新闻在社交网络平台中的发展趋势,可以用其在该网络中出现的频率的移动平均值来跟踪。该文利用两条不同时间周期的移动平均值来分别跟踪新闻的相对短期趋势和相对长期趋势,并定义这两种趋势的差值为该新闻趋势发展的趋势动量。当趋势动量值为正,该新闻将有更加热门的可能,反之,则表明该新闻的关注度正在降低。而该趋势动量值本身的大小变化,也同样能为新闻趋势的变化提供预测。实验证明,该文的方法简单有效,能很好地对社交网络中新闻未来可能发展趋势做出即时、准确的预测。
Abstract
The trend of news event in the social network can be tracked by the moving average of its frequency of occurrence. This paper presets a novel method to track the shorter trend and the longer trend of news by the moving average lines of two different sized time-window are adopted. The momentum of the news trend is defined as the difference between the shorter moving average and the longer moving average. When the value of the momentum is positive, the news are more likely to get hotter, and vice versa. Moreover, the change of the momentum value itself provides an even earlier indicator of the news trend. Experimental results show proves the proposed method is simple and effective in predicting the news trend in time and accurately.
Key wordssocial-network; news trend predicting;moving average
关键词
社交网络 /
新闻趋势预测 /
移动平均
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Key words
social-network /
news trend predicting /
moving average
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参考文献
[1] A Kontostathis, L Galitsky, W M Pottenger, et al. A survey of emerging trend detection in textual data mining[M]. Survey of Text Mining, 2003:185-224.
[2] J Allan, J Carbonell, G Doddington, et al. Topic detection and tracking pilot study final report[C]//Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, 1998.
[3] 骆卫华,于满泉,许洪波,等. 基于多策略优化的分治多层聚类算法的话题发现研究[J]. 中文信息学报, 2006,20(1):29-36.
[4] 洪宇,张宇,刘挺,等. 话题检测与跟踪的评测及研究综述[J]. 中文信息学报,2007,21(6): 71-87.
[5] A L Porter, M J Detampel. Technology opportunities analysis[C]//Technological Forecasting and Social Change, 1995,49:237-255.
[6] R Swan, D Jensen. Timemines: constructing timelines with statistical models of word usage[C]//KDD Workshop on Text Mining, 2000: 73-80.
[7] B Lent, R Agrawal, R Srikant. Discovering trends in test databases[C]//Proceedings of the 3rd Intel Conference on Knowledge Discovery in Databases and Data Mining, 1997.
[8] Pottenger W M, Kim Y B, Meling D D. HDDITM: Hierarchical Distributed Dynamic Indexing[C]//Proceedings of Data Mining for Scientific and Engineering Applications, 2001.
[9] Blei D M, Ng A Y, Jordan M I. Latent dirichlet allocation[J]. The Journal of Machine Learning Research, 2003, (3): 993-1022.
[10] Blei D M, Lafferty J D. Dynamic topic models[C]//Proceedings of the 23rd International Conference on Machine Learning, 2006: 113-120.
[11] Wang C, Blei D, Heckerman D. Continuous time dynamic topic models[C]//Proceedings of UAI, 2008.
[12] Wang X, McCallum A. Topics over time: a non-Markov continuous-time model of topical trends[C]//Proceedings of the 12th ACM SIGKDD. 2006: 424-433.
[13] Kawamae N. Trend analysis model: trend consists of temporal words, topics, and timestamps[C]//Proceedings of 4th ACM International Conference on Web Search and Data Mining,2011: 317-326.
[14] Leskovec J., Backstrom L, Kleinberg J. Meme-tracking and the dynamics of the news cycle[C]//Proceedings of the 15th ACM SIGKDD. 2009:497-506.
[15] Goorha S, Ungar L. Discovery of significant emerging trends[C]//Proceedings of the 16th ACM SIGKDD. 2010: 57-64.
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