Limited individual attention and online virality of low-quality information

Xiaoyan Qiu, Diego F. M. Oliveira (), Alireza Sahami Shirazi, Alessandro Flammini and Filippo Menczer
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Xiaoyan Qiu: School of Economics and Management, Shanghai Institute of Technology
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
Filippo Menczer: Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University

Nature Human Behaviour, 2017, vol. 1, issue 7, 1-7

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41562-017-0132

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