臺灣博碩士論文加值系統

隨著網路及行動裝置的普級，民眾可以隨時輕鬆地在網路上散佈與獲取資訊。不過網路上的資訊內容往往過於雜亂，導致我們花費太多時間在吸收瑣碎的訊息，反而太晚得知甚至錯過了重要的訊息。因此，我們希望訊息爆發前就能事先得知以便提早採取行動。此類的問題在生活中有許多相關的應用，如推薦系統、內容過濾、疾病預防、市場行銷等等，近年來也有越來越多的人員投入這塊領域的研究。在本論文，我們提出一個基於王等人設計的模型並進一步結合角色特徵的新方法，在Twitter資料集上預測熱門主題標籤，其中資料集包含595,460個使用者及14,607個最終大小不小於50的主題標籤。實驗結果顯示角色特徵的資訊對級聯預測問題是有幫助的。

With ubiquitous networks and mobile devices, people can easily spread and access information on the Internet at any time. But information on the Internet is often too messy, causing us to spend too much time on the absorption of trivial messages, yet too late to know or even missed the important messages. For this reason, we hope to know beforehand the outbreak of news in order to take early action. This kind of problem has many related applications in life, such as recommendation system, content filtering, disease prevention, marketing, etc. In recent years, more and more people have been involved in research in this field. In this paper, we propose a new method based on the model designed by Wang et al. and further combine the role features. We predict popular hashtags on a Twitter dataset that contains 595,460 users and 14,607 hashtags with final size no less than 50. The experiment results show that the role features information is helpful for the cascade prediction problems.

論文摘要 I
Abstract II
Contents III
List of Figures IV
List of Tables V
Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Related Work 1
Chapter 2. Preliminaries 3
2.1 Definitions 3
2.2 Problem Statement 4
Chapter 3. Prediction Features 5
3.1 User Features 5
3.2 Distance Features 5
3.3 Community Features 6
3.4 Temporal Features 6
3.5 Role Features 7
Chapter 4. Experiments 10
4.1 Dataset and Preprocessing 10
4.2 Evaluation Criteria 11
4.3 Results 12
Chapter 5. Conclusions and Future Work 21
References 22
Appendix 24

[1] L. Weng, F. Menczer, and Y-Y. Ahn, “Predicting successful memes using network and community structure.” In Proceedings of the 8th International Conference on Weblogs and Social Media (ICWSM), 2014.
[2] Q. Zhao, M. A. Erdogdu, H. Y. He, A. Rajaraman, and J. Leskovec, “Seismic: A self-exciting point process model for predicting tweet popularity.” In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015.
[3] R. Guo, and P. Shakarian, “A Comparison of Methods for Cascade Prediction.” In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016.
[4] B. Shulman, A. Sharma, and D. Cosley, “Predictability of Popularity: Gaps between Prediction and Understanding.” In Proceedings of the 10th International Conference on Web and Social Media (ICWSM), 2016.
[5] D. Kempe, J. Kleinberg, and É. Tardos, “Maximizing the spread of influence through a social network.” In Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), 2003.
[6] C. Luo, W. Osborne, and M. L. Lee, “Mining Brokers in Dynamic Social Networks.” In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.
[7] C. T. Li, Y. J. Lin, and M. Y. Yeh, “The roles of network communities in social information diffusion.” In 2015 IEEE International Conference on Big Data (BigData), 2015.
[8] Hashtag, https://en.wikipedia.org/wiki/Wikipedia, reference on April 27th, 2017.
[9] W. Chen, C. Wang, and Y. Wang, “Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks.” In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2010.
[10] J. Ugandera, L. Backstromb, C. Marlowb, and J.Kleinberg, “Structural diversity in social contagion.” In Proceedings of the National Academy of Sciences (PNAS), 2012.
[11] J. Scripps, P. N. Tan, and A. H. Esfahanian, “Exploration of Link Structure and Community-based Node Roles in Network Analysis.” In Proceedings of the 7th IEEE International Conference on Data Mining (ICDM), 2007.
[12] L. Page, S. Brin, R. Motwani, and T. Winograd, “The PageRank Citation Ranking: Bringing Order to the Web.”, Stanford InfoLab, 1999.
[13] R. S. Burt, “Structural Holes: The Social Structure of Competition.” In Harvard University Press, 1992.
[14] T. Lou, and J. Tang, “Mining structural hole spanners through information diffusion in social networks.” In 22nd International World Wide Web Conference (WWW), 2013.
[15] J. Yang, and J. Leskovec, “Overlapping community detection at scale: a nonnegative matrix factorization approach.” In Sixth ACM International Conference on Web Search and Data Mining (WSDM), 2013.
[16] V. D. Blondel, J- L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of community hierarchies in large networks.”, Journal of Statistical Mechanics: Theory and Experiment, 2008.
[17] L. Weng, F. Menczer, and Y-Y .Ahn, “Virality Prediction and Community Structure in Social Networks.” Nature Scientific Report. (3)2522, 2013.
[18] D. Vallet, S. Berkovsky, S. Ardon, A. Mahanti, and M. A. Kâafar, “Characterizing and Predicting Viral-and-Popular Video Content.”, In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.
[19] H. Tong, B. A. Prakash, C. E. Tsourakakis, T. Eliassi-Rad, C. Faloutsos, and D. H. Chau, “On the Vulnerability of Large Graph.”, In The 10th IEEE International Conference on Data Mining (ICDM), 2010.
[20] Z. Luo, M. Osborne, J. Tang, and T. Wang, “Who will retweet me?: finding retweeters in twitter.”, In The 36th International ACM SIGIR conference on research and development in Information Retrieval (SIGIR), 2013.
[21] PageRank, https://en.wikipedia.org/wiki/Wikipedia, reference on June 1st, 2017.
[22] Clustering coefficient, https://en.wikipedia.org/wiki/Clustering_coefficient, reference on June 1st, 2017.
[23] J. Yang, and J. Leskovec, “Community-Affiliation Graph Model for Overlapping Network Community Detection.”, In The 12th IEEE International Conference on Data Mining (ICDM), 2012.