835815

Liang Duan, C. Aggarwal, Shuai Ma, Renjun Hu, J. Huai

{"title":"Scaling up Link Prediction with Ensembles","authors":"Liang Duan, C. Aggarwal, Shuai Ma, Renjun Hu, J. Huai","doi":"10.1145/2835776.2835815","DOIUrl":"https://doi.org/10.1145/2835776.2835815","url":null,"abstract":"A network with $n$ nodes contains O(n2) possible links. Even for networks of modest size, it is often difficult to evaluate all pairwise possibilities for links in a meaningful way. Furthermore, even though link prediction is closely related to missing value estimation problems, such as collaborative filtering, it is often difficult to use sophisticated models such as latent factor methods because of their computational complexity over very large networks. Due to this computational complexity, most known link prediction methods are designed for evaluating the link propensity over a specified subset of links, rather than for performing a global search over the entire networks. In practice, however, it is essential to perform an exhaustive search over the entire networks. In this paper, we propose an ensemble enabled approach to scaling up link prediction, which is able to decompose traditional link prediction problems into subproblems of smaller size. These subproblems are each solved with the use of latent factor models, which can be effectively implemented over networks of modest size. Furthermore, the ensemble enabled approach has several advantages in terms of performance. We show the advantage of using ensemble-based latent factor models with experiments on very large networks. Experimental results demonstrate the effectiveness and scalability of our approach.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81437989","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

Inferring Latent Triggers of Purchases with Consideration of Social Effects and Media Advertisements 考虑社会效应和媒体广告的购买潜在诱因推断

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835789

Yusuke Tanaka, Takeshi Kurashima, Y. Fujiwara, Tomoharu Iwata, H. Sawada

引用次数: 22

Modeling and Predicting Learning Behavior in MOOCs mooc学习行为建模与预测

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835842

J. Qiu, Jie Tang, T. Liu, Jie Gong, Chenhui Zhang, Qian Zhang, Yufei Xue

{"title":"Modeling and Predicting Learning Behavior in MOOCs","authors":"J. Qiu, Jie Tang, T. Liu, Jie Gong, Chenhui Zhang, Qian Zhang, Yufei Xue","doi":"10.1145/2835776.2835842","DOIUrl":"https://doi.org/10.1145/2835776.2835842","url":null,"abstract":"Massive Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environment, offer us an unprecedented opportunity to analyze students' learning behavior at a very fine granularity than ever before. Using dataset from xuetangX, one of the largest MOOCs from China, we analyze key factors that influence students' engagement in MOOCs and study to what extent we could infer a student's learning effectiveness. We observe significant behavioral heterogeneity in students' course selection as well as their learning patterns. For example, students who exert higher effort and ask more questions are not necessarily more likely to get certificates. Additionally, the probability that a student obtains the course certificate increases dramatically (3 x higher) when she has one or more \"certificate friends\". Moreover, we develop a unified model to predict students' learning effectiveness, by incorporating user demographics, forum activities, and learning behavior. We demonstrate that the proposed model significantly outperforms (+2.03-9.03% by F1-score) several alternative methods in predicting students' performance on assignments and course certificates. The model is flexible and can be applied to various settings. For example, we are deploying a new feature into xuetangX to help teachers dynamically optimize the teaching process.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78532966","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: 156

Session details: Social Networks 会话细节:社交网络

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/3253879

C. Clarke

引用次数: 0

Session details: Search and Semantics 会话详细信息:搜索和语义

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/3253873

Vanessa Murdock

引用次数: 0

Click Models for Web Search and their Applications to IR: WSDM 2016 Tutorial 点击Web搜索模型及其在IR中的应用:WSDM 2016教程

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2855113

A. Chuklin, I. Markov, M. de Rijke

引用次数: 9

An Information-Theoretic Approach to Individual Sequential Data Sanitization 个体顺序数据处理的信息论方法

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835828

Luca Bonomi, Liyue Fan, Hongxia Jin

引用次数: 18

AMiner: Toward Understanding Big Scholar Data AMiner:迈向理解大学者数据

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835849

Jie Tang

{"title":"AMiner: Toward Understanding Big Scholar Data","authors":"Jie Tang","doi":"10.1145/2835776.2835849","DOIUrl":"https://doi.org/10.1145/2835776.2835849","url":null,"abstract":"In this talk, I will present a novel academic search and mining system, AMiner, the second generation of the ArnetMiner system. Different from traditional academic search systems that focus on document (paper) search, AMiner aims to provide a systematic modeling approach for researchers (authors), ultimately to gain a deep understanding of the big (heterogeneous) network formed by authors, papers they have published, and venues they published those papers. The system extracts researchers' profiles automatically from the Web and integrates the researcher profiles with publication papers after name disambiguation. For now, the system has collected a big scholar data with more than 130,000,000 researcher profiles and 100,000,000 papers from multiple publication databases. We also developed an approach named COSNET to connect AMiner with several professional social networks such as LinkedIn and VideoLectures, which significantly enriches the metadata of the scholarly data. Based on the integrated big scholar data, we devise a unified topic modeling approach for modeling the different entities (authors, papers, venues) simultaneously and provide a topic-level expertise search by leveraging the modeling results. In addition, AMiner offers a set of researcher-centered functions including social influence analysis, influence visualization, collaboration recommendation, relationship mining, similarity analysis and community evolution. The system has been put into operation since 2006 and has attracted more than 7,000,000 independent IP accesses from over 200 countries/regions.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89602957","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: 47