Skip to main content
SpringerLink
Log in

Multiple bursts of highly retweeted articles on social media

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

In this study, we perform a diachronic analysis of the temporal trends of scholarly articles over a fairly long time scale. Through the retweeting dynamics of 298 highly retweeted articles, we find that nearly 57% of the articles have multiple peaks on diffusion curves after their initial exposure on social media. We characterize this phenomenon by measuring the time interval between peaks, the height and width of peaks, diffusion performance and the similarities between peaks. We discover that bursts of multi-peak articles usually have short durations and small coverage and that the length of the peak interval determines correlations between peaks. We also find that overlapping users between peaks can act like weak ties and spread information from one group to another, catalyzing information to experience a renewed burst of retweets. Based on the presence or absence of overlapping users, we summarize the occurrence mechanism of the multi-peak phenomenon into two types: (1) Cross-community diffusion through overlapping users. (2) Bursty diffusion of key opinion leaders at different times.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. https://www.nltk.org/.

  2. Science communicator: someone who links frequently to scientific articles from a variety of different journals or publishers.

References

  • Adelman, M. B., Parks, M. R., & Albrecht, T. L. (1987). Beyond close relationships: Support in weak ties. In T. L. Albrecht, & M. B. Adelman, Associates (Eds.), Communicating social support (pp. 126–147). Newbury Park, CA: Sage.

  • Bakshy, E., Karrer, B., & Adamic, L. A. (2009). Social influence and the diffusion of user-created content. In: Proceedings of the 10th ACM conference on Electronic commerce. New York, NY, USA: Association for Computing Machinery, 325–334. doi: https://doi.org/10.1145/1566374.1566421.

  • Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. In: Proceedings of the 21st international conference on World Wide Web, New York, NY, USA: Association for Computing Machinery, 519–528. doi: https://doi.org/10.1145/2187836.2187907.

  • Barabási, A.-L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435(7039), 207–211. https://doi.org/10.1038/nature03459.

    Article  Google Scholar 

  • Barabási, A.-L., & Gelman, A. (2010). Bursts: The hidden pattern behind everything we do. Physics Today, 63, 46. https://doi.org/10.1063/1.3431332.

    Article  Google Scholar 

  • Bauer, M. W. (2012). Public attention to science 1820–2010—A Longue Durée picture. In S. Rödder, M. Franzen, & P. Weingart (Eds.), The sciences’ media connection-public communication and its repercussions. (pp. 35–57). Springer.

    Chapter  Google Scholar 

  • Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205. https://doi.org/10.1509/jmr.10.0353.

    Article  Google Scholar 

  • Brase, C. H., & Brase, C. P. (2013). Understanding basic statistics. Cengage Learning.

    Google Scholar 

  • Cha, M., Mislove, A., & Gummadi, K. P. (2009). A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the 18th international conference on World wide web. New York, NY, USA: Association for Computing Machinery, 721–730. doi: https://doi.org/10.1145/1526709.1526806.

  • Chen, J., Hsu, W., Lee, M. L., & Ng, S. (2007). Labeling network motifs in protein interactomes for protein function prediction. In: 2007 IEEE 23rd International Conference on Data Engineering. Presented at the 2007 IEEE 23rd International Conference on Data Engineering, 546–555. doi: https://doi.org/10.1109/icde.2007.367900.

  • Cheng, J., Adamic, L. A., Kleinberg, J. M., & Leskovec, J. (2016). Do cascades recur? In: Proceedings of the 25th International Conference on World Wide Web, 671–681. doi: https://doi.org/10.1145/2872427.2882993.

  • Cohen, W. M., & Levinthal, D. A. (2000). Chapter 3—absorptive capacity: a new perspective on learning and innovation* *reprinted with permission © 1990 by Cornell University. In R. L. Cross & S. B. Israelit (Eds.), Strategic Learning in a knowledge economy. (pp. 39–67). Butterworth-Heinemann.

    Chapter  Google Scholar 

  • Crane, R., & Sornette, D. (2008). Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences, 105(41), 15649–15653. https://doi.org/10.1073/pnas.0803685105.

    Article  Google Scholar 

  • Fan, R., Xu, K., & Zhao, J. (2020). Weak ties strengthen anger contagion in social media. arXiv:2005.01924 [cs]. http://arxiv.org/abs/2005.01924. Accessed from 18 Feb 2021.

  • Fedoroff, N. V. (2012). The global knowledge society. Science, 335(6068), 503–503. https://doi.org/10.1126/science.1219498.

    Article  Google Scholar 

  • Garrett, R. K. (2009). Echo chambers online?: Politically motivated selective exposure among Internet news users1. Journal of Computer-Mediated Communication, 14(2), 265–285. https://doi.org/10.1111/j.1083-6101.2009.01440.x.

    Article  MathSciNet  Google Scholar 

  • Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2015). The structural virality of online diffusion. Management Science, 62(1), 180–196. https://doi.org/10.1287/mnsc.2015.2158.

    Article  Google Scholar 

  • Goel, S., Watts, D. J., & Goldstein, D. G. (2012). The structure of online diffusion networks. In: Proceedings of the 13th ACM Conference on Electronic Commerce. New York, NY, USA: Association for Computing Machinery, 623–638. doi: https://doi.org/10.1145/2229012.2229058.

  • Goh, K.-I., & Barabási, A.-L. (2008). Burstiness and memory in complex systems. EPL (Europhysics Letters), 81(4), 48002. https://doi.org/10.1209/0295-5075/81/48002.

    Article  MathSciNet  Google Scholar 

  • Grabowicz, P. A., Ramasco, J. J., Moro, E., Pujol, J. M., & Eguiluz, V. M. (2012). Social features of online networks: the strength of intermediary ties in online social media. PLoS ONE. https://doi.org/10.1371/journal.pone.0029358.

    Article  Google Scholar 

  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086/225469.

    Article  Google Scholar 

  • Hargittai, E., Füchslin, T., & Schäfer, M. S. (2018). How do young adults engage with science and research on social media? Some preliminary findings and an agenda for future research. Social MediaSociety, 4(3), 2056305118797720. https://doi.org/10.1177/2056305118797720.

    Article  Google Scholar 

  • Herie, M., & Martin, G. W. (2002). Knowledge diffusion in social work: a new approach to bridging the gap. Social Work, 47(1), 85–95. https://doi.org/10.1093/sw/47.1.85.

    Article  Google Scholar 

  • Larson, J. M. (2017). The weakness of weak ties for novel information diffusion. Applied Network Science. https://doi.org/10.1007/s41109-017-0034-3.

    Article  Google Scholar 

  • Lerman, K., & Ghosh, R. (2010). Information contagion: An empirical study of the spread of news on digg and twitter social Networks. In: Proceedings of the International AAAI Conference on Web and Social Media, 4(1). https://ojs.aaai.org/index.php/ICWSM/article/view/14021. Accessed from 14 Feb 2021.

  • 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 (pp. 497–506). New York, NY: Association for Computing Machinery. https://doi.org/10.1145/1557019.1557077

  • Liu, J.-G., Zhou, Q., Guo, Q., Yang, Z.-H., Xie, F., & Han, J.-T. (2017). Knowledge diffusion of dynamical network in terms of interaction frequency. Scientific Reports, 7(1), 10755. https://doi.org/10.1038/s41598-017-11057-8.

    Article  Google Scholar 

  • Liu, S.-Y., Xiao, J., & Xu, X.-K. (2020). Link prediction in signed social networks: From status theory to motif families. IEEE Transactions on Network Science and Engineering, 7(3), 1724–1735. https://doi.org/10.1109/tnse.2019.2951806.

    Article  Google Scholar 

  • Matsubara, Y., Sakurai, Y., Prakash, B. A., Li, L., & Faloutsos, C. (2012). Rise and fall patterns of information diffusion: model and implications. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: Association for Computing Machinery 6–14. doi: https://doi.org/10.1145/2339530.2339537.

  • Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298(5594), 824–827. https://doi.org/10.1126/science.298.5594.824.

    Article  Google Scholar 

  • Myers, S. A., & Leskovec, J. (2014). The bursty dynamics of the Twitter information network. In: Proceedings of the 23rd international conference on World wide web. New York, NY, USA: Association for Computing Machinery 913–924. doi: https://doi.org/10.1145/2566486.2568043.

  • Onnela, J. P., Saramki, J., Hyvnen, J., Szabó, G., & Barabási, A. L. (2007). Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Ences, 104(18), 7332–7336. https://doi.org/10.1073/pnas.0610245104.

    Article  Google Scholar 

  • Priem, J., & Hemminger, B. H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social Web. First monday. doi: 1592766041.

  • Priem, J., Taraborelli, D., Groth, P., & Neylon, C. (2010). Altmetrics: A manifesto. Retrieved from http://altmetrics.org/manifesto/. Accessed 6 Apr 2021.

  • Wang, S., & Noe, R. A. (2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20(2), 115–131. https://doi.org/10.1016/j.hrmr.2009.10.001.

    Article  Google Scholar 

  • Wang, X., Chen, L., Shi, J., & Peng, T.-Q. (2019). What makes cancer information viral on social media? Computers in Human Behavior, 93, 149–156. https://doi.org/10.1016/j.chb.2018.12.024.

    Article  Google Scholar 

  • Weng, L., Karsai, M., Perra, N., Menczer, F., & Flammini, A. (2018). Attention on weak ties in social and communication networks. In S. Lehmann & Y.-Y. Ahn (Eds.), Complex spreading phenomena in social Systems: Influence and contagion in real-world social networks. (pp. 213–228). Springer International Publishing.

    Chapter  Google Scholar 

  • Weng, L., Menczer, F., & Ahn, Y.-Y. (2013). Virality prediction and community structure in social networks. Scientific Reports, 3, 2522. https://doi.org/10.1038/srep02522.

    Article  Google Scholar 

  • Yang, J., & Leskovec, J. (2011). Patterns of temporal variation in online media. In: Proceedings of the fourth ACM international conference on Web search and data mining. New York, NY, USA: Association for Computing Machinery 177–186. doi: https://doi.org/10.1145/1935826.1935863.

  • Yin, Y., Gao, J., Jones, B. F., & Wang, D. (2021). Coevolution of policy and science during the pandemic. Science, 371(6525), 128–130. https://doi.org/10.1126/science.abe3084.

    Article  Google Scholar 

  • Zafarani, R., Abbasi, M. A., & Liu, H. (2014). Social media mining: An introduction. . Cambridge University Press.

    Book  Google Scholar 

  • Zakhlebin, I., & Horvát, E. -Á. (2020). Diffusion of scientific articles across online platforms. Proceedings of the International AAAI Conference on Web and Social Media, 14, 762–773.

    Google Scholar 

  • Zhan, C., Wu, F., Huang, Z., Jiang, W., & Zhang, Q. (2020). Analysis of collective action propagation with multiple recurrences. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04756-3.

    Article  Google Scholar 

  • Zhao, J., Wu, J., & Xu, K. (2010). Weak ties: Subtle role of information diffusion in online social networks. Physical Review E, 82(1), 016105. https://doi.org/10.1103/PhysRevE.82.016105.

    Article  Google Scholar 

  • Zhou, K. Z., & Li, C. B. (2012). How knowledge affects radical innovation: Knowledge base, market knowledge acquisition, and internal knowledge sharing. Strategic Management Journal, 33(9), 1090–1102. https://doi.org/10.1002/smj.1959.

    Article  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the Grant from the National Natural Science Foundation of China (71673038, 61773091, 71974029), thank the anonymous reviewer for helpful comments, and thank Altmetric.com for providing the altmetric data of scientific publications.

Author information

Authors and Affiliations

  1. WISE Lab, Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian, 116085, China

    Renmeng Cao & Xianwen Wang

  2. College of Information and Communication Engineering, Dalian Minzu University, Dalian, 116600, China

    Xiaoke Xu

  3. School of Economics and Management, China University of Geosciences (Beijing), Beijing, 100083, China

    Jianlin Zhou

Authors
  1. Renmeng Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianwen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoke Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianlin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xianwen Wang or Xiaoke Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, R., Wang, X., Xu, X. et al. Multiple bursts of highly retweeted articles on social media. Scientometrics 126, 5165–5179 (2021). https://doi.org/10.1007/s11192-021-03970-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-021-03970-7

Keywords

Search

Navigation