隨著社群網路趨於時代而變得流行，每天有各式各樣大量且龐複的資料被社群使用者們產生，而如果我們可以妥善的利用並分析這些資料，許多真實發生的事件便可以被立即的反映出來。實際上，一個真實的事件，是會隨著時間的演進而產生意義或內容上的變化的，因此我們可以透過偵測字詞與字詞間語意上的關係變化，進而描繪出一個事件的演變過程。綜上而論，本論文的目標乃是基於給定的事件關鍵字詞，透過分析使用者討論事件熱度的起伏、及言論語意結構上的相似度，終而得出使用者隨著時間的遞移而對於給定事件產生討論內容上的變化。本論文實驗結果並表示，我們能夠有效的擷取出使用者在時間點的推移上所產生的不同的討論內容。

With the emergence of social network sites, a huge amount of user-generated content can be utilized to reflect the real-world events, and sometimes even ahead of the newswire. Since we know that events will evolve with the time goes by, the relationship identification can be utilized to capture the evolution of these events, and therefore benefit the emergency control and the crowd opinion analysis.
The proposed framework is to excavate out the evolution of how these relationships change through time by reflecting user attention toward the events. The event identification results from our previous work are utilized as our source to identify evolving relationships through a syntax-based relationship generation technique with evolving peak detection of user mention. The experimental results show the usefulness of our framework in identifying evolving relationships for event concepts in social networks.

1 Introduction 1
2 Related work 4
3 Methodology 6
3.1 Data Preprocess 7
3.1.1 Top Event Selection 7
3.1.2 Language Filter and POS-Tagging 10
3.1.3 Tweets Filtering based on Event Concepts 10
3.2 Evolving Peak Detection 11
3.2.1 Mention Decay Calculation 12
3.2.2 Peak Detection 13
3.3 Relationship Generation 14
3.3.1 Relationship Syntax Merging 15
3.3.2 Representative Relationship Selection 16
4 Experiments 19
4.1 Experimental Setup 19
4.2 Experimental Results 21
5 Conclusions and future work 26
References 27

[1] F. Alvanaki, S. Michel, K. Ramamritham, and G. Weikum. See what’s enblogue:
real-time emergent topic identification in social media. In Proceedings of the 15th
International Conference on Extending Database Technology, pages 336–347. ACM,
2012.
[2] F. Alvanaki, M. Sebastian, K. Ramamritham, and G. Weikum. Enblogue: emergent
topic detection in web 2.0 streams. In Proceedings of the 2011 ACM SIGMOD International
Conference on Management of data, pages 1271–1274. ACM, 2011.
[3] D. M. Blei and J. D. Lafferty. Dynamic topic models. In Proceedings of the 23rd
international conference on Machine learning, pages 113–120. ACM, 2006.
[4] Q. Diao, J. Jiang, F. Zhu, and E.-P. Lim. Finding bursty topics from microblogs. In
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics:
Long Papers-Volume 1, pages 536–544. Association for Computational Linguistics,
2012.
[5] L. Ding, T. Finin, A. Joshi, R. Pan, R. S. Cost, Y. Peng, P. Reddivari, V. Doshi, and
J. Sachs. Swoogle: a search and metadata engine for the semantic web. In Proceedings
of the thirteenth ACM international conference on Information and knowledge
management, pages 652–659. ACM, 2004.
[6] W. Dou, X. Wang, D. Skau, W. Ribarsky, and M. X. Zhou. Leadline: Interactive
visual analysis of text data through event identification and exploration. In Visual
Analytics Science and Technology (VAST), 2012 IEEE Conference on, pages 93–102.
IEEE, 2012.
[7] A. Fader, S. Soderland, and O. Etzioni. Identifying relations for open information
extraction. In Proceedings of the Conference on Empirical Methods in Natural
Language Processing, pages 1535–1545. Association for Computational Linguistics,
2011.
[8] G. P. C. Fung, J. X. Yu, P. S. Yu, and H. Lu. Parameter free bursty events detection in
text streams. In Proceedings of the 31st international conference on Very large data
bases, pages 181–192. VLDB Endowment, 2005.
[9] K. Gimpel, N. Schneider, B. O’Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman,
D. Yogatama, J. Flanigan, and N. A. Smith. Part-of-speech tagging for twitter: Annotation,
features, and experiments. In Proceedings of the 49th Annual Meeting of the
Association for Computational Linguistics: Human Language Technologies: short
papers-Volume 2, pages 42–47. Association for Computational Linguistics, 2011.
[10] J. Guzman and B. Poblete. On-line relevant anomaly detection in the twitter stream:
an efficient bursty keyword detection model. In Proceedings of the ACM SIGKDD
Workshop on Outlier Detection and Description, pages 31–39. ACM, 2013.
[11] P. Heymann and H. Garcia-Molina. Collaborative creation of communal hierarchical
taxonomies in social tagging systems. 2006.
[12] 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, pages 297–304. ACM, 2004.
[13] H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news
media? In Proceedings of the 19th international conference on World wide web,
pages 591–600. ACM, 2010.
[14] E. Kwan, P.-L. Hsu, J.-H. Liang, and Y.-S. Chen. Event identification for social
streams using keyword-based evolving graph sequences. In Proceedings of the 2013
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining, pages 450–457. ACM, 2013.
[15] V. I. Levenshtein. Binary codes capable of correcting deletions, insertions and reversals.
In Soviet physics doklady, volume 10, page 707, 1966.
[16] G. A. Miller. Wordnet: a lexical database for english. Communications of the ACM,
38(11):39–41, 1995.
[17] S. Petrovic, M. Osborne, R. McCreadie, C. Macdonald, I. Ounis, and L. Shrimpton.
Can twitter replace newswire for breaking news. In Seventh International AAAI Conference
on Weblogs and Social Media, 2013.
[18] A.-M. Popescu and M. Pennacchiotti. Detecting controversial events from twitter. In
Proceedings of the 19th ACM International Conference on Information and Knowledge
Management, pages 1873–1876, 2010.
[19] J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman, and J. Sperling. Twitterstand:
News in tweets. In Proceedings of the 17th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems, pages 42–51, 2009.
[20] H. Sayyadi, M. Hurst, and A. Maykov. Event detection and tracking in social streams.
In Proceedings of International AAAI Conference on Weblogs and Social Media,
2009.
[21] N. Shuyo. Language detection library for java, 2010.
[22] L. Specia and E. Motta. Integrating folksonomies with the semantic web. In The
semantic web: research and applications, pages 624–639. Springer, 2007.
[23] G. Valkanas and D. Gunopulos. How the live web feels about events. In Proceedings
of the 22nd ACM International Conference on Information & Knowledge Management,
pages 639–648. ACM, 2013.
[24] J.Weng and B.-S. Lee. Event detection in twitter. In Proceedings of the International
Conference on Weblogs and Social Media, 2011.
[25] J. Yao, B. Cui, Y. Huang, and Y. Zhou. Bursty event detection from collaborative tags.
World Wide Web, 15(2):171–195, 2012.