Ranking algorithms play a crucial role in online platforms ranging from search engines to recommender systems. In this paper, we identify a surprising consequence of popularity-based rankings: the fewer the items reporting a given signal, the higher the share of the overall traffic they collectively attract. This few-get-richer effect emerges in settings where there are few distinct classes of items (e.g., left-leaning news sources versus right-leaning news sources), and items are ranked based on their popularity. We demonstrate analytically that the few-get-richer effect emerges when people tend to click on top-ranked items and have heterogeneous preferences for the classes of items. Using simulations, we analyze how the strength of the effect changes with assumptions about the setting and human behavior.We also test our predictions experimentally in an online experiment with human participants. Our findings have important implications to understand the spread of misinformation.

Comunicació presentada a: the 2019 World Wide Web Conference (WWW ’19), celebrat a San Francisco, Estat Units d'Amèrica, del 13 al 17 de maig de 2019.

F. Germano acknowledges financial support from Grant ECO2017-89240-P (AEI/FEDER, UE) from the Spanish MINECO. G. Le Mens benefited from financial support from Ramon y Cajal Fellowship RYC-2014-15035 and Grant #AEI/FEDER UE-PSI2016-75353 from the Spanish MINECO, and ERC Consolidator Grant #772268 from the European Commission. V. Gómez is funded by the Ramon y Cajal program RYC-2015-18878 (AEI/MINECO/FSE,UE) and the María de Maeztu Units of Excellence Programme (MDM-2015-0502). The research leading to these results has received funding from “La Caixa" Banking Foundation. Data from the experiment are available at https://osf.io/nwjyf/.