The few-get-richer: a surprising consequence of popularity-based rankings
This work addresses the problem of understanding how ranking algorithms can amplify biases in online platforms, with implications for misinformation spread, though it is incremental in building on existing models of popularity dynamics.
The paper identifies that in popularity-based rankings, items from fewer distinct classes attract a larger share of overall traffic, a phenomenon termed the few-get-richer effect, demonstrated analytically and validated through simulations and an online experiment.
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.