Letter

Limited individual attention and online virality of low-quality information

  • Nature Human Behaviour 1, Article number: 0132 (2017)
  • doi:10.1038/s41562-017-0132
  • Download Citation
Received:
Accepted:
Published online:

Abstract

Social media are massive marketplaces where ideas and news compete for our attention1. Previous studies have shown that quality is not a necessary condition for online virality2 and that knowledge about peer choices can distort the relationship between quality and popularity3. However, these results do not explain the viral spread of low-quality information, such as the digital misinformation that threatens our democracy4. We investigate quality discrimination in a stylized model of an online social network, where individual agents prefer quality information, but have behavioural limitations in managing a heavy flow of information. We measure the relationship between the quality of an idea and its likelihood of becoming prevalent at the system level. We find that both information overload and limited attention contribute to a degradation of the market’s discriminative power. A good tradeoff between discriminative power and diversity of information is possible according to the model. However, calibration with empirical data characterizing information load and finite attention in real social media reveals a weak correlation between quality and popularity of information. In these realistic conditions, the model predicts that low-quality information is just as likely to go viral, providing an interpretation for the high volume of misinformation we observe online.

  • Subscribe to Nature Human Behaviour for full access:

    $99

    Subscribe

  • Purchase article full text and PDF:

    $32

    Buy now

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    . in Computers, Communication, and the Public Interest (ed. Greenberger, M.) 37–52 (Johns Hopkins Univ. Press, 1971).

  2. 2.

    , , & Competition among memes in a world with limited attention. Sci. Rep. 2, 335 (2012).

  3. 3.

    , & . Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 854–856 (2006).

  4. 4.

    et al. in Global Risks 2013 8th edn (ed. Howell, L.) Section 2 (World Economic Forum, 2013);

  5. 5.

    . Areopagitica (1644);

  6. 6.

    . in Philosophy of Economics (ed. Mäki, U.) 823–862 (Handbook of the Philosophy of Science Series, North-Holland, 2012).

  7. 7.

    . The Selfish Gene (Oxford Univ. Press, 1989).

  8. 8.

    , & Modeling users’ activity on twitter networks: validation of Dunbar’s number. PLoS ONE 6, e22656 (2011).

  9. 9.

    . The attention economy and the net. First Monday (1997).

  10. 10.

    . Attention economies. J. Econ. Theory 133, 266–294 (2007).

  11. 11.

    , & . The production of information in the attention economy. Sci. Rep. 5, 9452 (2015).

  12. 12.

    , , , & . Predicting the political alignment of twitter users. In Proc. 3rd IEEE Conference on Social Computing (SocialCom) 192–199 (IEEE, 2011).

  13. 13.

    , , & . Partisan asymmetries in online political activity. EPJ Data Sci. 1, 6 (2012).

  14. 14.

    , , & . More tweets, more votes: social media as a quantitative indicator of political behaviour. PLoS ONE 8, e79449 (2013).

  15. 15.

    . Stardom and talent. Am. Econ. Rev. 75, 208–12 (1985).

  16. 16.

    . Democracy’s Double-Edged Sword: How Internet Use Changes Citizens’ Views of Their Government (Johns Hopkins Univ. Press, 2014).

  17. 17.

    . The Wisdom of Crowds (Anchor, 2005).

  18. 18.

    . The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (Princeton Univ. Press, 2008).

  19. 19.

    , , & . How social influence can undermine the wisdom of crowd effect. Proc. Natl Acad. Sci. USA 108, 9020–9025 (2011).

  20. 20.

    , , & . Integrating advice and experience: learning and decision making with social and nonsocial cues. J. Pers. Soc. Psychol. 100, 967–982 (2011).

  21. 21.

    & . Contextualizing person perception: distributed social cognition. Psychol. Rev. 116, 343–364 (2009).

  22. 22.

    . Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2, 175–220 (1998).

  23. 23.

    . Evil acts and malicious gossip: a multiagent model of the effects of gossip in socially distributed person perception. Pers. Soc. Psychol. Rev. 18, 311–325 (2014).

  24. 24.

    & . Complex contagions and the weakness of long ties. Am. J. Sociol. 113, 702–734 (2007).

  25. 25.

    & . Structuring computer-mediated communication systems to avoid information overload. Commun. ACM 28, 680–689 (1985).

  26. 26.

    . Recent research on selective exposure to information. J. Exp. Soc. Psychol. 19, 41–80 (1986).

  27. 27.

    et al. The role of information diffusion in the evolution of social networks. In Proc. 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Dhillon, I. S. et al.) 356–364 (ACM, 2013).

  28. 28.

    , , , & . On the efficiency of the information networks in social media. In Proc. 9th ACM International Conference on Web Search and Data Mining 83–92 (ACM, 2016).

  29. 29.

    . The dissemination of culture a model with local convergence and global polarization. J. Confl. Resolut. 41, 203–226 (1997).

  30. 30.

    . Republic.com 2.0 (Princeton Univ. Press, 2009).

  31. 31.

    . The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think (Penguin, 2011).

  32. 32.

    . The law of group polarization. J. Political Philos. 10, 175–195 (2002).

  33. 33.

    et al. Political polarization on twitter. In Proc. 5th International AAAI Conference on Weblogs and Social Media (AAAI, 2011).

  34. 34.

    , & . Myside bias, rational thinking, and intelligence. Curr. Dir. Psychol. Sci. 22, 259–264 (2013).

  35. 35.

    , , & . Measuring online social bubbles. PeerJ Comput. Sci. 1, e38 (2015).

  36. 36.

    , & . Situating social influence processes: dynamic, multidirectional flows of influence within social networks. Pers. Soc. Psychol. Rev. 11, 279–300 2007.

  37. 37.

    & . The Person and the Situation (McGraw-Hill, 1991).

  38. 38.

    & . When corrections fail: the persistence of political misperceptions. J. Polit. Behav. 32, 303–330 (2010).

  39. 39.

    et al. The spreading of misinformation online. Proc. Natl Acad. Sci. USA 113, 554–559 (2016).

  40. 40.

    et al. Detecting and tracking political abuse in social media. In Proc. 5th International AAAI Conference on Weblogs and Social Media (AAAI, 2011).

  41. 41.

    , , , & . The rise of social bots. Comm. ACM 57, 96–104 (2016).

  42. 42.

    & . Robust dynamic classes revealed by measuring the response function of a social system. Proc. Natl Acad. Sci. USA 105, 15649–15653 (2008).

  43. 43.

    , , , & . Characterizing and modeling the dynamics of online popularity. Phys. Rev. Lett. 105, 158701(2010).

  44. 44.

    . Fame emerges as a result of small memory. Phys. Rev. E 77, 036118 (2008).

  45. 45.

    . Social computing and the attention economy. J. Stat. Phys. 151, 329–339 (2013).

  46. 46.

    & . Novelty and collective attention. Proc. Natl Acad. Sci. USA 104, 17599–17601 (2007).

  47. 47.

    & . How visibility and divided attention constrain social contagion. In Proc. ASE/IEEE International Conference on Social Computing 249–257 (IEEE, 2012).

  48. 48.

    & . in Social Computing, Behavioral Modeling and Prediction. SBP 2015. Lecture Notes in Computer Science Vol. 9021 (eds Agarwal, N. et al.) 101–110 (Springer, 2015).

  49. 49.

    , , & . Competition-induced criticality in a model of meme popularity. Phys. Rev. Lett. 112, 048701 (2014).

  50. 50.

    , , & . Effects of network structure, competition and memory time on social spreading phenomena. Phys. Rev. X 6, 021019 (2016).

  51. 51.

    . Contagion. Rev. Econ. Stud. 67, 57–78 (2000).

  52. 52.

    & . Generalization of epidemic theory. Nature 204, 225–228 (1964).

  53. 53.

    & . Epidemics and rumours. Nature 204, 1118 (1964).

  54. 54.

    The Mathematical Theory of Infectious Diseases and Its Applications (Charles Griffin & Co., 1975).

  55. 55.

    , , & . Modeling blog dynamics. In Proc. International AAAI Conference on Weblogs and Social Media (eds Adar, E. et al.) (AAAI, 2009).

  56. 56.

    , & . Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).

  57. 57.

    & . A mathematical theory of citing. J. Assoc. Inf. Sci. Technol. 58, 1661–1673 (2007).

  58. 58.

    . A new measure of rank correlation. Biometrika 30, 81–89 (1938).

  59. 59.

    , , , & . Online human-bot interactions: detection, estimation, and characterization. In Proc. International AAAI Conference on Web and Social Media (AAAI, 2017).

  60. 60.

    & . Social bots distort the 2016 U.S. Presidential election online discussion. First Monday (2016).

  61. 61.

    , & . Structure and evolution of online social networks. In Proc. 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 611–617 (ACM, 2006).

  62. 62.

    , , & . What is Twitter, a social network or a news media? In Proc. 19th International Conference on World Wide Web 591–600 (ACM, 2010).

  63. 63.

    & . Growing scale-free networks with tunable clustering. Phys. Rev. E 65, 026107 (2002).

Download references

Acknowledgements

We are grateful to Twitter for providing public post data, to Tumblr for mobile scrolling data, to C. Silverman for the Emergent data, and to J. Gleeson, K. Church, S. Buthpitiya, M. Patel and G. Ciampaglia for discussions and assistance with the data analysis. This work was supported in part by the James S. McDonnell Foundation (grant 220020274) and the National Science Foundation (award CCF-1101743). X.Q. thanks the NaN group in the Center for Complex Networks and Systems Research (http://cnets.indiana.edu) for the hospitality during her stay at the Indiana University School of Informatics and Computing. She was supported by grants from the National Natural Science Foundation of China (No. 90924030), the China Scholarship Council, the ‘Shuguang’ Project of Shanghai Education Commission (No. 09SG38), and the Program of Social Development of Metropolis and Construction of Smart City (No. 085SHDX001). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

    • Xiaoyan Qiu
    •  & Diego F. M. Oliveira

    These authors contributed equally to this work.

Affiliations

  1. School of Economics and Management, Shanghai Institute of Technology, 100 Haiquan Road, Fengxian District, Shanghai 201418, China.

    • Xiaoyan Qiu
  2. Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University, 919 East 10th Street, Bloomington, Indiana 47408, USA.

    • Xiaoyan Qiu
    • , Diego F. M. Oliveira
    • , Alessandro Flammini
    •  & Filippo Menczer
  3. Yahoo Research, 701 1st Avenue, Sunnyvale, California 94089, USA.

    • Alireza Sahami Shirazi
    •  & Filippo Menczer
  4. Indiana University Network Science Institute, 1001 IN-45, Bloomington, Indiana 47408, USA.

    • Alessandro Flammini
    •  & Filippo Menczer

Authors

  1. Search for Xiaoyan Qiu in:

  2. Search for Diego F. M. Oliveira in:

  3. Search for Alireza Sahami Shirazi in:

  4. Search for Alessandro Flammini in:

  5. Search for Filippo Menczer in:

Contributions

A.F. and F.M. developed the research question. X.Q., D.F.M.O., A.F. and F.M. designed the model. X.Q. and D.F.M.O. conducted the simulations and the primary analyses. D.F.M.O., A.S.S. and F.M. collected and analysed the empirical data. D.F.M.O., A.F. and F.M. wrote the manuscript. X.Q. and A.S.S. edited the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Diego F. M. Oliveira.