Search (2 results, page 1 of 1)

  • × theme_ss:"Internet"
  • × author_ss:"Lalmas, M."
  1. Nikolov, D.; Lalmas, M.; Flammini, A.; Menczer, F.: Quantifying biases in online information exposure (2019) 0.00 
    0.004963813 = product of:
  0.034746688 = sum of:
    0.02465703 = weight(_text_:web in 4986) [ClassicSimilarity], result of:
      0.02465703 = score(doc=4986,freq=4.0), product of:
        0.09670874 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.029633347 = queryNorm
        0.25496176 = fieldWeight in 4986, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4986)
    0.010089659 = weight(_text_:information in 4986) [ClassicSimilarity], result of:
      0.010089659 = score(doc=4986,freq=8.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.19395474 = fieldWeight in 4986, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4986)
  0.14285715 = coord(2/14)
    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."
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.3, S.218-229
  2. Lehmann, J.; Castillo, C.; Lalmas, M.; Baeza-Yates, R.: Story-focused reading in online news and its potential for user engagement (2017) 0.00 
    3.6034497E-4 = product of:
  0.0050448296 = sum of:
    0.0050448296 = weight(_text_:information in 3529) [ClassicSimilarity], result of:
      0.0050448296 = score(doc=3529,freq=2.0), product of:
        0.052020688 = queryWeight, product of:
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.029633347 = queryNorm
        0.09697737 = fieldWeight in 3529, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.7554779 = idf(docFreq=20772, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3529)
  0.071428575 = coord(1/14)
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.4, S.869-883

Authors