Abstract
The rapid increasing popularity of micro-blogging has made it an important information seeking channel. Keyphrase extraction is an effective way for summarizing and analyzing micro-blogging content, which can help users gain insights into internet hotspots. Existing methods for keyphrase extraction usually unilaterally consider phrase frequency or user retweet count as key factors. However, those methods may neglect the relationships between different phrases and the importance of user influence to further information diffusion. Generally, phrases shown in the influential users’ micro-blogs are more likely to attract other users’ interest, making them more likely to be diffused in the near future. Besides, phrases may have relations with each other, and some phrases usually have similar diffusion paths and attract the attention of the same population. In this paper, by comprehensively considering all the above mentioned factors to detect micro-blogging keyphrases, we proposed a novel model. The proposed model first detect high frequency term from abundant micro-blogs as candidate keyphrases, then construct a relation graph about them with user interest and user following web. Finally, we rank those candidates with graph models for realizing keyphrases detection. Experiments show this model is very effective for micro-blogging keyphrase extraction.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aral, S., Brynjolfsson, E., Alstyne, M.V.: Productivity Effects of Information Diffusion in Networks, The MIT Center for Digital Business, paper 234 (2007)
Barker, K., Cornacchia, N.: Using Noun Phrase Heads to Extract Document Keyphrases. In: Hamilton, H.J. (ed.) Canadian AI 2000. LNCS (LNAI), vol. 1822, pp. 40–52. Springer, Heidelberg (2000)
Bellaachia, A., Al-Dhelaan, M.: NE-Rank: A Novel Graph-Based Keyphrase Extraction in Twitter. In: Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology, pp. 372–379 (2012)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of the Seventh International Conference on World Wide Web, pp. 107–117 (1998)
Celli, F., Di Lascio, F.M.L., Magnani, M., Pacelli, B., Rossi, L.: Social network data and practices: The case of FriendFeed. In: Chai, S.-K., Salerno, J.J., Mabry, P.L. (eds.) SBP 2010. LNCS, vol. 6007, pp. 346–353. Springer, Heidelberg (2010)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: the million follower fallacy. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pp. 10–17 (2010)
Cheng, J., Sun, A., Hu, D., Zeng, D.: An information diffusion based recommendation framework for micro-blogging. Journal of the Association for Information Systems 12(7), 463–486 (2011)
Choudhury, M.D., Lin, Y.-R., Sundaram, H.: How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pp. 34–41 (2010)
Ding, Z., Zhang, Q., Huang, X.: Keyphrase Extraction from Online News Using Binary Integer Programming. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, pp. 165–173 (2011)
Haveliwala, T.: Topic Sensitive PageRank. In: Proceedings of the 11th International World Wide Web Conference, pp. 517–526 (2002)
Hussey, R., Williams, S., Mitchell, R., Field, I.: A Comparison of Automated Keyphrase Extraction Techniques and of Automatic Evaluation vs. Human Evaluation. International Journal on Advances in Life Sciences 4(3&4), 136–153 (2012)
Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65 (2007)
Nakagawa, H., Mori, T.: A simple but powerful automatic term extraction method. In: Proceedings of COLING 2002 on COMPUTERM 2002: Second International Workshop on Computational Terminology, pp. 1–7 (2002)
Li, X., Liu, B., Yu, P.: Time sensitive ranking with application to publication search. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pp. 893–898 (2008)
Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic keyphrase extraction via topic decomposition. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp. 366–376 (2010)
Lui, M., Baldwin, T.: Cross-domain Feature Selection for Language Identification. In: Proceedings of the Fifth International Joint Conference on Natural Language Processing, pp. 553–561 (2011)
Yang, M., Lee, J., Lee, S., Rim, H.: Finding interesting posts in Twitter based on retweet graph analysis. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1073–1074 (2012)
Mei, Q., Shen, X., Zhai, C.: Automatic labeling of multinomial topic model. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 490–499 (2007)
Mori, J., Ishizuka, M., Matsuo, Y.: Extracting Keyphrases to Represent Relations in Social Networks from Web. In: Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, pp. 2820–2827 (2007)
Paukkeri, M., Nieminen, I., Pöllä, M., Honkela, T.: A Language-independent Approach to Keyphrase Extraction and Evaluation. In: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 237–252 (2008)
Song, S., Li, Q., Zheng, N.: A spatio-temporal framework for related topic search in micro-blogging. In: An, A., Lingras, P., Petty, S., Huang, R. (eds.) AMT 2010. LNCS, vol. 6335, pp. 63–73. Springer, Heidelberg (2010)
Song, S., Li, Q., Zheng, X.: Detecting popular topics in micro-blogging based on a user interest-based model. In: Proceedings of the 2012 International Joint Conference on Neural Networks, pp. 1–8 (2012)
Wan, X., Xiao, J.: Single Document Keyphrase Extraction Using Neighborhood Knowledge. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence, pp. 855–860 (2008)
Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, pp. 261–270 (2010)
Wu, W., Zhang, B., Ostendorf, M.: Automatic generation of personalized annotation tags for twitter users. In: Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 689–692 (2010)
Zhao, W., Jiang, J., He, J., Song, Y., Achananuparp, P., Lim, E.P., Li, X.: Topical Keyphrase Extraction from Twitter. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 379–388 (2011)
Cho, V., Esfahbod, B., Mansouri, M.: City of New York on Twitter:@ NYCGov. In: Proceedings of the 13th Annual International Conference on Digital Government Research, pp. 274–275 (2012)
Macdonald, C., Ounis, I.: Voting Techniques for Expert Search. J. Knowledge and Information Systems, 259–280 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Song, S., Meng, Y., Sun, J. (2014). Detecting Keyphrases in Micro-blogging with Graph Modeling of Information Diffusion. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-13560-1_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13559-5
Online ISBN: 978-3-319-13560-1
eBook Packages: Computer ScienceComputer Science (R0)