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.
@cite_31 "Google may favor popularity over richness; it provides advertising that competes directly with "editorial" content; it so overwhelmingly dominates the industry that users seldom get a second opinion, and this is unlikely to change. "
@cite_31
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Search engines like Google are essential to navigating the Web’s endless supply of news, political information, and citizen discourse. The mechanisms and conditions under which search results are selected should therefore be of considerable interest to media scholars, political theorists, and citizens alike. In this chapter, I adopt a “deliberative” ideal for search engines and examine whether Google exhibits the “same old” media biases of mainstreaming, hypercommercialism, and industry consolidation. In the end, serious objections to Google are raised: Google may favor popularity over richness; it provides advertising that competes directly with “editorial” content; it so overwhelmingly dominates the industry that users seldom get a second opinion, and this is unlikely to change. Ultimately, however, the results of this analysis may speak less about Google than about contradictions in the deliberative ideal and the so-called “inherently democratic” nature of the Web. Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries–in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets. Search engines have become key media for our scientific, economic, and social activities by enabling people to access information on the web despite its size and complexity. On the down side, search engines bias the traffic of users according to their page ranking strategies, and it has been argued that they create a vicious cycle that amplifies the dominance of established and already popular sites. This bias could lead to a dangerous monopoly of information. We show that, contrary to intuition, empirical data do not support this conclusion; popular sites receive far less traffic than predicted. We discuss a model that accurately predicts traffic data patterns by taking into consideration the topical interests of users and their searching behavior in addition to the way search engines rank pages. The heterogeneity of user interests explains the observed mitigation of search engines’ popularity bias. With the rise of web 2.0 there is an ever-expanding source of interesting media because of the proliferation of usergenerated content. However, mixed in with this is a large amount of noise that creates a proverbial “needle in the haystack” when searching for relevant content. Although there is hope that the rich network of interwoven metadata may contain enough structure to eventually help sift through this noise, currently many sites serve up only the “most popular” things. Identifying only the most popular items can be useful, but doing so fails to take into account the famous “long tail” behavior of the web—the notion that the collective effect of small, niche interests can outweigh the market share of the few blockbuster (i.e. most-popular) items—thus providing only content that has mass appeal and masking the interests of the idiosyncratic many. YouTube, for example, hosts over 40 million videos— enough content to keep one occupied for more than 200 years. Are there intelligent tools to search through this information-rich environment and identify interesting and relevant content? Is there a way to identify emerging trends or “hot topics” in addition to indexing the long tail for content that has real value?