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Social Spread

Moderating Misinformation on Facebook and Twitter

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How Algorithms Create and Prevent Fake News

Abstract

In this chapter, I explore several ways in which algorithms interact with the complex dynamics of social media when it comes to fake news. First, I set the stage with some context, issues, and examples that help us better understand what has happened and what’s at stake. Next, I look at how algorithms have been used to scrape data from social media platforms to provide remarkable quantitative insight into how fake news spreads—both organically and when part of deliberate disinformation campaigns. Along the way, the role in this spread played by the social media platforms’ own content recommendation algorithms is explored. Attention is then turned to the algorithmic tools that social media companies—primarily Facebook and Twitter—have used and could potentially use in their battle against harmful misinformation, as well as the limitations and challenges of taking algorithmic approaches to this thorny, multifaceted problem.

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Notes

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  2. 2.

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  3. 3.

    A separate study in the UK found that half of parents of small children had been exposed on social media to misinformation about vaccines: Sarah Boseley, “A report from January 2019 in the U.K. found that half of parents with small children had been exposed to misinformation about vaccines,” Guardian, January 24, 2019: https://www.theguardian.com/society/2019/jan/24/anti-vaxxers-spread-misinformation-on-social-media-report.

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  6. 6.

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  19. 19.

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  20. 20.

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  21. 21.

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  27. 27.

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  28. 28.

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  29. 29.

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  30. 30.

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  32. 32.

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  33. 33.

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  34. 34.

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  35. 35.

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  36. 36.

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  37. 37.

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  38. 38.

    Monika Bickert, “Enforcing Against Manipulated Media,” Facebook newsroom, January 6, 2020: https://about.fb.com/news/2020/01/enforcing-against-manipulated-media/.

  39. 39.

    Mike Schroepfer, “Community Standards report,” Facebook blog, November 13, 2019: https://ai.facebook.com/blog/community-standards-report/.

  40. 40.

    With Twitter, an analogous challenge is developing a realistic-looking network of followers and accounts followed, although organic Twitter networks seem more varied—and therefore easier to spoof—than organic Facebook networks, perhaps because Facebook friendships tend to reflect real-life relationships whereas Twitter relationships do not.

  41. 41.

    Teng Xu et al., “Deep Entity Classification: Abusive Account Detection for Online Social Networks,” Facebook research, November 11, 2020: https://research.fb.com/publications/deep-entity-classification-abusive-account-detection-for-online-social-networks/.

  42. 42.

    “Community Standards Enforcement Report,” Facebook, February 2021: https://transparency.facebook.com/community-standards-enforcement#fake-accounts.

  43. 43.

    James Vincent, “Facebook is now using AI to sort content for quicker moderation,” The Verge, November 13, 2020: https://www.theverge.com/2020/11/13/21562596/facebook-ai-moderation.

  44. 44.

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  45. 45.

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  46. 46.

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  47. 47.

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  50. 50.

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  56. 56.

    See Footnote 14.

  57. 57.

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  58. 58.

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  59. 59.

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  63. 63.

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  64. 64.

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  65. 65.

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  66. 66.

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  1. Acton, MA, USA

    Noah Giansiracusa

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Giansiracusa, N. (2021). Social Spread. In: How Algorithms Create and Prevent Fake News. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-7155-1_8

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