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Profiling Bot Accounts Mentioning COVID-19 Publications on Twitter

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

Abstract

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.

Keywords

Twitter Bot Network analysis Altmetrics research 

References

  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).  https://doi.org/10.1002/asi.23833CrossRefGoogle 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).  https://doi.org/10.1093/scipol/scy024CrossRefGoogle Scholar
  3. 3.
    Van Noorden, R.: Online collaboration: scientists and the social network. Nature 512, 126–129 (2014).  https://doi.org/10.1038/512126aCrossRefGoogle 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).  https://doi.org/10.1007/s11192-017-2512-xCrossRefGoogle 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).  https://doi.org/10.4033/iee.2013.6.6.f
  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).  https://doi.org/10.1371/journal.pone.0183551CrossRefGoogle 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).  https://doi.org/10.1093/femsle/fnz075
  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). https://arxiv.org/abs/1806.02201
  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).  https://doi.org/10.1002/asi.23456
  10. 10.
    Yu, H.: Context of altmetrics data matters: an investigation of count type and user category. Scientometrics 111, 267–283 (2017).  https://doi.org/10.1007/s11192-017-2251-zCrossRefGoogle 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). https://www.altmetric.com/blog/not-sure-if-scientist-or-just-twitter-bot-or-who-tweets-about-scholarly-papers/
  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).  https://doi.org/10.1007/s00500-020-04689-yCrossRefGoogle 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).  https://doi.org/10.1162/qss_a_00066
  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).  https://doi.org/10.1109/TDSC.2012.75CrossRefGoogle 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).  https://doi.org/10.1109/UBMK.2017.8093483
  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).  https://doi.org/10.1007/978-3-319-47880-7_6CrossRefGoogle Scholar
  17. 17.
    Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. 467, 312–322 (2018).  https://doi.org/10.1016/j.ins.2018.08.019CrossRefGoogle 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).  https://doi.org/10.1145/3110025.3110091
  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).  https://doi.org/10.1145/2766462.2767701

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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