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A psychometric analysis of information propagation in online social networks using latent trait theory

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Abstract

The paper explores use of psychometric analysis based on latent trait theory to study quality of information propagation in online social networks. The collective intelligence of users of the network could be used to determine credibility of information. We use the latent trait of ability of users to distinguish between true information and misinformation as a measure of social computing in the network. Using repropagation features available in these networks as an affirmation of credibility of information, we build a dichotomous item response matrix which is evaluated using different models in latent trait theory. This enables us to detect presence of misinformation and also evaluate trust of users in the sources of information. Trust between users and sources of information is further used to construct a polytomous matrix. The matrices are evaluated using polytomous latent theory models to evaluate the types of trust and segregate possible collusion of users to spread misinformation. We show experimental results of psychometric analysis carried out in data sets obtained from ‘Twitter’ to support our claim.

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References

  1. Adali S, Escriva R, Goldberg MK, Hayvanovych M, Magdon-Ismail M, Szymanski BK, Wallace WA, Williams G (2010) Measuring behavioral trust in social networks. In: IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, pp 150–152

  2. Baker FB, Kim SH (2004) Item response theory: parameter estimation techniques. CRC Press, Boca Raton

    MATH  Google Scholar 

  3. BBC News: Boston bombing: How internet detectives got it very wrong. http://www.bbc.com/news/technology-22214511. Last Accessed 22 Mar 2014

  4. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10,008

    Article  Google Scholar 

  5. De Domenico M, Lima A, Mougel P, Musolesi M (2013) The anatomy of a scientific rumor. Scientific reports 3. http://www.nature.com/articles/srep02980. Accessed 16 Aug 2015

  6. Fayers PM, Hays RD (2005) Assessing quality of life in clinical trials: methods and practice. Oxford University Press, Oxford

    Google Scholar 

  7. Garner SD (2005) High-level colloquium on information literacy and lifelong learning. Bibliotheca Alexandra, Alexandria

    Google Scholar 

  8. Golbeck J (2006) Trust on the world wide web: a survey. Found Trends Web Sci 1(2):131–197

    Article  MATH  Google Scholar 

  9. Golbeck J, Parsia B, Hendler J (2003) Trust networks on the semantic web. Springer, New York

    Book  MATH  Google Scholar 

  10. Hawksey M (2013) Twitter archiving google spreadsheet TAGS v5. JISC CETIS MASHe: the musing of martin Hawksey (EdTech Explorer). http://mashe.hawksey.info/2013/02/twitter-archive-tagsv5/. Accessed 20 Mar 2014

  11. Karlova NA, Fisher KE (2013) Plz RT: a social diffusion model of misinformation and disinformation for understanding human information behaviour. Inform Res 18(1):1–17

    Google Scholar 

  12. Karlova NA, Lee JH (2011) Notes from the underground city of disinformation: a conceptual investigation. Proc Am Soc Inform Sci Technol 48(1):1–9

    Article  Google Scholar 

  13. Lewandowsky S, Ecker UK, Seifert CM, Schwarz N, Cook J (2012) Misinformation and its correction continued influence and successful debiasing. Psychol Sci Public Interest 13(3):106–131

    Article  Google Scholar 

  14. Mendoza M, Poblete B, Castillo C (2010) Twitter under crisis: can we trust what we rt? In: Proceedings of the first workshop on social media analytics. ACM, pp 71–79

  15. Morozov E (2009) Swine flu: Twitter’s power to misinform. In: Foreign Policy Magazine Website Post

  16. Muraki E (1992) A generalized partial credit model: application of an em algorithm. Appl Psychol Measur 16(2):159–176

    Article  Google Scholar 

  17. Partchev I (2004) A visual guide to item response theory. Friedrich Schiller Universität Jena, Jena

    Google Scholar 

  18. Ratkiewicz J, Conover M, Meiss M, Gonçalves B, Patil S, Flammini A, Menczer F (2011) Truthy: mapping the spread of astroturf in microblog streams. In: Proceedings of the 20th international conference companion on world wide web. ACM, pp 249–252

  19. Reuters IANS (2013) Ethnic riots sweep assam, at least 30 killed. http://in.reuters.com/article/2012/07/24/india-assam-riots-floods-idINDEE86N04520120724. Last Accessed 21 Jul 2013

  20. Samejima F (1997) Graded response model. In: Handbook of modern item response theory. Springer, New York, pp 85–100

  21. Sherchan W, Nepal S, Paris C (2013) A survey of trust in social networks. ACM Comput Surv (CSUR) 45(4):47

    Article  Google Scholar 

  22. Stahl BC (2006) On the difference or equality of information, misinformation, and disinformation: A critical research perspective. Inform Sci Int J Emerg Transdiscipline 9:83–96

    Google Scholar 

Download references

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Authors and Affiliations

  1. BITS-Pilani, Hyderabad Campus, Hyderabad, India

    K. P. Krishna Kumar, Agrima Srivastava & G. Geethakumari

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  1. K. P. Krishna Kumar
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  2. Agrima Srivastava
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  3. G. Geethakumari
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Correspondence to K. P. Krishna Kumar.

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Kumar, K.P.K., Srivastava, A. & Geethakumari, G. A psychometric analysis of information propagation in online social networks using latent trait theory. Computing 98, 583–607 (2016). https://doi.org/10.1007/s00607-015-0472-7

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  • DOI: https://doi.org/10.1007/s00607-015-0472-7

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