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General Information
    • ISSN: 2010-3700
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus(since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 327-332 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.139

Trend Analysis of News Topics on Twitter

Rong Lu and Qing Yang

Abstract—This paper focus on trend analysis of news topics on Twitter, which include trend prediction and reasons analysis for the changes of trend. We first present a novel method to predict the trends of topics on Twitter based on MACD (Moving Average Convergence-Divergence) indicator. It is one of the simplest and most effective tendency indicators in technique analysis of stocks. We improve the original MACD indicator according to the characteristics of news topics on Twitter, define a new concept as trend momentum and use it to predict the trend of news topics. Then, we propose some reasons for the variation of trends. Experimental results show that the trend prediction is simple and effective. And, the reasons for variation of trends are also verified.

Index Terms—Trend analysis, trend prediction, twitter.

Authors are with the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China (e-mail: rlu@nlpr.ia.ac.cn; qyang@ nlpr.ia.ac.cn).


Cite: Rong Lu and Qing Yang, "Trend Analysis of News Topics on Twitter," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 327-332, 2012.

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