Skip to main content
SpringerLink
Log in

You Are Known by Your Friends: Leveraging Network Metrics for Bot Detection in Twitter

  • Chapter
  • First Online:
Open Source Intelligence and Cyber Crime

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Automated social media bots have existed almost as long as the social media platforms they inhabit. Although efforts have long existed to detect and characterize these autonomous agents, these efforts have redoubled in the recent months following sophisticated deployment of bots by state and non-state actors. This research will study the differences between human and bot social communication networks by conducting an account snow ball data collection, and then evaluate network, content, temporal, and user features derived from this communication network in several bot detection machine learning models. We will compare this model to the other models of the bot-hunter toolbox as well as current state of the art models. In the evaluation, we will also explore and evaluate relevant training data. Finally, we will demonstrate the application of the bot-hunter suite of tools in Twitter data collected around the Swedish National elections in 2018.

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

Access this chapter

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. K.S. Adewole, N.B. Anuar, A. Kamsin, K.D. Varathan, S.A. Razak, Malicious accounts: dark of the social networks. J. Netw. Comput. Appl. 79, 41–67 (2017)

    Article  Google Scholar 

  2. L.M. Aiello, M. Deplano, R. Schifanella, G. Ruffo, People are strange when you’re a stranger: Impact and influence of bots on social networks. Links 697(483,151), 1–566 (2012)

    Google Scholar 

  3. A. Almaatouq, E. Shmueli, M. Nouh, A. Alabdulkareem, V.K. Singh, M. Alsaleh, A. Alarifi, A. Alfaris, et al., If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. Int. J. Inf. Secur. 15(5), 475–491 (2016)

    Article  Google Scholar 

  4. A. Ananthalakshmi, Ahead of Malaysian Polls, Bots Flood Twitter with Pro-government… (2018)

    Google Scholar 

  5. D.A Bader, S. Kintali, K. Madduri, M. Mihail, Approximating betweenness centrality, in International Workshop on Algorithms and Models for the Web-Graph (Springer, Berlin, 2007), pp. 124–137

    Google Scholar 

  6. A. Bavelas, A mathematical model for group structures. Hum. Organ. 7(3), 16–30 (1948)

    Article  Google Scholar 

  7. A.I. Bawaba, The Loop, in Thousands of Twitter Bots are Attempting to Silence Reporting on Yemen (2017)

    Google Scholar 

  8. M. Benigni, K.M. Carley, From tweets to intelligence: understanding the islamic jihad supporting community on twitter, in Proceedings of Social, Cultural, and Behavioral Modeling: 9th International Conference, SBP-BRiMS 2016, Washington, DC, USA, June 28-July 1, 2016 (Springer, Berlin, 2016), pp. 346–355

    Google Scholar 

  9. M.C. Benigni, K. Joseph, K.M. Carley, Online extremism and the communities that sustain it: detecting the ISIS supporting community on twitter. PLoS One 12(12), e0181405 (2017)

    Google Scholar 

  10. D. Beskow, K.M. Carley, Bot conversations are different: Leveraging network metrics for bot detection in twitter, in 2018 International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE, Piscataway, 2018), pp. 176–183

    Google Scholar 

  11. D. Beskow, K.M. Carley, Introducing bothunter: A tiered approach to detection and characterizing automated activity on twitter, in International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, ed. by H. Bisgin, A. Hyder, C. Dancy, R. Thomson (Springer, Berlin, 2018)

    Google Scholar 

  12. D. Beskow, K.M. Carley, 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, ed. by H. Bisgin, A. Hyder, C. Dancy, R. Thomson (Springer, Berlin, 2018)

    Google Scholar 

  13. A. Bessi, E. Ferrara, Social Bots Distort the 2016 US Presidential Election Online Discussion (2016)

    Google Scholar 

  14. S.Y. Bhat, M. Abulaish, Community-based features for identifying spammers in online social networks, in 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE, Piscataway, 2013), pp. 100–107

    Google Scholar 

  15. D.M. Blei, A.Y. Ng, M.I. Jordan, Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  16. V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Google Scholar 

  17. R.S. Burt, Structural Holes: The Social Structure of Competition (Harvard University Press, Cambridge, 2009)

    Google Scholar 

  18. K.M. Carley, G. Cervone, N. Agarwal, H. Liu, Social cyber-security, in International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, ed. by H. Bisgin, A. Hyder, C. Dancy, R. Thomson (Springer, Berlin, 2018)

    Google Scholar 

  19. D. Cartwright, F. Harary, Structural balance: a generalization of Heider’s theory. Psychol. Rev. 63(5), 277 (1956)

    Google Scholar 

  20. N. Chavoshi, H. Hamooni, A. Mueen, Debot: Twitter bot detection via warped correlation, in IEEE International Conference on Data Mining (ICDM) (2016), pp. 817–822

    Google Scholar 

  21. N. Chavoshi, H. Hamooni, A. Mueen, On-demand bot detection and archival system, in Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee (2017), pp. 183–187

    Google Scholar 

  22. C.-M. Chen, D.J. Guan, Q.-K. Su, Feature set identification for detecting suspicious urls using bayesian classification in social networks. Inform. Sci. 289, 133–147 (2014)

    Article  Google Scholar 

  23. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, M. Tesconi, Fame for sale: efficient detection of fake twitter followers. Decis. Support. Syst. 80, 56–71 (2015)

    Article  Google Scholar 

  24. S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, M. Tesconi, Social fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling. IEEE Trans. Dependable Secure Comput. 15(4), 561–576 (2018)

    Google Scholar 

  25. C.A. Davis, O. Varol, E. Ferrara, A. Flammini, F. Menczer, Botornot: a system to evaluate social bots, in Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee (2016), pp. 273–274

    Google Scholar 

  26. D.J. Dekker, Measures of Simmelian Tie Strength, Simmelian Brokerage, and, the Simmelianly Brokered (2006)

    Google Scholar 

  27. R.I.M. Dunbar, Coevolution of neocortical size, group size and language in humans. Behav. Brain Sci. 16(4), 681–694 (1993)

    Article  Google Scholar 

  28. E. Ferrara, Measuring social spam and the effect of bots on information diffusion in social media (2017). arXiv preprint:1708.08134

    Google Scholar 

  29. S. Fortunato, Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  30. O. Frank, Sampling and estimation in large social networks. Soc. Networks 1(1), 91–101 (1978)

    Article  MathSciNet  Google Scholar 

  31. L.C. Freeman, Centrality in social networks conceptual clarification. Soc. Networks 1(3), 215–239 (1978)

    Article  Google Scholar 

  32. L.C. Freeman, Centered graphs and the structure of ego networks. Math. Soc. Sci. 3(3), 291–304 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  33. K. Gani, H. Hacid, R. Skraba, Towards multiple identity detection in social networks, in Proceedings of the 21st International Conference on World Wide Web (ACM, New York, 2012), pp. 503–504

    Google Scholar 

  34. M. Gjoka, M. Kurant, C.T. Butts, A. Markopoulou, Practical recommendations on crawling online social networks. IEEE J. Sel. Areas Commun. 29(9), 1872–1892 (2011)

    Article  Google Scholar 

  35. A. Glaser, Russian Bots are Trying to Sow Discord on Twitter After Charlottesville (2017)

    Google Scholar 

  36. L.A. Goodman, Snowball sampling, in The Annals of Mathematical Statistics (1961), pp. 148–170

    Google Scholar 

  37. M.S. Granovetter, The strength of weak ties, in Social Networks (Elsevier, Amsterdam, 1977), pp. 347–367

    Google Scholar 

  38. C. Grimme, M. Preuss, L. Adam, H. Trautmann, Social bots: human-like by means of human control? Big Data 5(4), 279–293 (2017)

    Article  Google Scholar 

  39. A. Hagberg, P. Swart, D.S. Chult, Exploring Network Structure, Dynamics, and Function Using Networkx (Los Alamos National Lab.(LANL), Los Alamos, 2008). Technical report

    Google Scholar 

  40. F. Heider, Attitudes and cognitive organization. J. Psychol. 21(1), 107–112 (1946)

    Article  Google Scholar 

  41. P.W. Holland, S. Leinhardt, Transitivity in structural models of small groups. Compar. Group Stud. 2(2), 107–124 (1971)

    Article  Google Scholar 

  42. P.W. Holland, S. Leinhardt, A method for detecting structure in sociometric data, in Social Networks (Elsevier, Amsterdam, 1977), pp. 411–432

    Google Scholar 

  43. P.N. Howard, B. Kollanyi, Bots,# strongerin, and# brexit: computational propaganda during the uk-eu referendum, in Browser Download This Paper (2016)

    Google Scholar 

  44. B. Huang, K.M. Carley, On predicting geolocation of tweets using convolutional neural networks, in International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (Springer, Berlin, 2017), pp. 281–291

    Google Scholar 

  45. L. Katz, A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MATH  Google Scholar 

  46. D. Krackhardt, The ties that torture: Simmelian tie analysis in organizations. Res. Sociol. Organ. 16(1), 183–210 (1999)

    Google Scholar 

  47. S. Kudugunta, E. Ferrara, Deep neural networks for bot detection (2018). arXiv preprint:1802.04289

    Google Scholar 

  48. S. Lee, J. Kim, Early filtering of ephemeral malicious accounts on twitter. Comput. Commun. 54, 48–57 (2014)

    Article  Google Scholar 

  49. K. Lee, J. Caverlee, S. Webb, Uncovering social spammers: social honeypots+ machine learning, in Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM, New York, 2010), pp. 435–442

    Google Scholar 

  50. K. Lee, B.D. Eoff, J. Caverlee, Seven months with the devils: a long-term study of content polluters on twitter, in ICWSM (2011)

    Google Scholar 

  51. D. Liu, B. Mei, J. Chen, Z. Lu, X. Du, Community based spammer detection in social networks, in International Conference on Web-Age Information Management (Springer, Berlin, 2015), pp. 554–558

    Google Scholar 

  52. C. Lumezanu, N. Feamster, H. Klein, # bias: Measuring the tweeting behavior of propagandists, in Sixth International AAAI Conference on Weblogs and Social Media (2012)

    Google Scholar 

  53. Z. Miller, B. Dickinson, W. Deitrick, W. Hu, A.H. Wang, Twitter spammer detection using data stream clustering. Inf. Sci. 260, 64–73 (2014)

    Article  Google Scholar 

  54. A. Mislove, M. Marcon, K.P. Gummadi, P. Druschel, B. Bhattacharjee, Measurement and analysis of online social networks, in Proceedings of the 7th ACM SIGCOMM conference on Internet measurement (ACM, New York, 2007), pp. 29–42

    Google Scholar 

  55. L.M. Neudert, B. Kollanyi, P.N. Howard, Junk News and Bots During the German Federal Presidency Election: What Were German Voters Sharing Over Twitter? (2017)

    Google Scholar 

  56. B. Nimmo, #botspot: Twelve Ways to Spot a Bot—dfrlab—medium (2017). https://medium.com/dfrlab/botspot-twelve-ways-to-spot-a-bot-aedc7d9c110c (Accessed on 11/03/2018).

  57. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  58. J. Stubbs, J. Ahlander, Exclusive: Right-Wing Sites Swamp Sweden with ‘Junk News’ in Tight Election Race | Reuters (2018). https://www.reuters.com/article/us-sweden-election-disinformation-exclus/exclusive-right-wing-sites-swamp-sweden-with-junk-news-in-tight-election-race-idUSKCN1LM0DN (Accessed on 11/20/2018).

  59. V.S. Subrahmanian, A. Azaria, S. Durst, V. Kagan, A. Galstyan, K. Lerman, L. Zhu, E. Ferrara, A. Flammini, F. Menczer, The darpa twitter bot challenge. Computer 49(6), 38–46 (2016)

    Article  Google Scholar 

  60. Swedish Defence Reserch Agency, Antalet botar på twitter ökar inför valet—totalförsvarets forskningsinstitut (2018). https://www.foi.se/press--nyheter/nyheter/nyhetsarkiv/2018-08-29-antalet-botar-pa-twitter-okar-infor-valet.html (Accessed on 11/20/2018)

  61. K. Thomas, C. Grier, D. Song, V. Paxson, Suspended accounts in retrospect: an analysis of twitter spam, in Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference (ACM, New York, 2011), pp. 243–258

    Google Scholar 

  62. Twitter. Elections Integrity Data Archive. https://about.twitter.com/en_us/values/elections-integrity.html#us-elections (Accessed on 03/30/2019)

  63. O. Varol, E. Ferrara, C.A. Davis, F. Menczer, A. Flammini, Online human-bot interactions: Detection, estimation, and characterization (2017). arXiv preprint:1703.03107

    Google Scholar 

  64. J.-P. Verkamp, M. Gupta, Five incidents, one theme: Twitter spam as a weapon to drown voices of protest, in Presented as part of the 3rd USENIX Workshop on Free and Open Communications on the Internet (2013).

    Google Scholar 

  65. B. Viswanath, M.A. Bashir, M. Crovella, S. Guha, K.P. Gummadi, B. Krishnamurthy, A. Mislove, Towards detecting anomalous user behavior in online social networks, in USENIX Security Symposium (2014), pp. 223–238

    Google Scholar 

  66. G. Wang, M. Mohanlal, C. Wilson, X. Wang, M. Metzger, H. Zheng, B.Y. Zhao, Social turing tests: crowdsourcing sybil detection (2012). arXiv preprint:1205.3856

    Google Scholar 

  67. H. Yu, M. Kaminsky, P.B. Gibbons, A. Flaxman, Sybilguard: defending against sybil attacks via social networks, in ACM SIGCOMM Computer Communication Review, vol. 36 (ACM, New York, 2006), pp. 267–278

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Office of Naval Research (ONR) Multidisciplinary University Research Initiative Award N000140811186 and Award N000141812108, the Army Research Laboratory Award W911NF1610049, Defense Threat Reductions Agency Award HDTRA11010102, and the Center for Computational Analysis of Social and Organization 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, ARL, DTRA, or the U.S. government.

Author information

Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    David M. Beskow & Kathleen M. Carley

Authors
  1. David M. Beskow
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kathleen M. Carley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David M. Beskow .

Editor information

Editors and Affiliations

  1. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Mohammad A. Tayebi

  2. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Uwe Glässer

  3. School of Computing, Queen’s University, Kingston, ON, Canada

    David B. Skillicorn

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Beskow, D.M., Carley, K.M. (2020). You Are Known by Your Friends: Leveraging Network Metrics for Bot Detection in Twitter. In: Tayebi, M.A., Glässer, U., Skillicorn, D.B. (eds) Open Source Intelligence and Cyber Crime. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-41251-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-41251-7_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41250-0

  • Online ISBN: 978-3-030-41251-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation