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Predicting the Popularity of Internet Memes with Hilbert-Huang Spectrum

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Multidisciplinary Social Networks Research (MISNC 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 540))

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Abstract

This paper investigates the popularity of Internet memes exemplified by Twitter hashtags. A data set of more than 16 million tweets containing 690 thousand hashtags has been prepared by using the Twitter’s Streaming API. Some early adoption properties of a hashtag are used to predict its later popularity level. One such property is the adoption time series of the tag. Differential series resulting from differences between two successive adoption timestamps indicates the diffusion speed of a tag. Mean and standard deviation of the differential series have been used to predict the popularity level of a hashtag in previous studies. However, the mean and standard deviation statistics cannot catch the oscillation property of a time series. This study employs the Hilbert-Huang Transform [1] to analyze the differential series. Experimental results show that the derived Hilbert-Huang spectrum can help predict the popularity level of a hashtag at the later stage.

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References

  1. Huang, N.E., Shen, Z.: The Hilbert-Huang Transform and its Applications. World Scientific Publishing Company, Singapore (2005)

    Book  Google Scholar 

  2. Wikipedia: Twitter (accessed June 4, 2015). http://en.wikipedia.org/wiki/Twitter

  3. Ma, Z., Sun, A., Cong, G.: On Predicting the Popularity of Newly Emerging Hashtags in Twitter. Journal of the American Society for Information Science and Technology 64(7), 1399–1410 (2013)

    Article  Google Scholar 

  4. Weng, L., Menczer, F., Ahn, Y.Y.: Virality Prediction and Community Structure in Social Networks. Scientific Reports 3(2522) (2013)

    Google Scholar 

  5. Weng, L., Menczer, F., Ahn, Y.Y.: Predicting successful memes using network and community structure. In: Proceedings of the 8th International AAAI Conference on Weblog and Social Media, pp. 535–544. AAAI (2014)

    Google Scholar 

  6. Twitter: API Overview (accessed June 4, 2015). https://dev.twitter.com/overview/api

  7. Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web (WWW), pp. 591–600. ACM (2010)

    Google Scholar 

  8. Tsur, O., Rappoport, A.: What’s in a hashtag? content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM), pp. 643–652 (2012)

    Google Scholar 

  9. Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: Proceedings of IEEE International Conference on Social Computing, pp. 177–184. IEEE (2010)

    Google Scholar 

  10. Salganik, M., Dodds, P., Watts, D.: Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market. Science 311(5762), 754–756 (2006)

    Article  Google Scholar 

  11. Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: Proceedings of ACM International Conference on Web Search and Data Mining (WSDM), pp. 177–186. ACM (2011)

    Google Scholar 

  12. Szabo, G., Huberman, B.A.: Predicting the Popularity of Online Content. Communication of the ACM 53(8), 80–88 (2010)

    Article  Google Scholar 

  13. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Liu, H.H.: The Empirical Mode Decomposition And The Hilbert Spectrum For Nonlinear And Nonstationary Time Series Analysis. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 454(1971), 903–995 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  14. Breiman, L.: Random Forests. Machine Leaning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  15. Morstatter, F., Pfeffer, J., Liu, H., Carley, K.M.: Is the sample good enough? comparing data from twitter’s streaming api with twitter’s firehose. In: Proceedings of the 7th AAAI International Conference on Weblogs and Social Media. AAAI (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. ShuTe University, 59 HenShan Rd., Yanchao District, Kaohsiung City, Taiwan, R.O.C.

    Shing H. Doong

Authors
  1. Shing H. Doong
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Correspondence to Shing H. Doong .

Editor information

Editors and Affiliations

  1. National University of Kaohsiung, Kaohsiung City, Taiwan

    Leon Wang

  2. Matsuyama University, Matsuyama, Japan

    Shiro Uesugi

  3. National University of Kaohsiung, Kaohsiung City, Taiwan

    I-Hsien Ting

  4. Osaka University, Osaka, Japan

    Koji Okuhara

  5. National University of Kaohsiung, Kaohsiung City, Taiwan

    Kai Wang

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Doong, S.H. (2015). Predicting the Popularity of Internet Memes with Hilbert-Huang Spectrum. In: Wang, L., Uesugi, S., Ting, IH., Okuhara, K., Wang, K. (eds) Multidisciplinary Social Networks Research. MISNC 2015. Communications in Computer and Information Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48319-0_40

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  • DOI: https://doi.org/10.1007/978-3-662-48319-0_40

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48318-3

  • Online ISBN: 978-3-662-48319-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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