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Limited individual attention and online virality of low-quality information

Author

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  • Xiaoyan Qiu

    (School of Economics and Management, Shanghai Institute of Technology
    Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University)

  • Diego F. M. Oliveira

    (Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University)

  • Alireza Sahami Shirazi

    (Yahoo Research)

  • Alessandro Flammini

    (Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University
    Indiana University Network Science Institute)

  • Filippo Menczer

    (Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University
    Yahoo Research
    Indiana University Network Science Institute)

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.

Suggested Citation

  • Xiaoyan Qiu & Diego F. M. Oliveira & Alireza Sahami Shirazi & Alessandro Flammini & Filippo Menczer, 2017. "Limited individual attention and online virality of low-quality information," Nature Human Behaviour, Nature, vol. 1(7), pages 1-7, July.
    • Handle: RePEc:nat:nathum:v:1:y:2017:i:7:d:10.1038_s41562-017-0132
    DOI: 10.1038/s41562-017-0132
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    Citations

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    Cited by:

    1. Farivar, Samira & Wang, Fang, 2022. "Effective influencer marketing: A social identity perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    2. Fabio Padovano & Pauline Mille, 2022. "Education, fake news and the PBC," Economics Working Paper from Condorcet Center for political Economy at CREM-CNRS 2022-01-ccr, Condorcet Center for political Economy.
    3. Alberto Acerbi, 2019. "Cognitive attraction and online misinformation," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-7, December.
    4. Miguel-Ángel Esteban-Navarro & Antonia-Isabel Nogales-Bocio & Miguel-Ángel García-Madurga & Tamara Morte-Nadal, 2021. "Spanish Fact-Checking Services: An Approach to Their Business Models," Publications, MDPI, vol. 9(3), pages 1-18, August.
    5. Jayles, Bertrand & Escobedo, Ramon & Cezera, Stéphane & Blanchet, Adrien & Kameda, Tatsuya & Sire, Clément & Théraulaz, Guy, 2020. "The impact of incorrect social information on collective wisdom in human groups," TSE Working Papers 1101, Toulouse School of Economics (TSE).
    6. Oliveira, Diego F.M. & Chan, Kevin S., 2019. "The effects of trust and influence on the spreading of low and high quality information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 657-663.
    7. Fabio Sabatini & Francesco Sarracino, 2019. "Online Social Networks and Trust," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 229-260, February.
    8. Yiangos Papanastasiou, 2020. "Fake News Propagation and Detection: A Sequential Model," Management Science, INFORMS, vol. 66(5), pages 1826-1846, May.
    9. Kathie M. d'I. Treen & Hywel T. P. Williams & Saffron J. O'Neill, 2020. "Online misinformation about climate change," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(5), September.
    10. Marcella Tambuscio & Diego F. M. Oliveira & Giovanni Luca Ciampaglia & Giancarlo Ruffo, 2018. "Network segregation in a model of misinformation and fact-checking," Journal of Computational Social Science, Springer, vol. 1(2), pages 261-275, September.

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