Please use this identifier to cite or link to this item:
Social Event Magnitudes via Background Influences and Engagement Capacities and its Applications
Online event detection
|Issue Date: ||2019-03-04 21:42:56 (UTC+8)|
|Abstract: ||了解社群事件爆發(例如，阿拉伯之春)已是社群計算領域中受到廣泛重視的研究分支之一，因為除了社群事件偵測的相關應用多元之外，其亦與資訊擴散相關研究有著密切關係。社群事件的發生可從兩個角度來觀察: 一為資訊的異常變化(例如，關鍵文字於短時間內的暴增) 、二為活動的人氣高低(例如，資訊分享) 。事件偵測演算法通常針對前者中的異常變化進行設計，而社群事件強度則是透過統計方法針對分享的人氣高低提供一測度。換言之，社群事件強度提供了人們一個較為直覺的數值表達方式，藉由該數可易於瞭解事件在某個時空下的程度。然而，與事件偵測演算法相較下，社群事件強度於應用上有其劣勢: 一為其仰賴轉發數據、二為不易整合資訊進行估測。|
因此，本研究受事件偵測演算設計的啟發，提出了另一測度的表達方式，謂之為社群事件規模。社群事件規模的計算概念為某一時段內背景影響與群眾參與的乘值；背景影響值的抽取乃是應用非回溯矩陣從文字資訊中取出，而群眾參與的計算則是將網絡結構轉化為社群參與值(亦為Ordered Shapley Value)而得出。因此，社群事件規模的優勢，如事件偵測演算一樣，可整合資訊，且如社群事件強度一樣，可提供一個了解社群事件程度的概念性數字。此外，社群事件規模的計算也提供了另一資訊整合方式，有別於常用的相似度整合資訊的方法。
Detecting outbreaks of social events (e.g., Arab Spring) has been an active research area in social computing, because of its close relationship to the research of information diffusion. Outbreaks of social events can be viewed from two angles: 1. anomalous changes of information (e.g., an influx of text), and 2. popular actions (e.g., retweets). Event detection algorithms focus on extracting anomalous information, while popular actions are measured by estimating social event intensity, which is a rate to show how popular an action is in a social network during a short period. The rate is relatively intuitive and gives a holistic view about activity levels in a network, but it has a few disadvantages. First, its estimation cannot work without the information about numbers of shares. Second, the estimation isn’t easy. Inspired by event detection algorithms, this study proposes an alternative measure using the product of background influence and cooperation value in a fixed time interval, and it is called social event magnitude. Background influence is extracted from text via non-backtracking matrices, and cooperation value is obtained via social engagement capacities, a modified version of Shapley value. The proposed alternative gives a holistic view about activity levels in a network. The construction of proposed alternative shows another way to integrate multiple sources of information for event detection. We demonstrated social event magnitude on specified and unspecified online event detections. In the specified detection, the alternative has a better performance in precision-recall evaluation. In the unspecified detection, social event magnitudes follow a long-tailed distribution. Furthermore, social event magnitudes can be visualized for changing levels of activities.
|Reference: || C. Zhang, G. Zhou, Q. Yuan, H. Zhuang, Y. Zheng, L. Kaplan, S. Wang and J. Han, "GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams," in ACM Special Interest Group on Information Retrieval, Pisa, Italy, 2016.|
 T. Sakaki, M. Okazaki and Y. Matsuo, "Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors," in World Wide Web, Raleigh, North Carolina, USA, 2010.
 C. Aggarwal and K. Subbian, "Event Detection in Social Streams," in Society for Industrial and Applied Mathematics Data Mining, Anaheim, CA, USA, 2012.
 M. Cordeiro and J. Gama, "Online Social Networks Event Detection: A Survey," in Solving Large Scale Learning Tasks. Challenges and Algorithms, Springer, 2016, pp. 1-41.
 S. Ranshous, S. Shen, D. Kourtra, S. Harenberg, C. Faloutsos and N. Samatova, "Anomaly Detection in Dynamic Networks: A Survey," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 7, no. May, pp. 223-247, 2015.
 Q. Zhao, M. Erdogdu, H. He, A. Rajaraman and J. Leskovec, "SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity," in ACM Knowledge Discovery and Data Mining, Sydney, 2015.
 M.-A. Rizoiu, Y. Lee, S. Mishra and L. Xie, "Hawkes Processes for Events in Social Media," in Frontiers of Multimedia Research, New York, ACM, 2018, pp. 191-218.
 W. W. Cohen, "Enron Email Dataset," Carnegie Mellon University, 8 May 2015. [Online]. Available: https://www.cs.cmu.edu/~enron/. [Accessed 10 July 2018].
 Y. Ogata, "Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes," Journal of the American Statistical Association, vol. 83, no. 401, pp. 9-27, 1985.
 A. G. Hawkes, "Hawke Processes and their Applications to Finance: Review," Quantitative Finance, vol. 18, no. 2, pp. 193-198, 2018.
 M.-A. Rizoiu, L. Xie, S. Sanner, M. Cebrian, H. Yu and P. V. Henteryck, "Expecting to be HIP: Hawkes Intensity Processses for Social Media Popularity," in World Wide Web, Perth, Australia, 2017.
 R. Kobayashi and R. Lambiotte, "TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics," in International Conference on Web and Social Media, Cologne, Germany, 2016.
 M. Farajtabar, N. Du, M. Gomez-Rodriguez, I. Valera, H. Zha and L. Song, "Shaping Social Activity by Incentivizing Users," in Neural Information Processing Systems , Montreal, Canada, 2014.
 T. Martin, X. Zhang and M. E. J. Newman, "Localization and Centrality in Networks," Physical Review E, vol. 90, no. 5, p. 052808, 2014.
 F. Krzakala, C. Moore, E. Mossel, J. Neeman, A. Sly, L. Zdeborova and P. Zhang, "Spectral Redemption: Clustering Sparse Networks," Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 52, pp. 20935-20940, 24 December 2013.
 N. Alon, I. Benjamini, E. Lubetzky and S. Sodin, "Non-backtracking Random Walks Mix Faster," Communications in Contemporary Mathematics, vol. 9, no. 4, pp. 585-603, 2007.
 O. Angel, J. Friedman and S. Hoory, "The Non-backtracking Spectrum of the Universal Cover of a Graph," Transactions of The American Mathematical Society, vol. 367, no. 6, pp. 4287-4318, 2015.
 R. R. Nadakudiu and M. E. J. Newman, "Graph Spectra and Detectability of Community Structure in Networks," Physical Review Letters, vol. 108, no. 18, p. 188701, 2012.
 T. Tao, Topics in Random Matrix Theory, Rhode Island: American Mathematical Society, 2012.
 F. Chung, L. Lu and V. Vu, "The Spectra of Random Graphs with Given Expected Degrees," Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 11, pp. 6313-6318, 2003.
 C. Moore, "The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness," European Association for Theoretical Computer Science, vol. 121, no. February, 2017.
 A. Decelle, F. Krzakala, C. Moore and L. Zdeborova, "Asymptotic Analysis of the Stochastic Block Model for Modular Networks and its Algorithmic Applications," Physical Review E, vol. 84, no. 6, p. 066106, 2011.
 C. Bordenave, M. Lelarge and L. Massoulie, "Non-backtracking Spectrum of Random Graphs: Community Detection and Non-regular Ramanujan Graphs," in Symposium on Foundations of Computer Science, Berkeley, CA, USA, 2015.
 A. Nikolaev, S. Gore and V. Govindaraju, "Engagement Capacity and Engaging Team Formation for Reach Maximization of Online Social Media Platforms," in ACM Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016.
 R. P. Gilles, The Cooperative Game Theory of Networks and Hierarchies, Berlin Heidelberg: Springer, 2010.
 Y. Yang, T. Pierce and J. Carbonell, "A Study on Retrospective and On-Line Event Detection," in ACM Special Interest Group in Information Retrieval, Melbourne, Australia, 1998.
 S. Kumar, H. Liu, S. Metha and L. Subramaniam, "Exploring a Scalable Solution to Identifying Events in Noisy Twitter Streams," in ACM Advances in Social Networks Analysis and Mining, Paris, France, 2015.
 X. Dong, D. Mavroeidis, F. Calabrese and P. Frossard, "Multiscale Event Detection in Social Media," Data Mining and Knowledge Discovery, vol. 29, no. 5, pp. 1374-1405, 2015.
 M. Newman, "Networks: An Introduction," Oxford University Press, New York, NY, USA, 2010.
 Z. Bai, J. Demmel, J. Dongarra, A. Ruhe and H. Vorst, Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide, Philadelphia: Society for Industrial and Applied Mathematics, 2000.
|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0105753030|
|Data Type: ||thesis|
|Appears in Collections:||[資訊科學系] 學位論文|
Files in This Item:
All items in 政大典藏 are protected by copyright, with all rights reserved.