微信服务号

微信订阅号

2025年5月10日 12:41 星期六
首页 > 过刊浏览>2014年第25卷第12期 >2767-2776. DOI:10.13328/j.cnki.jos.004730
PDF HTML阅读 XML下载 导出引用 引用提醒
基于动态演化的讨论帖流行度预测
作者:
基金项目:

国家自然科学基金(61175040,71025001)


Predicting Popularity of Forum Threads Based on Dynamic Evolution
Author:
  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [26]
    • |
  • 相似文献
    • |
  • 引证文献
    • | |
  • 文章评论
    摘要:

    互联网用户间的交互行为,使得某些用户生成的内容(如讨论帖、微博话题)变得流行.对所关注内容的流行度进行建模和预测,在多个领域中具有十分重要的研究和应用价值.针对论坛讨论帖的流行度预测问题,基于早期的发展演化信息,探讨了影响讨论帖流行度的相关动态因素,并提出一种结合局部特性、融合多个动态因素的讨论帖流行度预测算法.以豆瓣小组的数据为例,对所提出的算法进行实验.实验结果表明,所提出的融合多种动态因素的方法与基准方法相比,能够较好地预测讨论帖的流行度.

    Abstract:

    Web user's online interacting behavior with others usually makes some user generated content (e.g. forum threads and Weibo topics) popular. The modeling and prediction of the popularity of online content are of great research importance and practical value in many different domains. To predict the popularity of forum threads, this paper discusses several dynamic factors that might affect the popularity of online content based on the information of dynamic evolution at the early stage, and proposes a popularity prediction algorithm which makes use of the locality property and combines multiple dynamic factors. The proposed algorithm is further evaluated with the Douban group dataset. The experimental results show that, compared with the baseline methods, our method achieves relatively better performance in predicting the popularity of forum threads.

    参考文献
    [1] Ugander J, Backstrom L, Marlow C, Kleinberg J. Structural diversity in social contagion. Proc. of the National Academy of Sciences, 2012,109(16):5962-5966. [doi: 10.1073/pnas.1116502109]
    [2] Yang J, Leskovec J. Patterns of temporal variation in online media. In: Proc. of the fourth ACM Int'l Conf. on Web Search and Data Mining. Hong Kong: ACM Press, 2011. 177-186. [doi: 10.1145/1935826.1935863]
    [3] Figueiredo F. On the prediction of popularity of trends and hits for user generated videos. In: Proc. of the 6th ACM Int'l Conf. on Web Search and Data Mining. Rome: ACM Press, 2013. 741-746. [doi: 10.1145/2433396.2433489]
    [4] Chatzopoulou G, Sheng C, Faloutsos M. A first step towards understanding popularity in YouTube. In: Proc. of the INFOCOM IEEE Conf. on Computer Communications Workshops. San Diego: IEEE, 2010. 1-6. [doi: 10.1109/INFCOMW.2010.5466701]
    [5] Borghol Y, Mitra S, Ardon S, Carlsson N, Eager D, Mahanti A. Characterizing and modelling popularity of user-generated videos. Performance Evaluation, 2011,68(11):1037-1055. [doi: 10.1016/j.peva.2011.07.008]
    [6] Figueiredo F, Benevenuto F, Almeida JM. The Tube over time: Characterizing popularity growth of YouTube videos. In: Proc. of the 4th ACM Int'l Conf. on Web Search and Data Mining. Hong Kong: ACM Press, 2011. 745-754. [doi: 10.1145/1935826. 1935925]
    [7] Borghol Y, Ardon S, Carlsson N, Eager D, Mahanti A. The untold story of the clones: Content-Agnostic factors that impact YouTube video popularity. In: Proc. of the 18th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Beijing: ACM Press, 2012. 1186-1194. [doi: 10.1145/2339530.2339717]
    [8] Brodersen A, Scellato S, Wattenhofer M. YouTube around the world: Geographic popularity of videos. In: Proc. of the 21st Int'l Conf. on World Wide Web. Lyon: ACM Press, 2012. 241-250. [doi: 10.1145/2187836.2187870]
    [9] Yin P, Luo P, Wang M, Lee WC. A straw shows which way the wind blows: Ranking potentially popular items from early votes. In: Proc. of the 5th ACM Int'l Conf. on Web Search and Data Mining. Seattle: ACM Press, 2012. 623-632. [doi: 10.1145/2124295. 2124370]
    [10] Ahmed M, Spagna S, Huici F, Niccolini S. A peek into the future: Predicting the evolution of popularity in user generated content. In: Proc. of the 6th ACM Int'l Conf. on Web Search and Data Mining. Rome: ACM Press, 2013. 607-616. [doi: 10.1145/2433396. 2433473]
    [11] Li HT, Ma XQ, Wang F, Liu JC, Xu K. On popularity prediction of videos shared in online social networks. In: Proc. of the 22nd ACM Int'l Conf. on Information & Knowledge Management. San Francisco: ACM Press, 2013. 169-178. [doi: 10.1145/2505515. 2505523]
    [12] Pinto H, Almeida JM, Gonçalves MA. Using early view patterns to predict the popularity of YouTube videos. In: Proc. of the 6th ACM Int'l Conf. on Web Search and Data Mining. Rome: ACM Press, 2013. 365-374. [doi: 10.1145/2433396.2433443]
    [13] Zaman T, Fox EB, Bradlow ET. A Bayesian approach for predicting the popularity of Tweets. arXiv e-print 1304.6777. 2013.
    [14] Ma HX, Qian WN, Xia F, He XF, Xu J, Zhou AY. Towards modeling popularity of microblogs. Frontiers of Computer Science in China, 2013,7(2):171-184. [doi: 10.1007/s11704-013-3901-9]
    [15] Kupavskii A, Umnov A, Gusev G, Serdyukov P. Predicting the audience size of a Tweet. In: Proc. of the 7th Int'l Conf. on Weblogs and Social Media. Cambridge: The AAAI Press, 2013.
    [16] Jenders M, Kasneci G, Naumann F. Analyzing and predicting viral Tweets. In: Proc. of the 22nd Int'l Conf. on World Wide Web Companion. Republic and Canton of Geneva: ACM Press, 2013. 657-664.
    [17] Hong LJ, Dan O, Davison BD. Predicting popular messages in Twitter. In: Proc. of the 20th Int'l Conf. on Companion on World Wide Web. Hyderabad: ACM Press, 2011. 57-58. [doi: 10.1145/1963192.1963222]
    [18] Chen GH, Nikolov S, Shah D. A latent source model for nonparametric time series classification. In: Proc. of the Advances in Neural Information Processing Systems. 2013. 1088-1096.
    [19] Ma ZY, Sun AX, Cong G. Will this #hashtag be popular tomorrow? In: Proc. of the 35th Int'l ACM SIGIR Conf. on Research and Development in Information Retrieval. Portland: ACM Press, 2012. 1173-1174. [doi: 10.1145/2348283.2348525]
    [20] Szabo G, Huberman BA. Predicting the popularity of online content. Communications of the ACM, 2010,53(8):80-88. [doi: 10.1145/1787234.1787254]
    [21] Lerman K, Hogg T. Using a model of social dynamics to predict popularity of news. In: Proc. of the 19th Int'l Conf. on World Wide Web. Raleigh: ACM Press, 2010. 621-630. [doi: 10.1145/1772690.1772754]
    [22] Khosla A, Sarma A, Hamid R. What makes an image popular? In: Proc. of the 23rd Int'l Conf. on World Wide Web Companion. Seoul: ACM Press, 2014. 867-876. [doi: 10.1145/2566486.2567996]
    [23] Cheng J, Adamic LA, Dow PA. Can cascades be predicted? In: Proc. of the 23rd Int'l Conf. on World Wide Web Companion. Seoul: ACM Press, 2014. 925-936. [doi: 10.1145/2566486.2567997]
    [24] Wu F, Huberman BA. Novelty and collective attention. Proc. of The National Academy of Sciences, 2007,104(45):17599-17601. [doi: 10.1073/pnas.0704916104]
    [25] Ye L, Keogh E. Time series shapelets: A new primitive for data mining. In: Proc. of the 15th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Paris: ACM Press, 2009. 947-956. [doi: 10.1145/1557019.1557122]
    [26] Japkowicz N, Stephen S. The class imbalance problem: A systematic study. Intelligent Data Analysis, 2002,6(5):429-449.Zhou ZH. Ensemble Methods: Foundations and Algorithms. Chapman & Hall/CRC, 2012. 72-73.
    相似文献
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

孔庆超,毛文吉.基于动态演化的讨论帖流行度预测.软件学报,2014,25(12):2767-2776

复制
分享
文章指标
  • 点击次数:6928
  • 下载次数: 8361
  • HTML阅读次数: 2752
  • 引用次数: 0
历史
  • 收稿日期:2014-05-06
  • 最后修改日期:2014-08-21
  • 在线发布日期: 2014-12-04
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第19919642位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号