Topical interests and the mitigation of search engine bias

By Santo Fortunato, Alessandro Flammini, Filippo Menczer, Alessandro Vespignani. 2005

136 citations recorded by the source corpus.

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.

Subfields: Information Systems, Management Science and Operations Research, Statistical and Nonlinear Physics

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Citations in the corpus

How algorithmic popularity bias hinders or promotes quality (2017, background): “The use of popularity in ranking algorithms by search engines was alleged to impede novel content from rising to the top, but such an entrenchment eﬀect was shown to be mitigated by diverse user queries [10].”
How Visibility and Divided Attention Constrain Social Contagion (2012, background): “Visibility not only impacts retweet selection, but it helps explain other observations, such as click position bias [37], [49].”
Quantifying Biases in Online Information Exposure (2018, methodology): “We observe that search engines all have low popularity bias, providing evidence against H3 and extending the conclusions of Fortunato et al. (2006) We can also observe that email and social media display a greater variation, which is consistent with H1 and not surprising given the different…”
Measuring Online Social Bubbles (2015, background): “Recommendation systems have a diversifying effect on purchases (Hosanagar et al., 2013), and search engines have had a democratizing effect on the discovery of information, despite the popularity-based signals used in their ranking algorithms (Fortunato et al., 2006).”
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system (2007, background): “This has also been true in other areas [10, 21, 37].”
Random walks and diffusion on networks (2016, background): “(35) is reasonably accurate in some cases but not in others [126–133].”
Analyzing the Video Popularity Characteristics of Large-Scale User Generated Content Systems (2009, methodology): “For instance, the scale-free nature of Web requests has been used to improve search engines and advertising policies [12].”
Beliefs and biases in web search (2013, background): “Over time, searchers’ focus of attention on top-ranked content can create a vicious cycle whereby clicks reinforce popular results [8], although this popularity bias may be offset somewhat by the heterogeneity of searchers’ topical interests [14].”
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017, background): “In recent years, there has been a growing interest in studying the bias in Web search engines results [11, 26, 39, 41, 42].”