Improving news ranking by community tweets

ABSTRACT

Users frequently express their information needs by means of short and general queries that are difficult for ranking algorithms to interpret correctly. However, users' social contexts can offer important additional information about their information needs which can be leveraged by ranking algorithms to provide augmented, personalized results. Existing methods mostly rely on users' individual behavioral data such as clickstream and log data, but as a result suffer from data sparsity and privacy issues. Here, we propose a Community Tweets Voting Model (CTVM) to re-rank Google and Yahoo news search results on the basis of open, large-scale Twitter community data. Experimental results show that CTVM outperforms baseline rankings from Google and Yahoo for certain online communities. We propose an application scenario of CTVM and provide an agenda for further research.

References

1. F. Abel, Q. Gao, G.-J. Houben, and K. Tao. Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web. In Proceedings of ACM WebSci '11, 3rd International Conference on Web Science, Koblenz, Germany, 2011.
2. J. Bollen, B. Gonçalves, R. GuangChen, and H. Mao. Happiness is assortative in online social networks. ALife, 17(3):237--251, 2011.
3. J. Bollen, H. Mao, and A. Pepe. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In ICWSM, 2011.
4. J. Bollen, H. Mao, and X.-J. Zeng. Twitter mood predicts the stock market.J. Comput. Science, 2(1):1--8, 2011.
5. K. Jarvelin and J. Kekalainen. Cumulated gain-based evaluation of ir techniques.ACM Trans. Inf. Syst., 20:422--446, October 2002.
6. X. Jin, C. Wang, J. Luo, X. Yu, and J. Han. Likeminer: a system for mining the power of 'like' in social media networks. InProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, 2011.
7. T. Joachims and F. Radlinski. Search engines that learn from implicit feedback. Computer, 40:34--40, August 2007.
8. J. M. Kleinberg. Authoritative sources in a hyperlinked environment.J. ACM, 46:604--632, September 1999.
9. H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, WWW '10, 2010.
10. L. Limam, D. Coquil, H. Kosch, and L. Brunie. Extracting user interests from search query logs: A clustering approach. In Proceedings of the 2010 Workshops on Database and Expert Systems Applications, DEXA '10, 2010.
11. J. Liu, P. Dolan, and E. R. Pedersen. Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on Intelligent user interfaces, IUI '10, 2010.
12. X. Liu and V. von Brzeski. Computational community interest for ranking. In Proceedings of the 18th ACM conference on Information and knowledge management, CIKM '09, 2009.
13. Y. Lv and C. Zhai. Positional language models for information retrieval. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, 2009.
14. L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, November 1999.
15. S. Phuvipadawat and T. Murata. Breaking news detection and tracking in twitter. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology -Volume 03, WI-IAT '10, 2010.
16. T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web, WWW '10, 2010.
17. G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing.Commun. ACM, 18:613--620, November 1975.

Index Terms

• Information systems

  • Information retrieval