Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Cyborgs for strategic communication on social media (2401.06582v1)

Published 12 Jan 2024 in cs.SI

Abstract: Social media platforms are a key ground of information consumption and dissemination. Key figures like politicians, celebrities and activists have leveraged on its wide user base for strategic communication. Strategic communications, or StratCom, is the deliberate act of information creation and distribution. Its techniques are used by these key figures for establishing their brand and amplifying their messages. Automated scripts are used on top of personal touches to quickly and effectively perform these tasks. The combination of automation and manual online posting creates a Cyborg social media profile, which is a hybrid between bot and human. In this study, we establish a quantitative definition for a Cyborg account, which is an account that are detected as bots in one time window, and identified as humans in another. This definition makes use of frequent changes of bot classification labels and large differences in bot likelihood scores to identify Cyborgs. We perform a large-scale analysis across over 3.1 million users from Twitter collected from two key events, the 2020 Coronavirus pandemic and 2020 US Elections. We extract Cyborgs from two datasets and employ tools from network science, natural language processing and manual annotation to characterize Cyborg accounts. Our analyses identify Cyborg accounts are mostly constructed for strategic communication uses, have a strong duality in their bot/human classification and are tactically positioned in the social media network, aiding these accounts to promote their desired content. Cyborgs are also discovered to have long online lives, indicating their ability to evade bot detectors, or the graciousness of platforms to allow their operations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (32)
  1. Alarifi A, Alsaleh M and Al-Salman A (2016) Twitter turing test: Identifying social machines. Information Sciences 372: 332–346.
  2. Augenstein I, Vlachos A and Bontcheva K (2016) Usfd at semeval-2016 task 6: Any-target stance detection on twitter with autoencoders. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016). pp. 389–393.
  3. Beskow DM and Carley KM (2018) Bot-hunter: a tiered approach to detecting & characterizing automated activity on twitter. In: Conference paper. SBP-BRiMS: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation, volume 3. p. 3.
  4. Blei DM, Ng AY and Jordan MI (2003) Latent dirichlet allocation. Journal of machine Learning research 3(Jan): 993–1022.
  5. In: Transforming Digital Worlds: 13th International Conference, iConference 2018, Sheffield, UK, March 25-28, 2018, Proceedings 13. Springer, pp. 17–26.
  6. Borchers NS (2019) Social media influencers in strategic communication.
  7. In: International Conference on Human-Computer Interaction. Springer, pp. 311–323.
  8. Chavoshi N, Hamooni H and Mueen A (2016) Debot: Twitter bot detection via warped correlation. In: Icdm, volume 18. pp. 28–65.
  9. IEEE Transactions on dependable and secure computing 9(6): 811–824.
  10. Elfardy H and Diab M (2016) Cu-gwu perspective at semeval-2016 task 6: Ideological stance detection in informal text. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016). pp. 434–439.
  11. Advances in Neural Information Processing Systems 35: 35254–35269.
  12. Disinformation, Misinformation, and Fake News in Social Media : 95–114.
  13. Gorwa R and Guilbeault D (2020) Unpacking the social media bot: A typology to guide research and policy. Policy & Internet 12(2): 225–248.
  14. Grimme C, Assenmacher D and Adam L (2018) Changing perspectives: Is it sufficient to detect social bots? In: Social Computing and Social Media. User Experience and Behavior: 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Las Vegas, NV, USA, July 15-20, 2018, Proceedings, Part I 10. Springer, pp. 445–461.
  15. International journal of strategic communication 1(1): 3–35.
  16. Holtzhausen D and Zerfass A (2014) Strategic communication: Opportunities and challenges of the research area. The Routledge handbook of strategic communication : 27–41.
  17. Information Sciences 332: 72–83.
  18. Kawintiranon K and Singh L (2021) Knowledge enhanced masked language model for stance detection. In: Proceedings of the 2021 conference of the north american chapter of the association for computational linguistics: human language technologies. pp. 4725–4735.
  19. Kumar S (2020) Social media analytics for stance mining a multi-modal approach with weak supervision. PhD Thesis, Carnegie Mellon University.
  20. Lange-Ionatamishvili E, Svetoka S and Geers K (2015) Strategic communications and social media in the russia ukraine conflict. Cyber war in perspective: Russian aggression against Ukraine : 103–111.
  21. McHugh ML (2012) Interrater reliability: the kappa statistic. Biochemia medica 22(3): 276–282.
  22. Ng LHX and Carley KM (2022) Pro or anti? a social influence model of online stance flipping. IEEE Transactions on Network Science and Engineering 10(1): 3–19.
  23. Ng LHX and Carley KM (2023) Botbuster: Multi-platform bot detection using a mixture of experts. In: Proceedings of the International AAAI Conference on Web and Social Media, volume 17. pp. 686–697.
  24. Ng LHX, Robertson DC and Carley KM (2022) Stabilizing a supervised bot detection algorithm: How much data is needed for consistent predictions? Online Social Networks and Media 28: 100198.
  25. Information Processing & Management 57(4): 102250.
  26. Rajadesingan A and Liu H (2014) Identifying users with opposing opinions in twitter debates. In: Social Computing, Behavioral-Cultural Modeling and Prediction: 7th International Conference, SBP 2014, Washington, DC, USA, April 1-4, 2014. Proceedings 7. Springer, pp. 153–160.
  27. Rauchfleisch A and Kaiser J (2020) The false positive problem of automatic bot detection in social science research. PloS one 15(10): e0241045.
  28. Sobhani P, Inkpen D and Zhu X (2017) A dataset for multi-target stance detection. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. pp. 551–557.
  29. Volkova S and Bell E (2017) Identifying effective signals to predict deleted and suspended accounts on twitter across languages. In: Proceedings of the International AAAI Conference on Web and Social Media, volume 11. pp. 290–298.
  30. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016). pp. 384–388.
  31. In: Proceedings of the AAAI conference on artificial intelligence, volume 34. pp. 1096–1103.
  32. Journal of Computer-Mediated Communication 24(4): 182–202.

Summary

We haven't generated a summary for this paper yet.