Pattern Analysis of News Lifecycle in a Social News Aggregation Service

소셜 뉴스 집적 서비스에서의 카테고리별 뉴스 수명주기 패턴 분석

  • Published : 2009.05.31

Abstract

The purpose of this paper is to present a statistical model that can predict the rapid shift of users' attention by analyzing the lifecycle patterns of news in a social news aggregation service. Internet news service sites have a distinct characteristic in a sense that users' attention change very quickly in a short period of time. In this research, we propose a regression model for each news category which can model the decay pattern of users' attention and the content promotion policy of a social news aggregator is proven to be a major source of the rapid growth in the popularity of news. The proposed model is expected to be useful for evaluation of the social news aggregation service provider's content promotion policy that attempts to maximize users' attention as well as the diversity of news contents.

본 연구는 소셜 뉴스 집적 서비스(Social news aggregation service)에서 뉴스의 수명주기 패턴을 카테고리 별로 분석하여 사용자의 관심 변화를 예측할 수 있는 통계적 모델 제시를 목적으로 한다. 인터넷 뉴스는 사용자의 관심이 단시간에 집중되며 시간에 따른 사용자 관심의 쇠퇴가 명확하게 드러나는 웹 자원으로, 사용자 관심 변화에 대한 다양한 연구가 현재 진행 중에 있다. 본 연구는 뉴스의 수명주기를 카테고리 별로 분석하여 사용자 관심의 쇠퇴 정도를 예측할 수 있는 통계적 모델을 도출하였으며 소셜 뉴스 서비스 제공자(Social news aggregator)의 콘텐트 게시 정책이 사용자 관심의 급격한 성장을 발생시키는 주된 외부적 요인임을 분석하였다. 본 연구에서 제안된 인터넷 뉴스의 수명주기 모델은 독자의 관심을 지속시키면서 다양한 콘텐트를 공급하려는 소셜 뉴스 집적 서비스에 유용하게 적용될 수 있다.

Keywords

References

  1. Alexa.com, http://www.alexa.com.
  2. Chen, C. C., Chen, Y. T., and Chen, M. C., “An Aging Theory for Event Life Cycle Modeling,” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. PART A:SYSTEMS AND HUMANS, Vol. 7, No. 2, 2007, pp. 237-248.
  3. Chen, C. C. and Chen, M. C., PVA:A Self-Adaptive Personal View Agent, Journal of Intelligent Information Systems, Vol. 18, No. 2, 2002, pp. 173-194. https://doi.org/10.1023/A:1013629527840
  4. Chicco, G., Napoli, R., and Pigilone, F., “Load pattern clustering for short-term load forecasting of anomalous days,” IEEE Porto Power Tech Conference, Porto, Portugal, 2001.
  5. CNN, http://www.cnn.com.
  6. Digg.com, http://www.Digg.com.
  7. Digg.com API, http://apidoc.Digg.com.
  8. Flavia’n, C. and Gurrea, R., “Reading newspapers on the Internet:the influence of web sites’ attributes,” Internet Research, Vol. 18, No. 1, 2008, pp. 26-45. https://doi.org/10.1108/10662240810849577
  9. Garofalakis, J., Kappos, P., and Mourloukos, D., “Web site optimization using page popularity,” IEEE Internet Computing, Vol. 3, No. 4, 1999, pp. 22-29. https://doi.org/10.1109/4236.780957
  10. Gurzick, D. and Lutters, W. G., “From the personal to the profound:understanding the blog life cycle,” Conference on Human Factors in Computing Systems, Montreal, Quebec, Canada, 2006, pp. 827-832.
  11. Hayter, A. J., Probability and Statistics for Engineers and Scientists, Duxbury Press, 2001.
  12. Kohonen., T., Hynninen, J., Hynninen, J., and Kangas, J., SOM-PAK, The Self-Organizing Map Program Package, User’s Guide, Helsinki University of Technology, 1995.
  13. Lerman, K. and Galstyan, A., “Analysis of Social Voting Patterns on Digg,” Proceedings of the first workshop on Online social networks, Seattle, Washington, USA, 2008, pp. 7-12.
  14. Lerman, K., “Social Information Processing in Social News Aggregation,” IEEE Internet Computing, Vol. 11, No. 6, 2007, pp. 16-28.
  15. The New York Times, http://www.times.com.
  16. O’Reilly, T., “What is Web 2.0:Design Patterns and Business Models for the next generation of software,” http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html, 2005.
  17. Perseus, The Blogging Iceberg, Perseus Survey Solutions (http://www.perseus.com/blogsurvey), 2004.
  18. Saleem, M., “The Decline and Fall of Quality on Digg, http://www.readriteweb.com/archives/the_decline_and_fall_of_quality_on_digg.php, 2008.
  19. Reddit.com, http://www.Reddit.com.
  20. Wu, F. and Huberman, B. A., “Popularity, Novelty and Attention,” Conference on Electronic Commerce, Chicago, Il, USA, 2008, pp. 240-245.
  21. Wu, F. and Huberman, B. A., “The economics of attention:maximizing user value in information-rich environments, International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, 2007, pp. 16-20.