Profiling Bot Accounts Mentioning COVID-19 Publications on Twitter

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12504)


This paper presents preliminary findings regarding automated bots mentioning scientific papers about COVID-19 publications on Twitter. A quantitative approach was adopted to characterize social and posting patterns of bots, in contrast to other users, in Twitter scholarly communication. Our findings indicate that bots play a prominent role in research dissemination and discussion on the social web. We observed 0.45% explicit bots in our sample, producing 2.9% of tweets. The results implicate that bots tweeted differently from non-bot accounts in terms of the volume and frequency of tweeting, the way handling the content of tweets, as well as preferences in article selection. In the meanwhile, their behavioral patterns may not be the same as Twitter bots in another context. This study contributes to the literature by enriching the understanding of automated accounts in the process of scholarly communication and demonstrating the potentials of bot-related studies in altmetrics research.


Twitter Bot Network analysis Altmetrics research 


  1. 1.
    Sugimoto, C.R., Work, S., Larivière, V., Haustein, S.: Scholarly use of social media and altmetrics: a review of the literature. J. Assoc. Inf. Sci. Technol. 68, 2037–2062 (2017). Scholar
  2. 2.
    Robinson-Garcia, N., van Leeuwen, T.N., Rafols, I.: Using altmetrics for contextualised mapping of societal impact: from hits to networks. Sci. Public Policy 45, 815–826 (2018). Scholar
  3. 3.
    Van Noorden, R.: Online collaboration: scientists and the social network. Nature 512, 126–129 (2014). Scholar
  4. 4.
    Hassan, S.-U., Imran, M., Gillani, U., Aljohani, N.R., Bowman, T.D., Didegah, F.: Measuring social media activity of scientific literature: an exhaustive comparison of scopus and novel altmetrics big data. Scientometrics 113(2), 1037–1057 (2017). Scholar
  5. 5.
    Darling, E., Shiffman, D., Côté, I., Drew, J.: The role of Twitter in the life cycle of a scientific publication. Ideas Ecol. Evol. 6 (2013).
  6. 6.
    Robinson-Garcia, N., Costas, R., Isett, K., Melkers, J., Hicks, D.: The unbearable emptiness of tweeting—about journal articles. PLoS ONE 12, e0183551 (2017). Scholar
  7. 7.
    Robinson-Garcia, N., Arroyo-Machado, W., Torres-Salinas, D.: Mapping social media attention in Microbiology: identifying main topics and actors. FEMS Microbiol. Lett. 366 (2019).
  8. 8.
    Haustein, S.: Scholarly Twitter metrics. In: Glänzel, W., Moed, H.F., Schmoch, U., Thelwall, M. (eds.) Handbook of Quantitative Science and Technology Research (2018).
  9. 9.
    Haustein, S., Bowman, T.D., Holmberg, K., Tsou, A., Sugimoto, C.R., Larivière, V.: Tweets as impact indicators: examining the implications of automated “bot” accounts on Twitter. J. Assoc. Inf. Sci. Technol. (2016).
  10. 10.
    Yu, H.: Context of altmetrics data matters: an investigation of count type and user category. Scientometrics 111, 267–283 (2017). Scholar
  11. 11.
    Haustein, S., Toupin, R., Alperin, J.P.: “Not sure if scientist or just Twitter bot” Or: who tweets about scholarly papers (2018).
  12. 12.
    Aljohani, N.R., Fayoumi, A., Hassan, S.-U.: Bot prediction on social networks of Twitter in altmetrics using deep graph convolutional networks. Soft. Comput. 24(15), 11109–11120 (2020). Scholar
  13. 13.
    Kousha, K., Thelwall, M.: COVID-19 publications: database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts. Quant. Sci. Stud. 1–24 (2020).
  14. 14.
    Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Trans. Dependable Secur. Comput. 9, 811–824 (2012). Scholar
  15. 15.
    Kantepe, M., Ganiz, M.C.: Preprocessing framework for Twitter bot detection. In: 2017 International Conference on Computer Science and Engineering (UBMK), pp. 630–634. IEEE (2017).
  16. 16.
    Oentaryo, R.J., Murdopo, A., Prasetyo, P.K., Lim, E.-P.: On profiling bots in social media. In: Spiro, E., Ahn, Y.-Y. (eds.) SocInfo 2016. LNCS, vol. 10046, pp. 92–109. Springer, Cham (2016). Scholar
  17. 17.
    Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. 467, 312–322 (2018). Scholar
  18. 18.
    Gilani, Z., Kochmar, E., Crowcroft, J.: Classification of Twitter accounts into automated agents and human users. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 489-496 (2017).
  19. 19.
    Sedhai, S., Sun, A.: HSpam14: a collection of 14 million tweets for hashtag-oriented spam research. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 223–232 (2015).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Wee Kim Wee School of Communication and InformationNanyang Technological UniversitySingaporeSingapore

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