Filter Bubbles and Targeted Advertising (20)

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Over a decade ago, tech companies began using algorithms to personalize our experience of the web. Using sophisticated technology and vast amounts of consumer data, companies began to predict our tastes better than we could ourselves. In response, ecommerce expanded, and journalism adapted itself to the personalized attention economy. However, there was a hidden side effect, which Eli Pariser termed "the filter bubble," which is the exclusion of other perspectives from our tech-assisted preferences. Raising many hard questions including data security, political propaganda, and the pervasiveness of digital "junk food," filter bubbles reveal the future challenges of a personalized, automated web. Features such as media literacy questions and terms enhance this collection, encouraging readers to analyze reporting styles and devices.
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    • SKU
      RR12449
  • Manufacturer
    Rosen and PowerKids
  • Series
    Looking Forward: Set 3
  • ISBN-13
    978-1-6428-2269-4
  • Format
    Library Binding
  • # of Pages
    224
  • Size
    6" x 9"
  • Author
    The New York Times Editorial Staff
  • Reading Interest
    7,8,9,10,11,12
  • Publisher or imprint
    Rosen: New York Times Educational Publishing
  • Date Published
    2020
  • Dewey Decimal
    025.5'24--dc23
  • Guided Reading Level
    Z
Over a decade ago, tech companies began using algorithms to personalize our experience of the web. Using sophisticated technology and vast amounts of consumer data, companies began to predict our tastes better than we could ourselves. In response, ecommerce expanded, and journalism adapted itself to the personalized attention economy. However, there was a hidden side effect, which Eli Pariser termed "the filter bubble," which is the exclusion of other perspectives from our tech-assisted preferences. Raising many hard questions including data security, political propaganda, and the pervasiveness of digital "junk food," filter bubbles reveal the future challenges of a personalized, automated web. Features such as media literacy questions and terms enhance this collection, encouraging readers to analyze reporting styles and devices.

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