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Mining Actionable Insights from Social Networksat WSDM 2017

Published:02 February 2017Publication History

ABSTRACT

The first international workshop on Mining Actionable Insights from Social Networks (MAISoN'17) is to be held on February 10, 2017; co-located with the Tenth ACM International Web Search and Data Mining (WSDM) Conference in Cambridge, UK. MAISoN'17 aims at bringing together researchers and participants from different disciplines such as computer science, big data mining, machine learning, social network analysis and other related areas in order to identify challenging problems and share ideas, algorithms, and technologies for mining actionable insight from social network data. We organized a workshop program that includes the presentation of eight peer-reviewed papers and keynote talks, which foster discussions around state-of-the-art in social network mining and will hopefully lead to future collaborations and exchanges.

References

  1. Zarrinkalam, F., Fani, H., Bagheri, E. and Kahani, M., 2016, March. Inferring Implicit Topical Interests on Twitter. In European Conference on Information Retrieval (pp. 479--491). Springer International Publishing. Google ScholarGoogle ScholarCross RefCross Ref
  2. Asur, S. and Huberman, B.A., 2010. Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on (Vol. 1, pp. 492--499). IEEE. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Melville, P., Sindhwani, V. and Lawrence, R., 2009. Social media analytics: Channeling the power of the blogosphere for marketing insight. Proc. of the WIN, 1(1), pp.1--5.Google ScholarGoogle Scholar
  4. Guille, A., Hacid, H., Favre, C. and Zighed, D.A., 2013. Information diffusion in online social networks: A survey. ACM SIGMOD Record, 42(2), pp.17--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Wang, X., Gerber, M.S. and Brown, D.E., 2012. Automatic crime prediction using events extracted from twitter posts. In International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (pp. 231--238). Springer Berlin Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Fani, H., Zarrinkalam, F., Bagheri, E. and Du, W., 2016. Time-Sensitive Topic-Based Communities on Twitter. In Canadian Conference on Artificial Intelligence (pp. 192--204). Springer International Publishing. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.H. and Liu, B., 2011. Predicting flu trends using twitter data. In Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on (pp. 702--707). IEEE. Google ScholarGoogle ScholarCross RefCross Ref
  8. Bollen, J., Mao, H. and Zeng, X., 2011. Twitter mood predicts the stock market. Journal of Computational Science, 2(1), pp.1--8. Google ScholarGoogle ScholarCross RefCross Ref
  9. Papadopoulos, S., Kompatsiaris, Y., Vakali, A. and Spyridonos, P., 2012. Community detection in social media. Data Mining and Knowledge Discovery, 24(3), pp.515--554. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Limsaiprom, P. and Tantatsanawong, P., 2010, May. Social network anomaly and attack patterns analysis. In Networked Computing (INC), 2010 6th International Conference on (pp. 1--6). IEEE.Google ScholarGoogle Scholar

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            • Published in

              cover image ACM Conferences
              WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining
              February 2017
              868 pages
              ISBN:9781450346757
              DOI:10.1145/3018661

              Copyright © 2017 Owner/Author

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 2 February 2017

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              WSDM '17 Paper Acceptance Rate80of505submissions,16%Overall Acceptance Rate498of2,863submissions,17%

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