Proceedings of the Eighth ACM International Conference on Web Search and Data Mining最新文献

Session details: Keynote Address 会议详情:主题演讲

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/3251090

Xueqi Cheng

引用次数: 0

Leveraging In-Batch Annotation Bias for Crowdsourced Active Learning 利用批处理注释偏差进行众包主动学习

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/2684822.2685301

Honglei Zhuang, Joel Young

引用次数: 29

WorkerRank: Using Employer Implicit Judgements to Infer Worker Reputation WorkerRank:利用雇主内隐判断推断员工声誉

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/2684822.2685286

Maria Daltayanni, L. D. Alfaro, Panagiotis Papadimitriou

引用次数: 34

Modeling and Predicting Retweeting Dynamics on Microblogging Platforms 微博平台转发动态建模与预测

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/2684822.2685303

Shuai Gao, Jun Ma, Zhumin Chen

{"title":"Modeling and Predicting Retweeting Dynamics on Microblogging Platforms","authors":"Shuai Gao, Jun Ma, Zhumin Chen","doi":"10.1145/2684822.2685303","DOIUrl":"https://doi.org/10.1145/2684822.2685303","url":null,"abstract":"Popularity prediction on microblogging platforms aims to predict the future popularity of a message based on its retweeting dynamics in the early stages. Existing works mainly focus on exploring effective features for prediction, while ignoring the underlying arrival process of retweets. Also, the effect of user activity variation on the retweeting dynamics in the early stages has been neglected. In this paper, we propose an extended reinforced Poisson process model with time mapping process to model the retweeting dynamics and predict the future popularity. The proposed model explicitly characterizes the process through which a message gain its retweets, by capturing a power-law temporal relaxation function corresponding to the aging in the ability of the message to attract new retweets and an exponential reinforcement mechanism characterizing the \"richer-get-richer\" phenomenon. Further, we introduce the notation of weibo time and integrate a time mapping process into the proposed model to eliminate the effect of user activity variation. Extensive experiments on two Weibo datasets, with 10K and 18K messages respectively, well demonstrate the effectiveness of our proposed model in popularity prediction.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126834836","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: 139

Engagement Periodicity in Search Engine Usage: Analysis and its Application to Search Quality Evaluation 搜索引擎使用中的约定周期:分析及其在搜索质量评价中的应用

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/2684822.2685318

Alexey Drutsa, Gleb Gusev, P. Serdyukov

引用次数: 28

Understanding and Predicting Graded Search Satisfaction 理解和预测分级搜索满意度

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/2684822.2685319

Jiepu Jiang, Ahmed Hassan Awadallah, Xiaolin Shi, Ryen W. White

{"title":"Understanding and Predicting Graded Search Satisfaction","authors":"Jiepu Jiang, Ahmed Hassan Awadallah, Xiaolin Shi, Ryen W. White","doi":"10.1145/2684822.2685319","DOIUrl":"https://doi.org/10.1145/2684822.2685319","url":null,"abstract":"Understanding and estimating satisfaction with search engines is an important aspect of evaluating retrieval performance. Research to date has modeled and predicted search satisfaction on a binary scale, i.e., the searchers are either satisfied or dissatisfied with their search outcome. However, users' search experience is a complex construct and there are different degrees of satisfaction. As such, binary classification of satisfaction may be limiting. To the best of our knowledge, we are the first to study the problem of understanding and predicting graded (multi-level) search satisfaction. We ex-amine sessions mined from search engine logs, where searcher satisfaction was also assessed on multi-point scale by human annotators. Leveraging these search log data, we observe rich and non-monotonous changes in search behavior in sessions with different degrees of satisfaction. The findings suggest that we should predict finer-grained satisfaction levels. To address this issue, we model search satisfaction using features indicating search outcome, search effort, and changes in both outcome and effort during a session. We show that our approach can predict subtle changes in search satisfaction more accurately than state-of-the-art methods, affording greater insight into search satisfaction. The strong performance of our models has implications for search providers seeking to accu-rately measure satisfaction with their services.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116451885","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: 86

Learning About Health and Medicine from Internet Data 从互联网数据中学习健康和医学

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/2684822.2697042

E. Yom-Tov, I. Cox, Vasileios Lampos

{"title":"Learning About Health and Medicine from Internet Data","authors":"E. Yom-Tov, I. Cox, Vasileios Lampos","doi":"10.1145/2684822.2697042","DOIUrl":"https://doi.org/10.1145/2684822.2697042","url":null,"abstract":"Surveys show that around 70% of US Internet users consult the Internet when they require medical information. People seek this information using both traditional search engines and via social media. The information created using the search process offers an unprecedented opportunity for applications to monitor and improve the quality of life of people with a variety of medical conditions. In recent years, research in this area has addressed public-health questions such as the effect of media on development of anorexia, developed tools for measuring influenza rates and assessing drug safety, and examined the effects of health information on individual wellbeing. This tutorial will show how Internet data can facilitate medical research, providing an overview of the state-of-the-art in this area. During the tutorial we will discuss the information which can be gleaned from a variety of Internet data sources, including social media, search engines, and specialized medical websites. We will provide an overview of analysis methods used in recent literature, and show how results can be evaluated using publicly-available health information and online experimentation. Finally, we will discuss ethical and privacy issues and possible technological solutions. This tutorial is intended for researchers of user generated content who are interested in applying their knowledge to improve health and medicine.","PeriodicalId":179443,"journal":{"name":"Proceedings of the Eighth ACM International Conference on Web Search and Data Mining","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132390676","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

Session details: Session 5: Practice & Experience Talks 会议详情:第五部分:实践与经验讲座

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/3251096

P. Bennett

引用次数: 0

Sarcasm Detection on Twitter: A Behavioral Modeling Approach Twitter上的讽刺检测:一种行为建模方法

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/2684822.2685316

Ashwin Rajadesingan, R. Zafarani, Huan Liu

引用次数: 359

Boosting Search with Deep Understanding of Contents and Users 通过对内容和用户的深入理解来促进搜索

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining Pub Date : 2015-02-02 DOI: 10.1145/2684822.2697047

Kaihua Zhu

引用次数: 0