The efficacy of Facebook's vaccine misinformation policies and architecture during the COVID-19 pandemic

被引:19
作者
Broniatowski, David A. [1 ,2 ]
Simons, Joseph R. [3 ]
Gu, Jiayan [4 ]
Jamison, Amelia M. [5 ]
Abroms, Lorien C. [2 ,4 ]
机构
[1] George Washington Univ, Dept Engn Management & Syst Engn, Washington, DC 20052 USA
[2] George Washington Univ, Inst Data Democracy & Polit, Washington, DC 20052 USA
[3] United States Dept Hlth & Human Serv, Off Assistant Secretary Financial Resources, Washington, DC 20543 USA
[4] George Washington Univ, Dept Prevent & Community Hlth, Washington, DC 20052 USA
[5] Johns Hopkins Univ, Dept Hlth Behav & Soc, Baltimore, MD 21218 USA
基金
美国国家科学基金会;
关键词
FLEXIBILITY; DISCOVERY; NEWS;
D O I
10.1126/sciadv.adh2132
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Online misinformation promotes distrust in science, undermines public health, and may drive civil unrest. During the coronavirus disease 2019 pandemic, Facebook-the world's largest social media company-began to remove vaccine misinformation as a matter of policy. We evaluated the efficacy of these policies using a comparative interrupted time-series design. We found that Facebook removed some antivaccine content, but we did not observe decreases in overall engagement with antivaccine content. Provaccine content was also removed, and antivaccine content became more misinformative, more politically polarized, and more likely to be seen in users' newsfeeds. We explain these findings as a consequence of Facebook's system architecture, which provides substantial flexibility to motivated users who wish to disseminate misinformation through multiple channels. Facebook's architecture may therefore afford antivaccine content producers several means to circumvent the intent of misinformation removal policies.
引用
收藏
页数:17
相关论文
共 105 条
  • [61] A postmodern Pandora's box: Anti-vaccination misinformation on the Internet
    Kata, Anna
    [J]. VACCINE, 2010, 28 (07) : 1709 - 1716
  • [62] Computer-Assisted Keyword and Document Set Discovery from Unstructured Text
    King, Gary
    Lam, Patrick
    Roberts, Margaret E.
    [J]. AMERICAN JOURNAL OF POLITICAL SCIENCE, 2017, 61 (04) : 971 - 988
  • [63] Kurkowski J., 2020, GitHub
  • [64] The science of fake news
    Lazer, David M. J.
    Baum, Matthew A.
    Benkler, Yochai
    Berinsky, Adam J.
    Greenhill, Kelly M.
    Menczer, Filippo
    Metzger, Miriam J.
    Nyhan, Brendan
    Pennycook, Gordon
    Rothschild, David
    Schudson, Michael
    Sloman, Steven A.
    Sunstein, Cass R.
    Thorson, Emily A.
    Watts, Duncan J.
    Zittrain, Jonathan L.
    [J]. SCIENCE, 2018, 359 (6380) : 1094 - 1096
  • [65] Loomba S, 2021, NAT HUM BEHAV, V5, P337, DOI 10.1038/s41562-021-01056-1
  • [66] McCallum AK, 2002, Mallet: A machine learning for language toolkit
  • [67] Merrill J., 2021, The Washington Post
  • [68] Mimno David., 2014, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing EMNLP, P1319, DOI DOI 10.3115/V1/D14-1138
  • [69] Removal of Anti-Vaccine Content Impacts Social Media Discourse
    Mitts, Tamar
    Pisharody, Nilima
    Shapiro, Jacob N.
    [J]. PROCEEDINGS OF THE 14TH ACM WEB SCIENCE CONFERENCE, WEBSCI 2022, 2022, : 319 - 326
  • [70] Flexibility and Its Relation to Complexity and Architecture
    Moses, Joel
    [J]. COMPLEX SYSTEMS DESIGN AND MANAGEMENT, 2010, : 197 - 206