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WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining
ACM2011 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
WSDM'11: Fourth ACM International Conference on Web Search and Data Mining Hong Kong China February 9 - 12, 2011
ISBN:
978-1-4503-0493-1
Published:
09 February 2011
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Abstract

Welcome to the Fourth ACM International Conference on Web Search and Data Mining (WSDM 2011) held on February 9-12, 2011, in Hong Kong. As the premier ACM conference in the field, WSDM 2011 offers a highly competitive forum for reporting the latest developments in websearch, social search and data mining. We are pleased to present the proceedings of the conference as its published record.

Although it is only in its fourth year, WSDM has already witnessed significant growth. We received a record 372 submissions, representing a 22% increase compared to WSDM 2010. 19 Senior PC members and 134 PC members conducted reviews to the submissions. The conference accepted 83 papers (22.3% acceptance rate). Among these, 32 papers were selected for oral and poster presentations and 51 papers were selected for poster only presentations. The authors of submitted papers were from 35 countries and regions, authors of accepted papers are from 13 countries and regions. The quality of accepted papers is very high, making WSDM a first tier conference in computer science.

Cited By

  1. khaledian N and Mardukhi F (2021). CFMT: a collaborative filtering approach based on the nonnegative matrix factorization technique and trust relationships, Journal of Ambient Intelligence and Humanized Computing, 10.1007/s12652-021-03368-6, 13:5, (2667-2683), Online publication date: 1-May-2022.
  2. Kaligotla C, Yücesan E and Chick S (2020). Diffusion of competing rumours on social media, Journal of Simulation, 10.1080/17477778.2020.1785345, 16:3, (230-250), Online publication date: 4-May-2022.
  3. Silva C, Galster M and Gilson F (2021). Topic modeling in software engineering research, Empirical Software Engineering, 10.1007/s10664-021-10026-0, 26:6, Online publication date: 1-Nov-2021.
  4. Yu H and Schroeder S (2017). Distribution and Popularity Patterns of Chinese Music on YouTube: A Case Study of Local Music’s Representation on a Global Internet Platform, Journal of New Music Research, 10.1080/09298215.2017.1369129, 47:1, (68-77), Online publication date: 1-Jan-2018.
  5. Uitermark J (2016). Complex contention: analyzing power dynamics within Anonymous, Social Movement Studies, 10.1080/14742837.2016.1184136, 16:4, (403-417), Online publication date: 4-Jul-2017.
  6. Preface Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  7. Introduction Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  8. Background Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  9. Text Data Understanding Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  10. MeTA : A Unified Toolkit for Text Data Management and Analysis Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  11. Overview of Text Data Access Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  12. Retrieval Models Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  13. Feedback Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  14. Search Engine Implementation Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  15. Search Engine Evaluation Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  16. Web Search Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  17. Recommender Systems Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  18. Overview of Text Data Analysis Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  19. Word Association Mining Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  20. Text Clustering Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  21. Text Categorization Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  22. Text Summarization Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  23. Topic Analysis Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  24. Opinion Mining and Sentiment Analysis Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  25. Joint Analysis of Text and Structured Data Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  26. Toward A Unified System for Text Management and Analysis Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  27. Appendixes Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  28. References Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining
  29. Feng Y, Li H and Chen Z Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles, International Journal of Web Services Research, 10.4018/IJWSR.2014100103, 11:4, (32-46)
  30. Chen W, Lakshmanan L and Castillo C (2013). Information and Influence Propagation in Social Networks, Synthesis Lectures on Data Management, 10.2200/S00527ED1V01Y201308DTM037, 5:4, (1-177), Online publication date: 27-Oct-2013.
  31. Al-Rawi A (2017). Viral News on Social Media, Digital Journalism, 10.1080/21670811.2017.1387062, (1-17)
  32. Han J, Lee S and Cha M (2023). The secret to successful evocative messages: Anger takes the lead in information sharing over anxiety, Communication Monographs, 10.1080/03637751.2023.2236183, (1-21)
Contributors
  • Chinese University of Hong Kong
  • L3S Research Center

Recommendations

Acceptance Rates

WSDM '11 Paper Acceptance Rate 83 of 372 submissions, 22%;
Overall Acceptance Rate 498 of 2,863 submissions, 17%
YearSubmittedAcceptedRate
WSDM '195118416%
WSDM '185148116%
WSDM '175058016%
WSDM '163686718%
WSDM '152383916%
WSDM '143556418%
WSDM '113728322%
Overall2,86349817%