Exploiting concept drift to predict popularity of social multimedia in microblogs

Information Sciences

Volumn 339, Issue , 2016, Pages 310-331

  Li, Cheng Te ^a   Shan, Man Kwan ^b   Jheng, Shih Hong ^b   Chou, Kuan Ching ^b  

^a RESEARCH CENTER FOR INFORMATION TECHNOLOGY INNOVATION   (Taiwan)

^b NATIONAL CHENGCHI UNIVERSITY   (Taiwan)

Author keywords

Concept drift; Information diffusion; Microblog social network; Popularity prediction; Social multimedia

Indexed keywords

FORECASTING; MULTIMEDIA SERVICES; SOCIAL NETWORKING (ONLINE);

CONCEPT DRIFTS; INFORMATION DIFFUSION; MICRO-BLOG; POPULARITY PREDICTIONS; SOCIAL MULTIMEDIA;

CLASSIFICATION (OF INFORMATION);

EID: 84963801035     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2016.01.009     Document Type: Article

References (54)

1. M. Ahemd, S. Spagna, F. Huici, S. Niccolini A peek into the future: predicting the evolution of popularity in user generated content Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) 2013
2. I. Arapakis, B.B. Cambazoglu, M. Lalmas On the feasibility of predicting news popularity at cold start Proceedings of the Sixth International Conference on Social Informatics (SocInfo) 2014 290 299
3. P. Bao, H.-W. Shen, J. Huang, X.-Q. Cheng Popularity prediction in microblogging network: a case study on Sina Weibo Proceedings of ACM International World Wide Web Conference (WWW) 2013
4. P. Bao, H.-W. Shen, X. Jin, X.-Q. Cheng Modeling and predicting popularity dynamics of microblogs using self-excited Hawkes processes Proceedings of ACM International World Wide Web Conference (WWW) Companion 2015
5. R. Bandari, S. Asur, B.A. Huberman The pulse of news in social media: forecasting popularity Proceedings of AAAI International Conference on Weblogs and Social Media (ICWSM) 2012
6. A. Bifet, J. Gama, M. Pechenizkiy, I. Žliobaite Handling concept drift: importance, challenges & solutions. Tutorial Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2011
7. L. Breiman Random forests Mach. Learn. 45 1 2001 5 32
8. M. Cha, H. Kwak, P. Rodriguez, Y.Y. Ahn, S. Moon Analyzing the video popularity characteristics of large-scale user generated content systems IEEE/ACM Trans. Netw. 17 5 2009 1357 1370
9. W. Ding, Y. Shang, L. Guo, X. Hu, R. Yan, T. He Video popularity prediction by sentiment propagation via implicit network Proceedings of ACM International Conference on Information and Knowledge Management (CIKM) 2015
10. J. Famaey, T. Wauters, F.D. Turck On the merits of popularity prediction in multimedia content catching Integrated Network Management 2011
11. F. Figueiredo On the prediction of popularity of trends and hits for used generated videos Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) 2013
12. F. Figueiredo, F. Benevenuto, J.M. Almeida The tube over time: characterizing popularity growth of YouTube videos Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) 2011
13. W. Galuba, K. Aberer, D. Chakraborty, Z. Despotovic, W. Kellerer Outtweeting the twitters: predicting information cascades in microblogs Proceedings of Third Conference on Online Social Networks (WOSN) 2010
14. S. Gao, J. Ma, Z. Chen Modeling and predicting retweeting dynamics on microblogging platforms Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) 2015
15. M. Harries, K. Horn Detecting concept drift in financial time series prediction using symbolic machine learning Proceedings of Australian Joint Conference on Artificial Intelligence 1995
16. X. He, M. Gao, M.-Y. Kan, Y. Lin, K. Sugiyama Predicting the popularity of Web 2.0 items based user comments Proceedings of ACM International Conference on Research and Development in Information Retrieval (SIGIR) 2014
17. C.-T. Ho, C.-T. Li, S.-D. Lin Modeling and visualizing information propagation in a micro-blogging platform Proceedings of IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM) 2011
18. L. Hong, O. Dan, B.D. Davison Predicting Popular Messages in Twitter Proceedings of ACM International World Wide Web Conference (WWW) 2011
19. J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, A. Bouchachia A survey on concept drift adaption ACM Comput. Surv. 46 4 2014 Article 44
20. L. Jiang, Y. Miao, Y. Yang, Z. Lan, A.G. Hauptmann Viral video style: a closer look at viral videos on YouTube Proceedings of ACM International Conference on Multimedia Retrieval (ICMR) 2014
21. X. Jin, A. Gallagher, L. Cao, J. Luo, J. Han The wisdom of social multimedia: using Flickr for prediction and forecast Proceedings of ACM International Conference on Multimedia (MM) 2010
22. A. Khosla, A.D. Sarma, R. Hamid What makes an image popular? Proceedings of ACM International World Wide Web Conference (WWW) 2014
23. J.Z. Kolter, M.A. Maloof Dynamic weighted majority: a new ensemble method for tracking concept drift Proceedings of IEEE International Conference on Data Mining (ICDM) 2003
24. H. Lakkaraju, J. Ajmera Attention prediction on social media brand pages Proceedings of ACM International Conference on Information and Knowledge Management (CIKM) 2011
25. J.G. Lee, S. Moon, K. Salamatian Modeling and predicting the popularity of online contents with Cox proportional hazard regression model J. Neurocomput. 76 1 2012 134 145
26. H. Li, X. Ma, F. Wang, J. Liu, K. Xu On popularity prediction of videos shared in online social networks Proceedings of ACM International Conference on Information and Knowledge Management (CIKM) 2013
27. K. Lerman, T. Hogg Using a model of social dynamics to predict popularity of news Proceedings of ACM International World Wide Web Conference 2010
28. K. Lerman, R. Ghosh Information contagion: an empirical study of the spread of news on Digg and Twitter social networks Proceedings of AAAI International Conference on Weblogs and Social Media (ICWSM) 2010
29. S.K. Maity, A. Gupta, P. Goyal, A. Mukherjee A stratified learning approach for predicting the popularity of twitter idioms Proceedings of AAAI International Conference on Web and Social Media (ICWSM) 2015
30. P.J. McParlane, Y. Moshfeghi, J.M. Jose "Nobody Comes Here Anymore, it's too crowded": predicting image popularity on Flickr Proceedings of ACM International Conference on Multimedia Retrieval (ICMR) 2014
31. M. Naaman, H. Becker, L. Gravano Hip and trendy: characterizing emerging trends on Twitter J. Am. Soc. Inf. Sci. Technol. (JASIST) 62 5 2011 902 918
32. N. Naveed, T. Gottron, J. Kunegis, A.C. Alhadi Bad news travel fast: a content-based analysis of interestingness on Twitter Proceedings of ACM International Conference on Web Science 2011
33. H. Pinto, J.M. Almeida, M.A. Goncalves Using early view patterns to predict the popularity of YouTube videos Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) 2013
34. K. Radinsky, K. Svore, S. Dumais, J. Teevan, A. Bocharov, E. Horvitz Modeling and predicting behavioral dynamics on the Web Proceedings of ACM International World Wide Web Conference (WWW) 2012
35. W.N. Street, Y. Kim A Streaming Ensemble Algorithm (SEA) for large-scale classification Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (KDD) 2001
36. G. Szabo, B.A. Huberman Predicting the popularity of online content Commun. ACM 53 8 2010 80 88
37. A. Tsymbal The Problem of Concept Drift: Definitions and Related Work 2004 Trinity College Technical Report
38. D.R. Wilson, T.R. Martinez Improved heterogeneous distance functions J. Artif. Intell. Res. 6 1 1997 1 34
39. A. Tatar, P. Antoniadis, M.D. de Amorim, S. Fdida From popularity prediction to ranking online news Social Netw. Anal. Mining 4 1 2014 Article 174
40. O. Tsur, A. Rappoport What's in a hashtag? Content based prediction of the spread of ideas in microblogging communities Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) 2012
41. A. Tsymbla, M. Pechenizkiy, P. Cunningham, S. Puuronen Dynamic integration of classifiers for handling concept drift Int. J. Multi-Sens. Multi-Sour. Inf. Fusion 9 1 2008 56 68
42. D. Vallet, S. Berkovsky, S. Ardon, A. Mahanti, M.A. Kaafar Characterizing and predicting viral-and-popular video content Proceedings of ACM International Conference on Information and Knowledge Management (CIKM) 2015
43. M. Vasconelos, J.M. Almeida, M.A. Goncalves Predicting the popularity of micro-reviews: a foursquare case study Inf. Sci. 325 2015 335 374
44. A. Wang, T. Chen, M.-Y. Kan Re-tweeting from a linguistic perspective Proceedings of North American Chapter of the Association for Computational Linguistics: Human Language Techniques (NAACL-HLT) 2012
45. H. Wang, W. Fan, P.S. Yu, J. Han Mining concept-drifting data streams using ensemble classifiers Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (KDD) 2003
46. L. Xu, J.A. Duan, A. Whinston Path to purchase: a mutually exciting point process model for online advertising and conversion Manage. Sci. 60 6 2014 1392 1412
47. J. Xu, V.d. Schaar M., J. Liu, H. Li., Forecasting, dia IEEE J. Sel. Top. Signal Process. 9 2 2014 330 343
48. J. Yang, S. Counts Predicting the speed, scale, and range of information diffusion in Twitter Proceedings of AAAI International Conference on Weblogs and Social Media (ICWSM) 2010
49. J. Yang, J. Leskovec Patterns of temporal variation in online media Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) 2011
50. P. Yin, P. Luo, M. Wang, W.-C. Lee A straw shows which way the wind blows: ranking potentially popular items from early votes Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) 2012
51. A.H. Zadeh, R. Sharda Modeling brand post popularity dynamics in online social networks Decis. Support Syst. 65 2014 59 68
52. Q. Zhao, M.A. Erdogdu, H.Y. He, A. Rajaraman, J. Leskovec SEISMIC: a self-exciting point process model for predicting Tweet popularity Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (KDD) 2015
53. Y. Zhou, L. Chen, C. Yang, D.M. Chiu Video popularity dynamics and its implication for replication IEEE Trans. Multimedia 17 8 2015 1273 1285
54. I. Žliobaite, Learning under Concept Drift: An Overview. Technical Report, Vilnius University, 2009.