3 Matching Annotations
  1. Nov 2021
  2. www.isdglobal.org www.isdglobal.org
    Recommended Reading: Amazon’s algorithms, conspiracy theories and extremist literature - ISD
    1
    1. lucyparfitt16 08 Nov 2021

      Recommended Reading: Amazon’s algorithms, conspiracy theories and extremist literature. (n.d.). ISD. Retrieved November 8, 2021, from https://www.isdglobal.org/isd-publications/recommended-reading-amazons-algorithms-conspiracy-theories-and-extremist-literature/

      is:report lang:en conspiracy theory Amazon algorithm misinformation radicalization social media platform extremist disinformation promotion recommendation
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    isdglobal.org/isd-publications/recommended-reading-amazons-algorithms-conspiracy-theories-and-extremist-literature/
  3. Jan 2021
  4. twitter.com twitter.com
    ReconfigBehSci on Twitter
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    1. Grace1999 29 Jan 2021

      NetScience[@net_science] (2020-08-23) Pseudoscientific Content on YouTube: Assessing the Effects of Watch History on the Recommendation Algorithm. (arXiv:2010.11638v1 [http://cs.CY]) http://arxiv.org/abs/2010.11638. Twitter.Retrieved from: https://twitter.com/SciBeh/status/1319564893503225856

      is:twitter lang:en COVID-19 Misinformation Pseudoscience Social media Youtube Watch history Recommendation algorithm
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    twitter.com/SciBeh/status/1319564893503225856
  5. Aug 2020
  6. www.nature.com www.nature.com
    Extracting user influence from ratings and trust for rating prediction in recommendations
    1
    1. marta_radosevic 14 Aug 2020

      Shi, W., Wang, L., & Qin, J. (2020). Extracting user influence from ratings and trust for rating prediction in recommendations. Scientific Reports, 10(1), 13592. https://doi.org/10.1038/s41598-020-70350-1

      is:article lang:en recommendation trust prediction rating influence collaborative filtering recommendation systems algorithm model accuracy internet social rating
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    nature.com/articles/s41598-020-70350-1