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Software for: "It is just a flu: Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations"

Kostantinos Papadamou; Savvas Zannettou; Jeremy Blackburn; Emiliano De Cristofaro; Gianluca Stringhini; Michael Sirivianos


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{
  "description": "<p><strong>Abstract:</strong></p>\n\n<p>The role played by YouTube&#39;s recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood.&nbsp;Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic.&nbsp;In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTube.&nbsp;We collect 6.6K videos related to COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask movements.&nbsp;Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learning classifier to detect pseudoscientific videos with an accuracy of 0.79.</p>\n\n<p>We quantify user exposure to this content on various parts of the platform&nbsp;and how this exposure changes based on the user&#39;s watch history.&nbsp;We find that YouTube suggests more pseudoscientific content regarding&nbsp;traditional pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging ones (like COVID-19).&nbsp;At the same time, these recommendations are more common on the search results page than on a user&#39;s homepage or in the recommendation section when actively watching videos.&nbsp;Finally, we shed light on how a user&#39;s watch history substantially affects the type of recommended videos.</p>\n\n<p><strong>What do we offer in this software?</strong></p>\n\n<p>We make&nbsp;publicly available to the research community, as well as the open-source community, the following tools, and libraries:</p>\n\n<ol>\n\t<li>\n\t<p>The&nbsp;codebase&nbsp;of a Deep Learning Classifier for pseudoscientific videos detection on YouTube, and&nbsp;examples&nbsp;on how to train and test it;</p>\n\t</li>\n\t<li>\n\t<p>A&nbsp;library&nbsp;that simplifies the usage of the trained classifier and implements all the required tasks for the classification of YouTube videos;</p>\n\t</li>\n\t<li>\n\t<p>An&nbsp;open-source library&nbsp;that provides a unified framework for assessing the effects of personalization on YouTube video recommendations in multiple parts of the platform: a) the homepage; b) the search results page; and c) the video recommendations section (recommendations when watching videos).</p>\n\t</li>\n</ol>\n\n<p>The codebase is also available on <a href=\"https://github.com/kostantinos-papadamou/pseudoscience-paper\">GitHub</a>.</p>\n\n<p>If you make use of any modules available in this codebase in your work, please cite the following paper:</p>\n\n<pre><code>@article{papadamou2020just,\n  title={\"It is just a flu\": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations},\n  author={Papadamou, Kostantinos and Zannettou, Savvas and Blackburn, Jeremy and De Cristofaro, Emiliano and Stringhini, Gianluca and Sirivianos, Michael},\n  journal={arXiv preprint arXiv:2010.11638},\n  year={2020}\n}</code></pre>\n\n<p>&nbsp;</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Cyprus University of Technology", 
      "@type": "Person", 
      "name": "Kostantinos Papadamou"
    }, 
    {
      "affiliation": "Max Planck Institute", 
      "@type": "Person", 
      "name": "Savvas Zannettou"
    }, 
    {
      "affiliation": "Binghamton University", 
      "@type": "Person", 
      "name": "Jeremy Blackburn"
    }, 
    {
      "affiliation": "University College London", 
      "@type": "Person", 
      "name": "Emiliano De Cristofaro"
    }, 
    {
      "affiliation": "Boston University", 
      "@type": "Person", 
      "name": "Gianluca Stringhini"
    }, 
    {
      "affiliation": "Cyprus University of Technology", 
      "@type": "Person", 
      "name": "Michael Sirivianos"
    }
  ], 
  "url": "https://zenodo.org/record/4580999", 
  "datePublished": "2021-03-04", 
  "keywords": [
    "YouTube", 
    "YouTube Videos", 
    "YouTube's Recommendation Algorithm", 
    "Science", 
    "Pseudoscience", 
    "Pseudoscientific Misinformation", 
    "Watch History", 
    "COVID-19", 
    "Anti-vaccination", 
    "Anti-mask", 
    "Flat Earth"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.4580999", 
  "@id": "https://doi.org/10.5281/zenodo.4580999", 
  "@type": "SoftwareSourceCode", 
  "name": "Software for: \"It is just a flu: Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations\""
}
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