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Limited individual attention and online virality of low-quality information

This article was retracted on 07 January 2019


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.

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Figure 1: Illustration of the meme diffusion model and predicted popularity distributions.
Figure 2: Average popularity of memes.
Figure 3: Discriminative power and diversity.
Figure 4: Tradeoff between discriminative power with diversity, and empirical calibration.
Figure 5: Popularity distributions.


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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 ( 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.

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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.

Corresponding author

Correspondence to Diego F. M. Oliveira.

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The authors declare no competing interests.

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Qiu, X., F. M. Oliveira, D., Sahami Shirazi, A. et al. Limited individual attention and online virality of low-quality information. Nat Hum Behav 1, 0132 (2017).

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