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Link to original content: https://doi.org/10.1007/s11192-017-2390-2
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Sleeping beauties in meme diffusion

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Abstract

A sleeping beauty in diffusion indicates that certain information, whether an idea or innovation, will experience a hibernation period before it undergoes a sudden spike of popularity, and this pattern is found widely in the citation history of scientific publications. However, in this study, we demonstrate that the sleeping beauty is an interesting and unexceptional phenomenon in information diffusion; more inspiring is that there exists two consecutive sleeping beauties in the entire lifetime of a meme’s propagation, which suggests that the information, including scientific topics, search queries or Wikipedia entries, which we call memes, will go unnoticed for a period and suddenly attract some attention, and then it falls asleep again and later wakes up with another unexpected popularity peak. Further exploration of this phenomenon shows that the intervals between two wake-ups follow an exponential distributions, both the rising and falling stage lengths, follow power law distributions, and the second wake-up tends to reach its peak in a shorter period of time. In addition, the total volumes of the two wake-ups have positive correlations. Taking these findings into consideration, an upgraded Bass model is presented to well describe the diffusion dynamics of memes on different media. Our results can help understand the common mechanism behind the propagation of different memes and are instructive towards locating the tipping point in marketing or in finding innovative publications in science.

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Notes

  1. https://figshare.com/articles/Meme_popularity_and_diffusion/3187159/1.

References

  • Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–227.

    Article  MATH  Google Scholar 

  • Bauckhage, C. (2011). Insights into internet memes. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, ICWSM-11 (pp. 42–49).

  • Bauckhage, C., Kersting, K., & Hadiji, F. (2013). Mathematical models of fads explain the temporal dynamics of internet memes. In ICWSM (pp. 22–30).

  • Bentley, R. A., Garnett, P., O’Brien, M. J., & Brock, W. A. (2012). Word diffusion and climate science. PLoS ONE, 7(11), 1–9.

    Article  Google Scholar 

  • Bornmann, L., Leydesdorff, L., & Wang, J. (2014). How to improve the prediction based on citation impact percentiles for years shortly after the publication date? Journal of Informetrics, 8(1), 175–180.

    Article  Google Scholar 

  • Braun, T., Glnzel, W., & Schubert, A. (2010). On sleeping beauties, princes and other tales of citation distributions. Research Evaluation, 19(3), 195–202.

    Article  Google Scholar 

  • Burrell, L. Q. (2005). Are “sleeping beauties” to be expected? Scientometrics, 65(3), 381–389.

    Article  Google Scholar 

  • Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329(5996), 1194–1197.

    Article  Google Scholar 

  • Centola, D. (2011). An experimental study of homophily in the adoption of health behavior. Science, 334(6060), 1269–1272.

    Article  Google Scholar 

  • Cheng, J., Adamic, L., Dow, P. A., Kleinberg, J. M, & Leskovec, J. (2014). Can cascades be predicted? In Proceedings of the 23rd International Conference on World Wide Web (pp. 925–936). ACM.

  • Coscia, M. (2013). Competition and success in the meme pool: A case study on quickmeme.com. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, AAAI (pp. 100–109).

  • Coscia, M. (2014). Average is boring: How similarity kills a meme’s success. Scientific Reports, 4, 6477.

    Article  Google Scholar 

  • Dawkins, R. (1976). The selfish gene. Oxford: Oxford University Press.

    Google Scholar 

  • Garfield, E. (1980). Premature discovery or delayed recognition-why. Current Contents, 21, 5–10.

    Google Scholar 

  • Garfield, E. (1989). Essays of an information scientist: Creativity, delayed recognition, and other essays. Current Contents, 23, 3–9.

    Google Scholar 

  • Gleeson, J. P., Ward, J. A., O’Sullivan, K. P., & Lee, W. T. (2014). Competition-induced criticality in a model of meme popularity. Physical Review Letters, 112(048), 701.

    Google Scholar 

  • Gleeson, J. P., O’Sullivan, K. P., Baños, R. A., & Moreno, Y. (2016). Determinants of meme popularity. Physical Review X, 6, 021019.

    Article  Google Scholar 

  • Glnzel, W., Schlemmer, B., & Thijs, B. (2005). Better late than never? On the chance to become highly cited only beyond the standard bibliometric time horizon. Scientometrics, 58(3), 571–586.

    Article  Google Scholar 

  • Graus, D., Odijk, D., & de Rijke, M. (2017). The birth of collective memories: Analyzing emerging entities in text streams. arXiv preprint arXiv:1701.04039.

  • Ke, Q., Ferrara, E., Radicchi, F., & Flammini, A. (2015). Defining and identifying sleeping beauties in science. Proceedings of the National Academy of Sciences, 112(24), 7426–7431.

    Article  Google Scholar 

  • Kristoufek, L. (2013). Bitcoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the internet era. Scientific Reports, 3, 3415.

    Article  Google Scholar 

  • Lachance, C., & Larivire, V. (2014). On the citation lifecycle of papers with delayed recognition. Journal of Informetrics, 8(4), 863–872.

    Article  Google Scholar 

  • Lansdall-Welfare, T., Sudhahar, S., Thompson, J., Lewis, J., Team, F. N., & Cristianini, N. (2017). Content analysis of 150 years of british periodicals. Proceedings of the National Academy of Sciences, 114(4), E457–E465. doi:10.1073/pnas.1606380114.

    Article  Google Scholar 

  • LaRowe, G., Ambre, S., Burgoon, J., Ke, W., & Brner, K. (2008). The scholarly database and its utility for scientometrics research. Scientometrics, 79(2), 219–234.

    Article  Google Scholar 

  • Leskovec, J., Backstrom, L., & Kleinberg, J. (2009). Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09 (pp. 497–506). New York: ACM.

  • Li, J. (2013). Citation curves of “all-elements-sleeping-beauties”: “flash in the pan” first and then “delayed recognition”. Scientometrics, 100(2), 595–601.

    Article  Google Scholar 

  • Li, J., & Ye, F. Y. (2012). The phenomenon of all-elements-sleeping-beauties in scientific literature. Scientometrics, 92(3), 795–799.

    Article  Google Scholar 

  • Li, J., Shi, D., Zhao, S. X., & Ye, F. Y. (2014). A study of the heartbeat spectra for sleeping beauties. Journal of Informetrics, 8(3), 493–502.

    Article  Google Scholar 

  • Light, R. P., Polley, David Eand, & Börner, K. (2014). Open data and open code for big science of science studies. Scientometrics, 101(2), 1535–1551.

    Article  Google Scholar 

  • Marx, W. (2014). The Shockley–Queisser paper a notable example of a scientific sleeping beauty. Annalen der Physik, 526(5–6), A41–A45.

    Article  Google Scholar 

  • Norton, J. A., & Bass, F. M. (1987). A diffusion theory model of adoption and substitution for successive generations of high-technology products. Management Science, 33(9), 1069–1086.

    Article  Google Scholar 

  • Ohba, N., & Nakao, K. (2012). Sleeping beauties in ophthalmology. Scientometrics, 93(2), 253–264.

    Article  Google Scholar 

  • Palshikar, G. (2009). Simple algorithms for peak detection in time-series. In Proceedings of 1st International Conference Advanced Data Analysis, Business Analytics and Intelligence (pp. 1–13).

  • Redner, S. (2005). Citation statistics from 110 Years of Physical Review. Physics Today, 58(6), 49–54.

    Article  Google Scholar 

  • Sanl, C., & Lambiotte, R. (2015). Local variation of hashtag spike trains and popularity in twitter. PLoS ONE, 10(7), e0131704.

    Article  Google Scholar 

  • Sasahara, K., Hirata, Y., Toyoda, M., Kitsuregawa, M., & Aihara, K. (2013). Quantifying collective attention from tweet stream. PLoS ONE, 8(4), 1–10.

    Article  Google Scholar 

  • Senz-Royo, C., Gracia-Lzaro, C., & Moreno, Y. (2015). The role of the organization structure in the diffusion of innovations. PLoS ONE, 10(5), 1–13.

    Google Scholar 

  • Shifman, L. (2012). An anatomy of a youtube meme. New Media & Society, 14(2), 187–203.

    Article  Google Scholar 

  • Spitzberg, B. H. (2014). Toward a model of meme diffusion (M3D). Communication Theory, 24(3), 311–339.

    Article  Google Scholar 

  • Van Raan, A. F. (2004). Sleeping beauties in science. Scientometrics, 59(3), 461–466.

    Google Scholar 

  • van Raan, A. F. J. (2015). Dormitory of physical and engineering sciences: Sleeping beauties may be sleeping innovations. PLoS ONE, 10(10), e0139786.

    Article  Google Scholar 

  • Wang, D., Song, C., & Barabsi, A. L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127–132.

    Article  Google Scholar 

  • Wang, S., Yan, Z., Hu, X., Yu, P. S., & Li, Z. (2015). Burst time prediction in cascades. In Proceedings of the Twenty-Ninth Conference on Artificial Intelligence, AAAI (pp. 325–331).

  • Weng, L., Flammini, A., Vespignani, A., & Menczer, F. (2012). Competition among memes in a world with limited attention. Scientific Reports, 2, 335.

    Article  Google Scholar 

  • Weng, L., Menczer, F., & Ahn, Y. Y. (2013). Virality prediction and community structure in social networks. Scientific Reports, 3, 2522.

    Article  Google Scholar 

  • Weng, L., Menczer, F., & Ahn, Y. Y. (2014). Predicting successful memes using network and community structure. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, Ann Arbor, MI, ICWSM-14 (pp. 535–544).

  • Yoshida, M., Arase, Y., Tsunoda, T., & Yamamoto, M. (2015). Wikipedia page view reflects web search trend. In Proceedings of the Web Science Conference (pp. 1–2).

  • Yu, L., Cui, P., Wang, F., Song, C., & Yang, S. (2017). Uncovering and predicting the dynamic process of information cascades with survival model. Knowledge and information systems, 50(2), 633–659.

    Article  Google Scholar 

  • Zhang, L., Zhao, J., & Xu, K. (2016). Who creates trends in online social media: The crowd or opinion leaders? Journal of Computer-Mediated Communication, 21(1), 1–16.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 71501005, 71531001, and 61421003) and the fund of the State Key Lab of Software Development Environment (Grant Nos. SKLSDE-2017ZX-05 and SKLSDE-2015ZX-28). ZJC thanks the support from the research foundation of graduate education and development in Beihang Unversity. We also thank Ms. Xiaoqian Hu for her valuable suggestions.

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Correspondence to Jichang Zhao.

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Zhang, L., Xu, K. & Zhao, J. Sleeping beauties in meme diffusion. Scientometrics 112, 383–402 (2017). https://doi.org/10.1007/s11192-017-2390-2

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