Interoperable pipelines for social cyber-security: assessing Twitter information operations during NATO Trident Juncture 2018

Abstract

Social cyber-security is an emergent field defining a multidisciplinary and multimethodological approach to studying and preserving the free and open exchange of information online. This work contributes to burgeoning scholarship in this field by advocating the use of interoperable pipelines of computational tools. We demonstrate the utility of such a pipeline in a case study of Twitter information operations during the NATO Trident Juncture Exercises in 2018. By integratively utilizing tools from machine learning, natural language processing, and dynamic network analysis, we uncover significant bot activity aiming to discredit NATO targeted to key allied nations. We further show how to extend such analysis through drill-down procedures on individual influencers and influential subnetworks. We reflect on the value of interoperable pipelines for accumulating and triangulating insights that enable social cyber-security analysts to draw relevant insights across various scales of granularity.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Al-Khateeb S, Hussain MN, Agarwal N (2019) Leveraging social network analysis and cyber forensics approaches to study cyber propaganda campaigns. Social networks and surveillance for society. Springer, Berlin, pp 19–42

    Google Scholar 

  2. Arif A, Stewart LG, Starbird K (2018) Acting the part: examining information operations within# blacklivesmatter discourse. In: Proceedings of the ACM on human–computer interaction 2(CSCW):20

  3. Babcock M, Cox RAV, Kumar S (2019) Diffusion of pro-and anti-false information tweets: the black panther movie case. Comput Math Org Theory 25(1):72–84

    Article  Google Scholar 

  4. Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on web search and data mining, ACM, pp 65–74

  5. Benigni M, Joseph K, Carley KM (2018) Mining online communities to inform strategic messaging: practical methods to identify community-level insights. Comput Math Org Theory 24(2):224–242

    Article  Google Scholar 

  6. Bennett WL, Livingston S (2018) The disinformation order: disruptive communication and the decline of democratic institutions. Eur J Commun 33(2):122–139

    Article  Google Scholar 

  7. Beskow DM, Carley KM (2018a) Bot conversations are different: leveraging network metrics for bot detection in twitter. In: 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 825–832

  8. Beskow DM, Carley KM (2018b) Using random string classification to filter and annotate automated accounts. In: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, New York, pp 367–376

  9. Beskow DM, Carley KM (2019a) Its all in a name: detecting and labeling bots by their name. Comput Math Org Theory 25:24–35

    Article  Google Scholar 

  10. Beskow DM, Carley KM (2019b) Social cybersecurity: an emerging national security requirement. Mil Rev 99(2):117

    Google Scholar 

  11. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

    Google Scholar 

  12. Carley KM, Beskow DM (2017) Trident joust 2017, after action report. Technical report, Center for computational analysis of social and organizational systems, Carnegie Mellon University

  13. Carley KM, Diesner J, Reminga J, Tsvetovat M (2007) Toward an interoperable dynamic network analysis toolkit. Decis Support Syst 43(4):1324–1347

    Article  Google Scholar 

  14. Carley KM, Cervone G, Agarwal N, Liu H (2018) Social cyber-security. In: International conference on social computing. Behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, New York, pp 389–394

  15. Cheng J, Bernstein M, Danescu-Niculescu-Mizil C, Leskovec J (2017) Anyone can become a troll: causes of trolling behavior in online discussions. In: Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing, ACM, pp 1217–1230

  16. Chew PA, Turnley JG (2017) Understanding Russian information operations using unsupervised multilingual topic modeling. In: International conference on social computing. Behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, New York, pp 102–107

  17. Conroy NJ, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol 52(1):1–4

    Article  Google Scholar 

  18. Devlin J, Chang MW, Lee K, Toutanova K (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805

  19. Dubois E, Gaffney D (2014) The multiple facets of influence: identifying political influentials and opinion leaders on twitter. Am Behav Sci 58(10):1260–1277

    Article  Google Scholar 

  20. Ferrara E (2017) Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday. https://doi.org/10.2139/ssrn.2995809

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Garrett RK (2009) Echo chambers online? Politically motivated selective exposure among internet news users. J Comput Mediat Commun 14(2):265–285

    Article  Google Scholar 

  23. Huang B, Carley KM (2017) On predicting geolocation of tweets using convolutional neural networks. In: Lee D, Lin YR, Osgood N, Thomson R (eds) International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, New York, pp 281–291

    Google Scholar 

  24. Jin Z, Caro J, Zhang Y, Luo J (2016) News verification by exploiting conflicting social viewpoints in microblogs. In: Thirtieth AAAI conference on artificial intelligence

  25. Karlsen R, Steen-Johnsen K, Wollebæk D, Enjolras B (2017) Echo chamber and trench warfare dynamics in online debates. Eur J Commun 32(3):257–273

    Article  Google Scholar 

  26. Kudugunta S, Ferrara E (2018) Deep neural networks for bot detection. Inf Sci 467:312–322

    Article  Google Scholar 

  27. Lazer DM, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Metzger MJ, Nyhan B, Pennycook G, Rothschild D et al (2018) The science of fake news. Science 359(6380):1094–1096

    Article  Google Scholar 

  28. Lee K, Caverlee J, Webb S (2010) Uncovering social spammers: social honeypots+ machine learning. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, ACM, pp 435–442

  29. Lee K, Eoff BD, Caverlee J (2011) Seven months with the devils: a long-term study of content polluters on twitter. In: Fifth international AAAI conference on weblogs and social media

  30. Mejias UA, Vokuev NE (2017) Disinformation and the media: the case of Russia and Ukraine. Media Cult Soc 39(7):1027–1042

    Article  Google Scholar 

  31. Michelucci P, Shanley L, Dickinson J, Hirsh H (2015) A us research roadmap for human computation. arXiv preprint arXiv:150507096

  32. Mihaylov T, Georgiev G, Nakov P (2015) Finding opinion manipulation trolls in news community forums. In: Proceedings of the nineteenth conference on computational natural language learning, pp 310–314

  33. Montiel CJ, Boller AJ, Uyheng J, Espina EA (2019) Narrative congruence between populist president duterte and the filipino public: shifting global alliances from the United States to China. J Commun Appl Soc Psychol. https://doi.org/10.1002/casp.2411

    Article  Google Scholar 

  34. Morstatter F, Pfeffer J, Liu H, Carley KM (2013) Is the sample good enough? Comparing data from twitter’s streaming API with twitter’s firehose. In: Seventh international AAAI conference on weblogs and social media

  35. Morstatter F, Wu L, Nazer TH, Carley KM, Liu H (2016) A new approach to bot detection: striking the balance between precision and recall. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 533–540

  36. Nazer TH, Davis M, Karami M, Akoglu L, Koelle D, Liu H (2019) Bot detection: will focusing on recall cause overall performance deterioration? In: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, New York, pp 39–49

  37. Nekmat E, Lee K (2018) Prosocial vs. trolling community on facebook: a comparative study of individual group communicative behaviors. Int J Commun 12:22

    Google Scholar 

  38. Qi S, AlKulaib L, Broniatowski DA (2018) Detecting and characterizing bot-like behavior on twitter. In: International conference on social computing. Behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, New York, pp 228–232

  39. Riquelme F, González-Cantergiani P (2016) Measuring user influence on twitter: a survey. Inf Process Manag 52(5):949–975

    Article  Google Scholar 

  40. Röder M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. In: Proceedings of the eighth ACM international conference on web search and data mining, ACM, pp 399–408

  41. Seah CW, Chieu HL, Chai KMA, Teow LN, Yeong LW (2015) Troll detection by domain-adapting sentiment analysis. In: 2015 18th international conference on information fusion (fusion), IEEE, pp 792–799

  42. Shu K, Sliva A, Wang S, Tang J, Liu H (2017) Fake news detection on social media: a data mining perspective. ACM SIGKDD Explor Newslett 19(1):22–36

    Article  Google Scholar 

  43. Stewart LG, Arif A, Starbird K (2018) Examining trolls and polarization with a retweet network. In: Proc. ACM WSDM, workshop on misinformation and misbehavior mining on the web

  44. Tucker JA, Guess A, Barberá P, Vaccari C, Siegel A, Sanovich S, Stukal D, Nyhan B (2018) Social media, political polarization, and political disinformation: a review of the scientific literature. Political polarization, and political disinformation: a review of the scientific literature (March 19, 2018)

  45. Uyheng J, Carley KM (2019) Characterizing bot networks on twitter: an empirical analysis of contentious issues in the asia-pacific. In: International conference on social computing. Behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, New York, pp 153–162

  46. Varol O, Ferrara E, Davis CA, Menczer F, Flammini A (2017) Online human-bot interactions: detection, estimation, and characterization. In: Eleventh international AAAI conference on web and social media

  47. Wegner P (1996) Interoperability. ACM Comput Surv 28(1):285–287

    Article  Google Scholar 

  48. Yang Z, Wang C, Zhang F, Zhang Y, Zhang H (2015) Emerging rumor identification for social media with hot topic detection. In: 2015 12th web information system and application conference (WISA), IEEE, pp 53–58

  49. Zhou X, Zafarani R, Shu K, Liu H (2019) Fake news: fundamental theories, detection strategies and challenges. In: Proceedings of the twelfth ACM international conference on web search and data mining, ACM, pp 836–837

Download references

Acknowledgements

This work is supported in part by the Office of Naval Research under the Multidisciplinary University Research Initiatives (MURI) Program award number N000141712675 Near Real Time Assessment of Emergent Complex Systems of Confederates, BotHunter award number N000141812108, award number N00014182106 Group Polarization in Social Media: An Effective Network Approach to Communicative Reach and Disinformation, and award number N000141712605 Developing Novel Socio-computational Methodologies to Analyze Multimedia-based Cyber Propaganda Campaigns. This work is also supported by the center for Computational Analysis of Social and Organizational Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or the U.S. government. Additionally, Thomas Magelinski was supported by an ARCS foundation scholarship.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Joshua Uyheng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Uyheng, J., Magelinski, T., Villa-Cox, R. et al. Interoperable pipelines for social cyber-security: assessing Twitter information operations during NATO Trident Juncture 2018. Comput Math Organ Theory 26, 465–483 (2020). https://doi.org/10.1007/s10588-019-09298-1

Download citation

Keywords

  • Social cyber-security
  • Information operations
  • Interoperability