A Momentum Theory for Hot Topic Life-cycle: A Case Study of Hot Hashtag Emerging in Twitter
Keywords:
hashtag, hot topic, aging theoryAbstract
The existing work on mining of hot topics is mainly based on topic multiplicity andattention from users in unit time. With the advent of social networking, the weight has been put on the hot topics which can effectively describe the importance and hotness of a topic. However, the researches on the influence exerted by the accumulation of attention towards hot topics and the alternation between hot topics and outdated ones are still relatively weak. In this paper, a novel algorithm for calculating the hotness of topics is proposed based on momentum. The number of the participants, but also the long tail effect of the historical accumulation on the topic is taken into consideration. Through this algorithm, we can accurately build a model for the hot topics on their emerging growing period and effectively describe the whole life circle of the topic. Additionally, the change between hot topics and old ones can be distinguished efficiently. Our experiments show that the process of a topic growing into a hot topic can be detected explicitly. Potential hot topics can be explored and the overdue ones can be rejected respectively.
References
Aldhelaan, M.; Alhawasi, H. (2015); Graph Summarization for Hashtag Recommendation, 015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), 698-702.
Allan, J.; Carbonell, J.; Doddington, G., et al. (1998); Topic Detection and Tracking Pilot tudy Final Report, Proceedings of the DARPA Broadcast News Transcription and Understanding orkshop, 194-218.
Asadi, S.; Hadavandi, E.; Mehmanpazir, F., et al. (2012); Hybridization of evolutionary evenberg-Marquardt neural networks and data pre-processing for stock market prediction, nowledge-Based Systems, 35(15): 245-258.
Aydemir, E.; Koruca, H.I. (2015); A New Production Scheduling Module Using Priority-Rule ased Genetic Algorithm, International Journal of Simulation Modelling, ISBN 1726-4529, 4(3): 450-462.
Bas, E.; Egrioglu, E.; Aladag, C.H., et al. (2015); Fuzzy-time-series network used to forecast inear and nonlinear time series, Applied Intelligence, 43(2): 1-13.
Blekas, K.; Lagaris, I.E. (2013); A Spectral Clustering Approach Based on Newton's Equations f Motion, International Journal of Intelligent Systems, 28(4): 394-410.
Bo, Y.; Ming, L.; Bing-Quan, L., et al. (2012); Detecting hot topics in technology news treams, Machine Learning and Cybernetics (ICMLC), 2012 International Conference on, SBN 2160-133X, 5:1968-1974.
Borgelt, C. (2010); Simple Algorithms for Frequent Item Set Mining, Advances in Machine earning II, ISBN 978-3-642-05178-4, 263(16):351-369.
Caldeira, J.F.; Moura, G.V.; Santos, A.A.P. (2016); Predicting the yield curve using forecast ombinations, Computational Statistics & Data Analysis, 100: 79-98.
Cataldi, M.; Caro, L.D.; Schifanella, C. (2010); Emerging topic detection on Twitter based n temporal and social terms evaluation, Proceedings of the Tenth International Workshop on ultimedia Data Mining, 1-10.
Chang, M.K.; Cheung, W.; Tang, M. (2013); Building trust online: Interactions among rust building mechanisms, Information & Management, 50(7): 439-445.
Chen, C.; Chen, Y.-T.; Sun, Y., et al. (2003); Life Cycle Modeling of News Events Using ging Theory, Machine Learning: ECML 2003, ISBN 978-3-540-20121-2, 2837(7):47-59.
Chen, J.; Yu, J.; Shen, Y. (2012); Towards Topic Trend Prediction on a Topic Evolution odel with Social Connection, The Ieee/wic/acm International Joint Conferences on Web ntelligence & Intelligent Agent Technology, 153-157.
Chen, K.Y.; Luesukprasert, L.; Chou, S.c.T. (2007); Hot Topic Extraction Based on Timeline nalysis and Multidimensional Sentence Modeling, IEEE Transactions on Knowledge and ata Engineering, ISBN 1041-4347, 19(8): 1016-1025.
Chen, T.L. (2012); Forecasting the Taiwan Stock Market with a Novel Momentum-based uzzy Time-series, Review of Economics & Finance, 2:38-50.
Chien Chin, C.; Yao-Tsung, C.; Meng Chang, C. (2007); An Aging Theory for Event Life- ycle Modeling, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions n, ISBN 1083-4427, 37(2): 237-248.
Chung-Hong, L.; Tzan-Feng, C.; Hsin-Chang, Y. (2011); An automatic topic ranking approach or event detection on microblogging messages, Systems, Man and Cybernetics (SMC), 011 IEEE International Conference on, ISBN 1062-922X, 1358-1363
Galperin, E.A. (2011); Information transmittal, Newton's law of gravitation, and tensor pproach to general relativity, Computers & Mathematics with Applications, 62(2): 709-724.
Giannella, C.; Han, J.; Pei, J., et al. (2003); Mining Frequent Patterns in Data Streams at ultiple Time Granularities, Data Mining Next Generation Challenges & Future Directions.
Han, J.; Pei, J.; Yin, Y. (2000); Mining frequent patterns without candidate generation, IGMOD Rec., ISBN 0163-5808, 29(2): 1-12.
Hrelja, M.; Klancnik, S.; Balic, J., et al. (2014); Modelling of a turning process using the ravitational search algorithm, International Journal of Simulation Modelling, ISBN 1726- 529, 13(1): 30-41.
Hwi-Gang, K.; Seongjoo, L.; Sunghyon, K. (2013); Discovering hot topics using Twitter treaming data social topic detection and geographic clustering, Advances in Social Networks nalysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on, 1215-1220.
Jiangfeng, C.; Jianjun, Y.; Yi, S. (2012); Towards Topic Trend Prediction on a Topic volution Model with Social Connection, Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on, 1:153-157.
Jing, G.; Peng, Z.; Tanb, J., et al. (2012); Mining Hot Topics from Twitter Streams, Procedia omputer Science, 9(11): 2008-2011.
Khoo Khyou, B.; Ishizuka, M. (2002); Topic extraction from news archive using TF*PDF lgorithm, Web Information Systems Engineering, 2002. WISE 2002. Proceedings of the Third nternational Conference on, 73-82.
Khulief, Y.A. (2010); Numerical Modelling of Impulsive Events in Mechanical Systems, nternational Journal of Modelling & Simulation, 30: 80-86.
Kotsakos, D.; Sakkos, P.; Katakis, I., et al. (2015); Language agnostic meme-filtering for ashtag-based social network analysis, Social Network Analysis & Mining, 5(1): 1-14.
Li, M.; Tang, M. (2013); Information Security Engineering: a Framework for Research and ractices, International Journal of Computers Communications & Control, 8(4): 578-587.
Liu, M.; Liu, Y.; Xiang, L., et al. (2008); Extracting Key Entities and Significant Events rom Online Daily News, Intelligent Data Engineering and Automated Learning-IDEAL 2008, SBN 978-3-540-88905-2, 5326(26):201-209.
Liu, C.H.; Xiong, W. (2015); Modelling and Simulation of Quality Risk Forecasting in a upply Chain, International Journal of Simulation Modelling, ISBN 1726-4529, 14(2): 359-370.
Ma, H.; Lu, Z.; Li, D., et al. (2014); Mining hidden links in social networks to achieve quilibrium Aî, Theoretical Computer Science, 556:13-24.
Ramesh Kumar, L.; Padmanaban, K.; Balamurugan, C. (2016); Optimal Tolerance Allocation n a Complex Assembly Using Evolutionary Algorithms, International Journal of imulation Modelling, ISBN 1726-4529, 15(1): 121-132.
Salton, G. (1989); Automatic text processing: the transformation, analysis, and retrieval of nformation by computer, ISBN 0-201-12227-8.
Ternik, P.; Rudolf, R. (2013); Laminar Natural Convection of Non-Newtonian Nanofluids in Square Enclosure with Differentially Heated Side Walls, International Journal of Simulation odelling, 12(1): 5-16.
Tran, D.H.; Nguyen, H.L.; Zhao, W., et al. (2011); Towards security in sharing data on loud-based social networks, Information, Communications and Signal Processing (ICICS) 011 8th International Conference on, 1-5.
Wang, C.; Zhang, M.; Ru, L., et al. (2008); Automatic online news topic ranking using media ocus and user attention based on aging theory, Proceedings of the 17th ACM conference on nformation and knowledge management, 1033-1042.
Xiao, Z. (2014); A SOCIAL NETWORK-ORIENTED MINING ALGORITHM FOR HOT OPIC DATA, Computer Applications & Software
Yang, K.W.; Zhang, P.L.; Ge, B.F., et al. (2015); A Variables Clustering Based Differential volution Algorithm to Solve Production Planning Problem, International Journal of imulation Modelling, ISBN 1726-4529, 14: 525-538.
Yang, Y.; Pierce, T.; Carbonell, J. (1998); A study of retrospective and on-line event detection, roceedings of the 21st annual international ACM SIGIR conference on Research and evelopment in information retrieval, 28-36.
Zangerle, E.; Gassler, W.; Specht, G. (2013); On the impact of text similarity functions on ashtag recommendations in microblogging environments, Social Network Analysis & Mining, (4): 889-898.
Zheng, D.; Li, F. (2009); Hot Topic Detection on BBS Using Aging Theory, Web Information ystems and Mining, ISBN 978-3-642-05249-1, 5854(14): 129-138.
Published
Issue
Section
License
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
