Skip to main content
SpringerLink
Log in
Book cover

Multidisciplinary International Symposium on Disinformation in Open Online Media

MISDOOM 2022: Disinformation in Open Online Media pp 79–99Cite as

New Automation for Social Bots: From Trivial Behavior to AI-Powered Communication

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13545 ))

Abstract

Today, implications of automation in social media, specifically whether social bots can be used to manipulate people’s thoughts and behaviors are discussed. Some believe that social bots are simple tools that amplify human-created content, while others claim that social bots do not exist at all and that the research surrounding them is a conspiracy theory. This paper discusses the potential of automation in online media and the challenges that may arise as technological advances continue. The authors believe that automation in social media exists, but acknowledge that there is room for improvement in current scientific methodology for investigating this phenomenon. They focus on the evolution of social bots, the state-of-the-art content generation technologies, and the perspective of content generation in games. They provide a background discussion on the human perception of content in computer-mediated communication and describe a new automation level, from which they derive interdisciplinary challenges.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    In the most general sense, we understand automation to mean technically controlled processes that ensure a specified target achievement largely without human intervention. In closed-loop systems, target achievement is controlled by feedback mechanisms and through self-regulating control mechanisms. In open-loop systems, no feedback mechanism is implemented [31].

  2. 2.

    “Convincingly” in the sense that social media users are not aware of messaging with an automaton or consuming artificially generated content. This does not relate to direct change of opinion.

  3. 3.

    https://thispersondoesnotexist.com/.

  4. 4.

    https://thisxdoesnotexist.com/.

  5. 5.

    https://www.wired.com/story/facebook-removes-accounts-ai-generated-photos/.

  6. 6.

    https://openai.com/blog/better-language-models/.

  7. 7.

    The readers may ask themselves whether they can judge who wrote the abstract of this paper - the authors or GPT-3. In fact, the abstract has been generated automatically by GPT-3 using only the introduction chapter of this paper as input. No editing has been done by the authors.

  8. 8.

    https://www.nomanssky.com/.

  9. 9.

    https://www.markrjohnsongames.com/games/ultima-ratio-regum/.

References

  1. Adelani, D.I., Mai, H., Fang, F., Nguyen, H.H., Yamagishi, J., Echizen, I.: Generating sentiment-preserving fake online reviews using neural language models and their human- and machine-based detection. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) AINA 2020. AISC, vol. 1151, pp. 1341–1354. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44041-1_114

    Chapter  Google Scholar 

  2. Alabdulkarim, A., Li, S., Peng, X.: Automatic story generation: challenges and attempts (2021). https://doi.org/10.48550/ARXIV.2102.12634. https://arxiv.org/abs/2102.12634

  3. Alam, F., et al.: A survey on multimodal disinformation detection. In: The 29th International Conference on Computational Linguistics (COLING 2022). ACL (2022)

    Google Scholar 

  4. Alhayan, F., Pennington, D.R., Ruthven, I.: “She seems more human’’: understanding twitter users’ credibility assessments of dementia-related information. In: Smits, M. (ed.) Information for a Better World: Shaping the Global Future. LNCS, vol. 13193, pp. 292–313. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96960-8_20

    Chapter  Google Scholar 

  5. Arsenyan, J., Mirowska, A.: Almost human? A comparative case study on the social media presence of virtual influencers. Int. J. Hum. Comput. Stud. 155, 102694 (2021). https://doi.org/10.1016/j.ijhcs.2021.102694

    Article  Google Scholar 

  6. Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H., Grimme, C.: Demystifying social bots: on the intelligence of automated social media actors. Soc. Media + Soc. 6(3) (2020). https://doi.org/10.1177/2056305120939264

  7. Assenmacher, D., et al.: Benchmarking crisis in social media analytics: a solution for the data-sharing problem. Soc. Sci. Comput. Rev. (2021). https://doi.org/10.1177/08944393211012268

    Article  Google Scholar 

  8. Bessi, A., Ferrara, E.: Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 21(11) (2016). https://doi.org/10.5210/fm.v21i11.7090. https://firstmonday.org/ojs/index.php/fm/article/view/7090

  9. Bilewicz, M., et al.: Artificial intelligence against hate: intervention reducing verbal aggression in the social network environment. Aggress. Behav. 47(3), 260–266 (2021)

    Article  Google Scholar 

  10. Boneh, D., Grotto, A.J., McDaniel, P., Papernot, N.: How relevant is the Turing test in the age of sophisbots? IEEE Secur. Priv. 17(6), 64–71 (2019)

    Article  Google Scholar 

  11. Brown, T., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901. Curran Associates, Inc. (2020)

    Google Scholar 

  12. Chen, D., Dolan, W.: Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, pp. 190–200. Association for Computational Linguistics (2011). https://aclanthology.org/P11-1020

  13. Ciechanowski, L., Przegalinska, A., Magnuski, M., Gloor, P.: In the shades of the uncanny valley: an experimental study of human-chatbot interaction. Futur. Gener. Comput. Syst. 92, 539–548 (2019). https://doi.org/10.1016/j.future.2018.01.055

    Article  Google Scholar 

  14. Cresci, S.: A decade of social bot detection. Commun. ACM 63(10), 72–83 (2020)

    Article  Google Scholar 

  15. Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., Tesconi, M.: The paradigm-shift of social spambots: evidence, theories, and tools for the arms race. In: The 26th International Conference on World Wide Web Companion (WWW 2017), pp. 963–972 (2017)

    Google Scholar 

  16. Cresci, S., Petrocchi, M., Spognardi, A., Tognazzi, S.: The coming age of adversarial social bot detection. First Monday 26(7) (2021)

    Google Scholar 

  17. Cresci, S., Trujillo, A., Fagni, T.: Personalized interventions for online moderation. In: The 33rd ACM Conference on Hypertext and Social Media (HT 2022), pp. 248–251. ACM (2022)

    Google Scholar 

  18. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Minneapolis, Minnesota, pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/N19-1423

  19. Di Pietro, R., Caprolu, M., Raponi, S., Cresci, S.: New Dimensions of Information Warfare. Advances in Information Security, vol. 84. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-60618-3

    Book  Google Scholar 

  20. Di Pietro, R., Cresci, S.: Metaverse: security and privacy issues. In: The 3rd IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS 2021), pp. 281–288. IEEE (2021)

    Google Scholar 

  21. Echeverría, J., De Cristofaro, E., Kourtellis, N., Leontiadis, I., Stringhini, G., Zhou, S.: LOBO: evaluation of generalization deficiencies in Twitter bot classifiers. In: The 34th Annual Computer Security Applications Conference (ACSAC 2018), pp. 137–146 (2018)

    Google Scholar 

  22. Edwards, C., Beattie, A.J., Edwards, A., Spence, P.R.: Differences in perceptions of communication quality between a twitterbot and human agent for information seeking and learning. Comput. Hum. Behav. 65, 666–671 (2016). https://doi.org/10.1016/j.chb.2016.07.003

    Article  Google Scholar 

  23. Edwards, C., Edwards, A., Spence, P.R., Shelton, A.K.: Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on twitter. Comput. Hum. Behav. 33, 372–376 (2014). https://doi.org/10.1016/j.chb.2013.08.013

    Article  Google Scholar 

  24. Eichenberg, C., Black, S., Weinbach, S., Parcalabescu, L., Frank, A.: Magma-multimodal augmentation of generative models through adapter-based finetuning. arXiv preprint arXiv:2112.05253 (2021). https://doi.org/10.48550/arXiv.2112.05253

  25. Fagni, T., Falchi, F., Gambini, M., Martella, A., Tesconi, M.: TweepFake: about detecting deepfake tweets. PLoS ONE 16(5), e0251415 (2021)

    Article  Google Scholar 

  26. Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016)

    Article  Google Scholar 

  27. Gallwitz, F., Kreil, M.: The rise and fall of ‘social bot’ research. SSRN 3814191 (2021). https://ssrn.com/abstract=3814191

  28. Gillespie, T.: Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media. Yale University Press, New Haven (2018)

    Google Scholar 

  29. Grimme, C., Assenmacher, D., Adam, L.: Changing perspectives: is it sufficient to detect social bots? In: Meiselwitz, G. (ed.) SCSM 2018. LNCS, vol. 10913, pp. 445–461. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91521-0_32

    Chapter  Google Scholar 

  30. Grimme, C., Preuss, M., Adam, L., Trautmann, H.: Social bots: human-like by means of human control? Big Data 5(4), 279–293 (2017). https://doi.org/10.1089/big.2017.0044

    Article  Google Scholar 

  31. Groover, M.: Fundamentals of Modern Manufacturing: Materials, Processes, and Systems. Wiley, Hoboken (2010)

    Google Scholar 

  32. He, B., Ahamad, M., Kumar, S.: PETGEN: personalized text generation attack on deep sequence embedding-based classification models. In: The 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2021), pp. 575–584 (2021)

    Google Scholar 

  33. Ho, A., Hancock, J., Miner, A.S.: Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. J. Commun. 68(4), 712–733 (2018). https://doi.org/10.1093/joc/jqy026

    Article  Google Scholar 

  34. Im, J., Tandon, S., Chandrasekharan, E., Denby, T., Gilbert, E.: Synthesized social signals: computationally-derived social signals from account histories. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA. ACM (2020). https://doi.org/10.1145/3313831.3376383

  35. Ippolito, D., Duckworth, D., Callison-Burch, C., Eck, D.: Automatic detection of generated text is easiest when humans are fooled. In: Proceedings of the 58th Annual Meeting of the ACL, pp. 1808–1822. ACL (2020). https://doi.org/10.18653/v1/2020.acl-main.164

  36. Jeon, Y.A.: Reading social media marketing messages as simulated self within a metaverse: an analysis of gaze and social media engagement behaviors within a metaverse platform. In: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 301–303 (2022). https://doi.org/10.1109/VRW55335.2022.00068

  37. Jhaver, S., Boylston, C., Yang, D., Bruckman, A.: Evaluating the effectiveness of deplatforming as a moderation strategy on Twitter. In: The 24th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW 2021). ACM (2021)

    Google Scholar 

  38. Johnson, M.R.: Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 6th Workshop on Procedural Content Generation. Dundee, Scotland, UK (2016). https://www.pcgworkshop.com

  39. Knox, J.: The metaverse, or the serious business of tech frontiers. Postdigit. Sci. Educ. 4(2), 207–215 (2022). https://doi.org/10.1007/s42438-022-00300-9

    Article  Google Scholar 

  40. Lee, L.H., et al.: All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda (2021). arXiv Preprint. https://doi.org/10.48550/ARXIV.2110.05352

  41. Liapis, A., Yannakakis, G.N., Nelson, M.J., Preuss, M., Bidarra, R.: Orchestrating game generation. IEEE Trans. Games 11(1), 48–68 (2019). https://doi.org/10.1109/TG.2018.2870876

    Article  Google Scholar 

  42. Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, Barcelona, Spain, pp. 74–81. ACL (2004). https://aclanthology.org/W04-1013

  43. Livingstone, D.: Turing’s test and believable AI in games. Comput. Entertain. 4(1), 6 (2006). https://doi.org/10.1145/1111293.1111303

    Article  Google Scholar 

  44. Mendoza, M., Tesconi, M., Cresci, S.: Bots in social and interaction networks: detection and impact estimation. ACM Trans. Inf. Syst. 39(1), 1–32 (2020)

    Article  Google Scholar 

  45. Meta Platforms: The Facebook Company Is Now Meta (2021). https://about.fb.com/news/2021/10/facebook-company-is-now-meta/. Accessed 22 May 2022

  46. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015). https://doi.org/10.1038/nature14236

    Article  Google Scholar 

  47. Mori, M.: The uncanny valley. Energy 7(4), 33–35 (1970). https://doi.org/10.1109/MRA.2012.2192811

    Article  Google Scholar 

  48. Mozgovoy, M., Preuss, M., Bidarra, R.: Guest editorial special issue on team AI in games. IEEE Trans. Games 13(4), 327–329 (2021). https://doi.org/10.1109/TG.2021.3127967

    Article  Google Scholar 

  49. Nass, C., Steuer, J., Tauber, E.R.: Computers are social actors. In: Conference Companion on Human Factors in Computing Systems, Boston, MA, USA, CHI 1994, p. 204. Association for Computing Machinery (1994). https://doi.org/10.1145/259963.260288

  50. Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M.: Coordinated behavior on social media in 2019 UK general election. In: The 15th International AAAI Conference on Web and Social Media (ICWSM 2021), pp. 443–454. AAAI (2021)

    Google Scholar 

  51. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL 2002, Philadelphia, Pennsylvania, p. 311. Association for Computational Linguistics (2001). https://doi.org/10.3115/1073083.1073135

  52. Pohl, J.S., Assenmacher, D., Seiler, M.V., Trautmann, H., Grimme, C.: Artificial social media campaign creation for benchmarking and challenging detection approaches. In: Proceedings of the 16th International Conference on Web and Social Media. NEATCLasS, Association for the Advancement of Artificial Intelligence (AAI), Hybrid: Atlanta, Georgia, US and Online (2022)

    Google Scholar 

  53. Rabkin, M.: Connect 2021 Recap: Horizon Home, the Future of Work, Presence Platform, and More (2021). https://www.oculus.com/blog/connect-2021-recap-horizon-home-the-future-of-work-presence-platform-and-more/. Accessed 22 May 2022

  54. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. Technical report, OpenAI (2018)

    Google Scholar 

  55. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (2022). https://doi.org/10.48550/ARXIV.2204.06125

  56. Rauchfleisch, A., Kaiser, J.: The false positive problem of automatic bot detection in social science research. PLoS ONE 15(10), e0241045 (2020)

    Article  Google Scholar 

  57. Rauschnabel, P.A., Felix, R., Hinsch, C., Shahab, H., Alt, F.: What is XR? Towards a framework for augmented and virtual reality. Comput. Hum. Behav. 133, 107289 (2022). https://doi.org/10.1016/j.chb.2022.107289

    Article  Google Scholar 

  58. Reed, S., et al.: A generalist agent (2022). https://doi.org/10.48550/ARXIV.2205.06175. https://arxiv.org/abs/2205.06175

  59. Reeves, B., Nass, C.: The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Pla. Bibliovault OAI Repository, the University of Chicago Press (1996)

    Google Scholar 

  60. Riedl, M.O.: Computational narrative intelligence: a human-centered goal for artificial intelligence. arXiv preprint arXiv:1602.06484 (2016)

  61. Risi, S., Preuss, M.: From chess and atari to StarCraft and beyond: how game AI is driving the world of AI. KI - Künstliche Intelligenz 34(1), 7–17 (2020). https://doi.org/10.1007/s13218-020-00647-w

    Article  Google Scholar 

  62. Saygin, A.P., Chaminade, T., Ishiguro, H., Driver, J., Frith, C.: The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions. Soc. Cogn. Affect. Neurosci. 7(4), 413–422 (2012). https://doi.org/10.1093/scan/nsr025

    Article  Google Scholar 

  63. Shin, M., Song, S.W., Chock, T.M.: Uncanny valley effects on friendship decisions in virtual social networking service. Cyberpsychol. Behav. Soc. Netw. 22(11), 700–705 (2019). https://doi.org/10.1089/cyber.2019.0122

    Article  Google Scholar 

  64. Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016). https://doi.org/10.1038/nature16961

    Article  Google Scholar 

  65. Skjuve, M., Haugstveit, I., Følstad, A., Brandtzaeg, P.: Help! Is my chatbot falling into the uncanny valley? An empirical study of user experience in human-chatbot interaction. Hum. Technol. 15, 30–54 (2019). https://doi.org/10.17011/ht/urn.201902201607

  66. Spence, P.R., Edwards, A., Edwards, C., Jin, X.: ‘The bot predicted rain, grab an umbrella’: few perceived differences in communication quality of a weather twitterbot versus professional and amateur meteorologists. Behav. Inf. Technol. 38(1), 101–109 (2019). https://doi.org/10.1080/0144929X.2018.1514425

    Article  Google Scholar 

  67. Stephenson, N.: Snow Crash. Metropolis Media (1992)

    Google Scholar 

  68. Sundar, S.S.: The MAIN Model: A Heuristic Approach to Understanding Technology Effects on Credibility. Digital Media, p. 29 (2008)

    Google Scholar 

  69. Tardelli, S., Avvenuti, M., Tesconi, M., Cresci, S.: Characterizing social bots spreading financial disinformation. In: Meiselwitz, G. (ed.) HCII 2020. LNCS, vol. 12194, pp. 376–392. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49570-1_26

    Chapter  Google Scholar 

  70. Tekiroglu, S., Bonaldi, H., Fanton, M., Guerini, M.: Using pre-trained language models for producing counter narratives against hate speech: a comparative study. In: Findings of the Association for Computational Linguistics (ACL 2022), pp. 3099–3114. ACL (2022)

    Google Scholar 

  71. Togelius, J.: We tried learning AI from games. How about learning from players? (2022). https://modl.ai/learning-ai-from-players. modl.ai blog

  72. Togelius, J., et al.: Procedural content generation: goals, challenges and actionable steps. In: Lucas, S.M., Mateas, M., Preuss, M., Spronck, P., Togelius, J. (eds.) Artificial and Computational Intelligence in Games, Dagstuhl Follow-Ups, vol. 6, pp. 61–75. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany (2013). https://doi.org/10.4230/DFU.Vol6.12191.61. http://drops.dagstuhl.de/opus/volltexte/2013/4336

  73. Trujillo, A., Cresci, S.: Make reddit great again: assessing community effects of moderation interventions on r/The_Donald. In: The 25th ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW 2022). ACM (2022)

    Google Scholar 

  74. Tsimpoukelli, M., Menick, J., Cabi, S., Eslami, S.M.A., Vinyals, O., Hill, F.: Multimodal few-shot learning with frozen language models. In: Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems (2021)

    Google Scholar 

  75. Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 6000–6010. Curran Associates Inc., Red Hook (2017)

    Google Scholar 

  76. Walther, J.B.: Computer-mediated communication: impersonal, interpersonal, and hyperpersonal interaction. Commun. Res. 23(1), 3–43 (1996). https://doi.org/10.1177/009365096023001001

    Article  Google Scholar 

  77. Weber, D., Neumann, F.: Amplifying influence through coordinated behaviour in social networks. Soc. Netw. Anal. Min. 11(1), 1–42 (2021). https://doi.org/10.1007/s13278-021-00815-2

    Article  Google Scholar 

  78. Xu, K., Liao, T.: Explicating cues: a typology for understanding emerging media technologies. J. Comput.-Mediat. Commun. 25(1), 32–43 (2020). https://doi.org/10.1093/jcmc/zmz023

    Article  Google Scholar 

  79. Yang, C., Harkreader, R., Gu, G.: Empirical evaluation and new design for fighting evolving Twitter spammers. IEEE Trans. Inf. Forensics Secur. 8(8), 1280–1293 (2013)

    Article  Google Scholar 

  80. Yang, K.C., Varol, O., Hui, P.M., Menczer, F.: Scalable and generalizable social bot detection through data selection. In: Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, vol. 34 (2020). https://doi.org/10.1609/aaai.v34i01.5460

  81. Yannakakis, G.N., Togelius, J.: Experience-driven procedural content generation. IEEE Trans. Affect. Comput. 2(3), 147–161 (2011). https://doi.org/10.1109/T-AFFC.2011.6

    Article  Google Scholar 

  82. Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: evaluating text generation with BERT. arXiv preprint arXiv:1904.09675 (2019)

Download references

Author information

Authors and Affiliations

  1. University of Münster, Münster, Germany

    Christian Grimme & Janina Pohl

  2. IIT-CNR, Pisa, Italy

    Stefano Cresci

  3. Aleph Alpha, Heidelberg, Germany

    Ralf Lüling

  4. LIACS, Universiteit Leiden, Leiden, The Netherlands

    Mike Preuss

Authors
  1. Christian Grimme
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Janina Pohl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefano Cresci
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ralf Lüling
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mike Preuss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christian Grimme .

Editor information

Editors and Affiliations

  1. Boise State University, Boise, ID, USA

    Francesca Spezzano

  2. Universidade do Vale do Rio dos Sinos, São Leopoldo, Brazil

    Adriana Amaral

  3. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Davide Ceolin

  4. Vanderbilt University, Nashville, TN, USA

    Lisa Fazio

  5. Boise State University, Boise, ID, USA

    Edoardo Serra

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Grimme, C., Pohl, J., Cresci, S., Lüling, R., Preuss, M. (2022). New Automation for Social Bots: From Trivial Behavior to AI-Powered Communication. In: Spezzano, F., Amaral, A., Ceolin, D., Fazio, L., Serra, E. (eds) Disinformation in Open Online Media. MISDOOM 2022. Lecture Notes in Computer Science, vol 13545 . Springer, Cham. https://doi.org/10.1007/978-3-031-18253-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-18253-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18252-5

  • Online ISBN: 978-3-031-18253-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation