A new approach of ranking prevalent news topics from social media using unsupervised techniques
The precious data from online origin has developed into a extended research. The mass media and news media provides the daily events to the common people. Huge amount of information is been achieved by an online social media suchlike Twitter, which contains more information about news-associated content. It is necessary to find a way to filter noise, for these resources to be useful and grab the content that is depend on the similarity to news media. Despite after the noise is eliminated the excessive data still remain in the data so it is essential to prioritize it for utilization. We are introducing three factors for prioritization. The unsupervised technique finds the news topics that are common in the pair of social media and news media, and then ranks them by the applicability factors such as MF, UA and UI. Initially the temporal prevalence of the appropriate topic in news media focus (MF). Secondary the temporal prevalence of the appropriate topic in social media illustrates the user attention (UA). Finally the interconnection among the social media users who specify this topic demonstrates the power of the society who is discussing; it is termed as the user interaction (UI).
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, Jan. 2003.
T. Hofmann, “Probabilistic latent semantic analysis,” in Proc. 15th Conf. Uncertainty Artif. Intell., 1999, pp. 289–296.
T. Hofmann, “Probabilistic latent semantic indexing,” in Proc. 22nd Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, Berkeley, CA, USA, 1999, pp. 50–57.
C. Wartena and R. Brussee, “Topic detection by clustering keywords,” in Proc. 19th Int. Workshop Database Expert Syst. Appl. (DEXA), Turin, Italy, 2008, pp. 54–58.
M. Cataldi, L. Di Caro, and C. Schifanella, “Emerging topic detec- tion on Twitter based on temporal and social terms in Proc. 10th Int. Workshop Multimedia Data Min. (MDMKDD), Washington, DC, USA, 2010, Art. no. 4. [Online]. http://doi.acm.org/10.1145/1814245.1814249
W. X. Zhao et al., “Comparing Twitter and traditional media using topic models,” in Advances in Information Retrieval. Heidelberg, Germany: Springer Berlin Heidelberg, 2011, pp. 338–349.
Q. Diao, J. Jiang, F. Zhu, and E.-P. Lim, “Finding bursty topics from microblogs,” in Proc. 50th Annu. Meeting Assoc. Comput. Linguist. Long Papers, vol. 1. 2012, pp. 536–544.
H. Yin, B. Cui, H. Lu, Y. Huang, and J. Yao, “A unified model for stable and temporal topic detection from social media data,” in Proc. IEEE 29th Int. Conf. Data Eng. (ICDE), Brisbane, QLD, Australia, 2013,
pp. 661–672.
C. Wang, M. Zhang, L. Ru, and S. Ma, “Automatic online news topic ranking using media focus and user attention based on aging theory,” in Proc. 17th Conf. Inf. Knowl. Manag., Napa County, CA, USA, 2008,
qq. 1033–1042.
C. C. Chen, Y.-T. Chen, Y. Sun, and M. C. Chen, “Life cycle modeling of news events using aging theory,” in Machine Learning: ECML 2003. Heidelberg, Germany: Springer Berlin Heidelberg, 2003,
rr. 47–59.
J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman, and J. Sperling, “TwitterStand: News in tweets,” in Proc. 17th ACM SIGSPATIAL Int. Conf. Adv. Geograph. Inf. Syst., Seattle, WA, USA, 2009, pp. 42–51.
O. Phelan, K. McCarthy, and B. Smyth, “Using Twitter to recom-mend real-time topical news,” in Proc. 3rd Conf. Recommender Syst., New York, NY, USA, 2009, pp. 385–388.
K. Shubhankar, A. P. Singh, and V. Pudi, “An efficient algorithm for topic ranking and modeling topic evolution,” in Database Expert Syst. Appl., Toulouse, France, 2011, pp. 320–330.
S. Brin and L. Page, “Reprint of: The anatomy of a large-scale hypertex-tual web search engine,” Comput. Netw., vol. 56, no. 18, pp. 3825–3833, 2012.