Skip to main content Skip to main navigation menu Skip to site footer
Articles
DOI: 10.14254/1795-6889.2025.21-2.8
Submitted: 2025-02-27
Published: 2025-10-12

What’s in a bot? Tracing epistemic differences across expert discourses

DIGSUM, Department of Sociology, Umeå University, Sweden
DIGSUM, Department of Sociology, Umeå University, Sweden
Social bots Discourse Expert communities Text analysis AI

Abstract

Automated social agents-bots-are increasingly central to digital environments, yet definitions of what constitutes a bot vary across expert communities. This article analyses how bots are conceptualised in academic and technological discourse by examining scholarly publications (Scopus) and developer discussions (Stack Overflow). Using computational methods, including keyword-in-context analysis and topic modelling, we trace epistemic differences in bot definitions across disciplines. Findings reveal structural discursive silos, with technical fields emphasising functional properties and social sciences focusing on sociotechnical entanglements. These definitional divergences have implications for research, regulation, and governance in an era of AI-driven automation.

Metrics

Metrics Loading ...

References

  1. Aljabri, M., Zagrouba, R., Shaahid, A., & Alqarni, A. (2023). Machine learning-based social media bot detection: A comprehensive literature review. Social Network Analysis and Mining, 13, 20. https://doi.org/10.1007/s13278-022-01020-5 DOI: https://doi.org/10.1007/s13278-022-01020-5
  2. Barua, A., Thomas, S. W., & Hassan, A. E. (2014). What are developers talking about? An analysis of topics and trends in Stack Overflow. Empirical Software Engineering, 19, 619–654. DOI: https://doi.org/10.1007/s10664-012-9231-y
  3. Bessi, A., & Ferrara, E. (2016). Social bots distort the 2016 US Presidential election online discussion. First Monday, 21(11–7). DOI: https://doi.org/10.5210/fm.v21i11.7090
  4. Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python. O’Reilly.
  5. Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2017). Like trainer, like bot? Inheritance of bias in algorithmic content moderation. Lecture Notes in Computer Science, 10540 LNCS, 405–415. https://doi.org/10.1007/978-3-319-67256-4_32 DOI: https://doi.org/10.1007/978-3-319-67256-4_32
  6. Boichak, O., Jackson, S., Hemsley, J., & Tanupabrungsun, S. (2018). Automated diffusion? Bots and their influence during the 2016 U.S. Presidential election. Lecture Notes in Computer Science, 10766 LNCS, 26. https://doi.org/10.1007/978-3-319-78105-1_3 DOI: https://doi.org/10.1007/978-3-319-78105-1_3
  7. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
  8. Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2011). The socialbot network: When bots socialize for fame and money. Proceedings of the 27th Annual Computer Security Applications Conference (ACSAC 11), 93–102. https://doi.org/10.1145/2076732.2076746 DOI: https://doi.org/10.1145/2076732.2076746
  9. Brachten, F., Stieglitz, S., Hofeditz, L., Kloppenborg, K., & Reimann, A. (2017). Strategies and influence of social bots in a 2017 German state election—A case study on Twitter. Proceedings of the 28th Australasian Conference on Information Systems (ACIS 2017).
  10. Bucher, T. (2014). About a bot: Hoax, fake, performance art. M/C Journal, 17(3). https://doi.org/10.5204/mcj.814 DOI: https://doi.org/10.5204/mcj.814
  11. Bulat, B., & Hilbert, M. (2024). A dynamical systems approach to bots and online political communication. arXiv. http://arxiv.org/abs/2405.18652
  12. Cantini, R., Marozzo, F., Talia, D., & Trunfio, P. (2022). Analyzing political polarization on social media by deleting bot spamming. Big Data and Cognitive Computing, 6(1). https://doi.org/10.3390/bdcc6010003 DOI: https://doi.org/10.3390/bdcc6010003
  13. Chung, S. K. (2017). Computer-assisted language learning: Collocation analysis and learning in corpora. The International Journal of Literacies, 24(2), 33. DOI: https://doi.org/10.18848/2327-0136/CGP/v24i02/33-44
  14. Coeckelbergh, M. (2022). Robot ethics. MIT Press. DOI: https://doi.org/10.7551/mitpress/14436.001.0001
  15. De Morais, D. M. G., & Digiampietri, L. A. (2021). Methods and challenges in social bots detection: A systematic review. ACM International Conference Proceeding Series. https://doi.org/10.1145/3466933.3466973 DOI: https://doi.org/10.1145/3466933.3466973
  16. Dialektakis, G., Dimitriadis, I., & Vakali, A. (2022). CALEB: A conditional adversarial learning framework to enhance bot detection. arXiv. https://doi.org/10.48550/arXiv.2205.15707 DOI: https://doi.org/10.2139/ssrn.4344054
  17. Doshi, J., Patel, M., Mukherjee, S., & Banerjee, S. (2024). Sleeper social bots: A new generation of AI disinformation bots are already a political threat. arXiv. http://arxiv.org/abs/2408.12603
  18. Earl, J., & Kimport, K. (2011). Digitally enabled social change: Activism in the Internet age. MIT Press. DOI: https://doi.org/10.7551/mitpress/9780262015103.001.0001
  19. Fernquist, J., Kaati, L., & Schroeder, R. (2018). Political bots and the Swedish general election. Proceedings of the IEEE International Symposium on Information Systems (ISI 2018), 124–129. https://doi.org/10.1109/ISI.2018.8587347 DOI: https://doi.org/10.1109/ISI.2018.8587347
  20. Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96–104. https://doi.org/10.1145/2818717 DOI: https://doi.org/10.1145/2818717
  21. Freitas, C., Benevenuto, F., Veloso, A., Prates, R., Kwak, H., & An, J. (2016). An empirical study of socialbot infiltration strategies in the Twitter social network. Social Network Analysis and Mining, 6, 23. https://doi.org/10.1007/s13278-016-0331-3 DOI: https://doi.org/10.1007/s13278-016-0331-3
  22. Fernandes, S., & Bernardino, J. (2015). What is BigQuery? Proceedings of the ACM International Conference on Cloud Computing and Big Data (CloudCom 2015), 202–203. https://doi.org/10.1145/2790755.2790797 DOI: https://doi.org/10.1145/2790755.2790797
  23. Gorwa, R., & Guilbeault, D. (2020). Unpacking the social media bot: A typology to guide research and policy. Policy & Internet, 12(2), 225–248. https://doi.org/10.1002/poi3.184 DOI: https://doi.org/10.1002/poi3.184
  24. Guglielmi, G. (2020). The next-generation bots interfering with the US election. Nature, 587(7832), 21. https://doi.org/10.1038/41586-020-03034-5 DOI: https://doi.org/10.1038/d41586-020-03034-5
  25. Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H. A., Epstein, D. H., Leggio, L., & Curtis, B. (2021). Bots and misinformation spread on social media: Implications for COVID-19. Journal of Medical Internet Research, 23(5). https://doi.org/10.2196/26933 DOI: https://doi.org/10.2196/26933
  26. Howard, P. N., Kollanyi, B., & Woolley, S. (2016). Bots and automation over Twitter during the US election. Computational Propaganda Project: Working Paper Series.
  27. Howard, P. N., Woolley, S., & Calo, R. (2018). Algorithms, bots, and political communication in the US 2016 election: The challenge of automated political communication for election law and administration. Journal of Information Technology & Politics, 15(2), 81–93. https://doi.org/10.1080/19331681.2018.1448735 DOI: https://doi.org/10.1080/19331681.2018.1448735
  28. Hui, P.-M., Yang, K.-C., Torres-Lugo, C., Monroe, Z., McCarty, M., Serrette, B., Pentchev, V., & Menczer, F. (2019). Botslayer: Real-time detection of bot amplification on Twitter. Journal of Open Source Software, 4(42), 1706. https://doi.org/10.21105/joss.01706 DOI: https://doi.org/10.21105/joss.01706
  29. Kearney, M. W. (2018). Tweetbotornot: R package for detecting Twitter bots via machine learning [Software package]. https://github.com/mkearney/Tweetbotornot
  30. Keller, T. R., & Klinger, U. (2019). Social bots in election campaigns: Theoretical, empirical, and methodological implications. Political Communication, 36(1), 171–189. https://doi.org/10.1080/10584609.2018.1526238 DOI: https://doi.org/10.1080/10584609.2018.1526238
  31. Laclau, E., & Mouffe, C. (1985). Hegemony and socialist strategy: Towards a radical democratic politics. Verso.
  32. Lanius, C., Weber, R., & MacKenzie, W. I. Jr. (2021). Use of bot and content flags to limit the spread of misinformation among social networks: A behavior and attitude survey. Social Network Analysis and Mining, 11(1). https://doi.org/10.1007/s13278-021-00739-x DOI: https://doi.org/10.1007/s13278-021-00739-x
  33. Lindgren, S. (2022). Digital media and society (2nd ed.). SAGE Publications.
  34. Lindgren, S. (2023). Critical theory of AI. Polity.
  35. Martini, F., Samula, P., Keller, T. R., & Klinger, U. (2021). Bot, or not? Comparing three methods for detecting social bots in five political discourses. Big Data & Society, 8(2), 20539517211033566. https://doi.org/10.1177/20539517211033566 DOI: https://doi.org/10.1177/20539517211033566
  36. Mabey, B. (2018). pyLDAvis documentation. https://pyldavis.readthedocs.io
  37. Monaco, N., & Woolley, S. (2022). Bots. John Wiley & Sons.
  38. Olanipekun, S. (2025). Computational propaganda and misinformation: AI technologies as tools of media manipulation. World Journal of Advanced Research and Reviews, 25, 911–923. https://doi.org/10.30574/wjarr.2025.25.1.0131 DOI: https://doi.org/10.30574/wjarr.2025.25.1.0131
  39. Pinch, T. J., & Bijker, W. E. (1984). The social construction of facts and artefacts: Or how the sociology of science and the sociology of technology might benefit each other. Social Studies of Science, 14(3), 399–441. DOI: https://doi.org/10.1177/030631284014003004
  40. Rauchfleisch, A., & Kaiser, J. (2020). The false positive problem of automatic bot detection in social science research. DOI: https://doi.org/10.2139/ssrn.3565233
  41. Ravazzi, C., Malandrino, F., & Dabbene, F. (2022). Towards proactive moderation of malicious content via bot detection in fringe social networks. IEEE Control Systems Letters, 6, 2960–2965. https://doi.org/10.1109/LCSYS.2022.3182291 DOI: https://doi.org/10.1109/LCSYS.2022.3182291
  42. Řehůřek, R., & Sojka, P. (2011). Gensim—Statistical semantics in Python. NLP Centre, Faculty of Informatics.
  43. Rizoiu, M.-A., Lee, Y., Mishra, S., Kong, Q., Carman, M., & Xie, L. (2018). #DebateNight: The role and influence of socialbots on Twitter during the first 2016 U.S. presidential debate. arXiv. https://doi.org/10.48550/arXiv.1802.09808 DOI: https://doi.org/10.1609/icwsm.v12i1.15029
  44. Roberge, J., & Castelle, M. (2021). Toward an end-to-end sociology of 21st-century machine learning. In The cultural life of machine learning: An incursion into critical AI studies (pp. 1–29). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-56286-1_1
  45. Schuchard, R. J., & Crooks, A. T. (2021). Insights into elections: An ensemble bot detection coverage framework applied to the 2018 U.S. midterm elections. PLOS ONE, 16(1), e0244309. https://doi.org/10.1371/journal.pone.0244309 DOI: https://doi.org/10.1371/journal.pone.0244309
  46. Schuchard, R., Crooks, A., Stefanidis, A., & Croitoru, A. (2019). Bots fired: Examining social bot evidence in online mass shooting conversations. Palgrave Communications, 5(1). https://doi.org/10.1057/s41599-019-0359-x DOI: https://doi.org/10.1057/s41599-019-0359-x
  47. Schweitzer, S., Dobson, K. S. H., & Waytz, A. (2024). Political bot bias in the perception of online discourse. Social Psychological and Personality Science, 15(2), 234–244. https://doi.org/10.1177/19485506231156020 DOI: https://doi.org/10.1177/19485506231156020
  48. Seering, J., Flores, J. P., Savage, S., & Hammer, J. (2018). The social roles of bots: Situating bots in discussions in online communities. Proceedings of the ACM on Human-Computer Interaction, 2. https://doi.org/10.1145/3274426 DOI: https://doi.org/10.1145/3274426
  49. Sinclair, J. (1991). Corpus, concordance, collocation. Oxford University Press.
  50. Subrahmanian, V. S., Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Zhu, L., Ferrara, E., Flammini, A., & Menczer, F. (2016). The DARPA Twitter bot challenge. Computer, 49(6), 38–46. DOI: https://doi.org/10.1109/MC.2016.183
  51. Thompson, K. (1968). Programming techniques: Regular expression search algorithm. Communications of the ACM, 11(6), 419–422. DOI: https://doi.org/10.1145/363347.363387
  52. Torusdağ, M. B., Kutlu, M., & Selçuk, A. A. (2020). Are we secure from bots? Investigating vulnerabilities of Botometer. In 2020 5th International Conference on Computer Science and Engineering (UBMK) (pp. 343–348). IEEE. DOI: https://doi.org/10.1109/UBMK50275.2020.9219433
  53. Torusdağ, M. B., Kutlu, M., & Selçuk, A. A. (2022). Evaluation of social bot detection models. Turkish Journal of Electrical Engineering and Computer Sciences, 30(4), 1269–1283. DOI: https://doi.org/10.55730/1300-0632.3848
  54. VikParuchuri. (2025). pdftext: Extract structured text from PDFs quickly [Computer software]. https://github.com/VikParuchuri/pdftext
  55. Weber, M. (1978). Economy and society: An outline of interpretive sociology. University of California Press.
  56. Williams, R., & Edge, D. (1996). The social shaping of technology. Research Policy, 25(6), 865–899. DOI: https://doi.org/10.1016/0048-7333(96)00885-2
  57. Wrangel, C. (2007). Towards a Modified Discourse Theory pt. 1: Laclau’s ‘Empty Signifier’. That’s Not It (Blog).
  58. Yang, K.-C., Ferrara, E., & Menczer, F. (2022). Botometer 101: Social bot practicum for computational social scientists. Journal of Computational Social Science, 5(2), 1511–1528. DOI: https://doi.org/10.1007/s42001-022-00177-5
  59. Yang, K.-C., Varol, O., Davis, C. A., Ferrara, E., Flammini, A., & Menczer, F. (2020). Scalable and generalizable social bot detection through data selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 1096–1103. https://doi.org/10.1609/aaai.v34i01.5460 DOI: https://doi.org/10.1609/aaai.v34i01.5460
  60. Zhen, L., Yan, B., Tang, J. L., Nan, Y., & Yang, A. (2023). Social network dynamics, bots, and community-based online misinformation spread: Lessons from anti-refugee and COVID-19 misinformation cases. Information Society, 39(1), 17–34. https://doi.org/10.1080/01972243.2022.2139031 DOI: https://doi.org/10.1080/01972243.2022.2139031

How to Cite

Sigurdh, H., & Lindgren, S. (2025). What’s in a bot? Tracing epistemic differences across expert discourses. Human Technology, 21(2), 395–415. https://doi.org/10.14254/1795-6889.2025.21-2.8