1. bookVolume 7 (2017): Issue 3 (July 2017)
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English
access type Open Access

MapReduce and Semantics Enabled Event Detection using Social Media

Published Online: 20 Mar 2017
Page range: 201 - 213
Received: 01 Jan 2016
Accepted: 04 Jul 2016
Journal Details
License
Format
Journal
First Published
30 Dec 2014
Publication timeframe
4 times per year
Languages
English

Social media is playing an increasingly important role in reporting major events happening in the world. However, detecting events from social media is challenging due to the huge magnitude of the data and the complex semantics of the language being processed. This paper proposes MASEED (MapReduce and Semantics Enabled Event Detection), a novel event detection framework that effectively addresses the following problems: 1) traditional data mining paradigms cannot work for big data; 2) data preprocessing requires significant human efforts; 3) domain knowledge must be gained before the detection; 4) semantic interpretation of events is overlooked; 5) detection scenarios are limited to specific domains. In this work, we overcome these challenges by embedding semantic analysis into temporal analysis for capturing the salient aspects of social media data, and parallelizing the detection of potential events using the MapReduce methodology. We evaluate the performance of our method using real Twitter data. The results will demonstrate the proposed system outperforms most of the state-of-the-art methods in terms of accuracy and efficiency.

Keywords

[1] J. Wen and B. Lee, Event Detection in Twitter, In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, 2011, 401-408.Search in Google Scholar

[2] T. Sakaki, M. Okazaki, and Y. Matsuo, Earthquake shakes Twitter users: real-time event detection by social sensors, In Proceedings of the 19th International Conference on World Wide Web, 2010, 851-860.Search in Google Scholar

[3] Q. Zhao and P. Mitra, Event Detection and Visualization for Social Text Streams, In Proceedings of the International AAAI Conference on Weblogs and Social Media, 2007, 26-28.Search in Google Scholar

[4] G. Kumaran and J. Allan, Text classification and named entities for new event detection, In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2004, 297-304.Search in Google Scholar

[5] R. Parikh and K. Karlapalem, Et: events from tweets. In Proceedings of the 22nd International Conference on World Wide Web companion, 2013, 613-620.Search in Google Scholar

[6] G. Fung, J. Yu, P. Yu, and H. Lu, Parameter free bursty events detection in text streams, In Proceedings of the 31st International Conference on Very Large Databases, 2005, 181-192.Search in Google Scholar

[7] A. Guille and C. Favre, Mention-anomaly-based event detection and tracking in twitter, Advances in Social Networks Analysis and Mining(ASONAM), 2014, 375-382.Search in Google Scholar

[8] X. Wang, F. Zhu, J. Jiang and S. Li, Real time event detection in twitter, In: Web-Age Information Management, Springer, Berlin Heidelberg, 2013, 502-513.Search in Google Scholar

[9] A. Ritter, S. Clark and O. Etzioni, Named entity recognition in tweets: an experimental study, In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011, 1524-1534.Search in Google Scholar

[10] J. Kleinberg, Bursty and hierarchical structure in streams, Data Mining and Knowledge Discovery 7, no. 4, 2003, 373-397.Search in Google Scholar

[11] PearAnalytics. Twitter study - august 2009, http://www.pearanalytics.com/wpcontent/uploads/2009/08/Twitter-Study-August-2009.pdf, 2009Search in Google Scholar

[12] R. Li, S. Wang, H. Deng, R. Wang and K. Chang, Towards social user profiling: unified and discriminative influence model for inferring home locations. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012, 1023-1031.Search in Google Scholar

[13] Japan Earthquake, 2011, http://en.wikipedia.org/wiki/2011_T%C5%8Dhoku_earthquake_and_tsunamiSearch in Google Scholar

[14] Death of Bin Ladin, 2011, http://en.wikipedia.org/wiki/Death_of_Osama_bin_LadenSearch in Google Scholar

[15] F. Chen and D. Neill, Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, 1166-1175.Search in Google Scholar

[16] S. Levine, How fast the news spreads through social media, In http://blog.sysomos.com/2011/05/02/how-fast-the-news-spreads-through-social-media/, 2012.Search in Google Scholar

[17] J. Benhardus and J. Kalita, Streaming trend detection in twitter, International Journal of Web Based Communities 9, no. 1, 2013, 122-139.Search in Google Scholar

[18] D, Shamma, L. Kennedy and E. Churchill, Peaks and persistence: modeling the shape of microblog conversations, In Proceedings of the ACM 2011 conference on Computer supported cooperative work, 2011, 355-358.Search in Google Scholar

[19] J. Lau, N. Collier and T. Baldwin, On-line Trend Analysis with Topic Models:\# twitter Trends Detection Topic Model Online, In COLING, 2012, 1519-1534.Search in Google Scholar

[20] T. Lappas, B. Arai, M. Platakis, D. Kotsakos and D. Gunopulos, On burstiness-aware search for document sequences, In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009, 477-486.Search in Google Scholar

[21] D. Gruhl, R. Guha, D. Liben-Nowell and A. Tomkins, Information diffusion through blogspace, In Proceedings of the 13th International Conference on World Wide Web, 2004, 491-501.Search in Google Scholar

[22] Y. Hu, A. John, D. Seligmann and F. Wang, What Were the Tweets About? Topical Associations between Public Events and Twitter Feeds, In: ICWSM, 2012.Search in Google Scholar

[23] C. Li, A. Sun and A. Datta. Twevent: segment-based event detection from tweets, In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012, 155-164.Search in Google Scholar

[24] A. Kaplan and M. Haenlein, The early bird catches the news: Nine things you should know about micro-blogging. Business Horizons 54, no. 2, 2011, 105-113.Search in Google Scholar

[25] Y. Teh, M. Jordan, M. Beal and D. Blei, Hierarchical dirichlet processes, Journal of the American Statistical Association 101, no. 476, 2006Search in Google Scholar

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