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  • × author_ss:"Menczer, F."
  • × theme_ss:"Internet"
  1. Menczer, F.: Lexical and semantic clustering by Web links (2004) 0.01 
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    Abstract
    Recent Web-searching and -mining tools are combining text and link analysis to improve ranking and crawling algorithms. The central assumption behind such approaches is that there is a correiation between the graph structure of the Web and the text and meaning of pages. Here I formalize and empirically evaluate two general conjectures drawing connections from link information to lexical and semantic Web content. The link-content conjecture states that a page is similar to the pages that link to it, and the link-cluster conjecture that pages about the same topic are clustered together. These conjectures are offen simply assumed to hold, and Web search tools are built an such assumptions. The present quantitative confirmation sheds light an the connection between the success of the latest Web-mining techniques and the small world topology of the Web, with encouraging implications for the design of better crawling algorithms.
  2. Nikolov, D.; Lalmas, M.; Flammini, A.; Menczer, F.: Quantifying biases in online information exposure (2019) 0.01 
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    Abstract
    Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."

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