Gateway to Think Tanks
来源类型 | Report |
规范类型 | 报告 |
DOI | https://doi.org/10.7249/RR-A676-1 |
来源ID | RR-A676-1 |
Detecting Conspiracy Theories on Social Media: Improving Machine Learning to Detect and Understand Online Conspiracy Theories | |
William Marcellino; Todd C. Helmus; Joshua Kerrigan; Hilary Reininger; Rouslan I. Karimov; Rebecca Ann Lawrence | |
发表日期 | 2021-04-29 |
出版年 | 2021 |
语种 | 英语 |
结论 |
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摘要 | Conspiracy theories circulated online via social media contribute to a shift in public discourse away from facts and analysis and can contribute to direct public harm. Social media platforms face a difficult technical and policy challenge in trying to mitigate harm from online conspiracy theory language. As part of Google's Jigsaw unit's effort to confront emerging threats and incubate new technology to help create a safer world, RAND researchers conducted a modeling effort to improve machine-learning (ML) technology for detecting conspiracy theory language. They developed a hybrid model using linguistic and rhetorical theory to boost performance. They also aimed to synthesize existing research on conspiracy theories using new insight from this improved modeling effort. This report describes the results of that effort and offers recommendations to counter the effects of conspiracy theories that are spread online. |
目录 |
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主题 | Information Operations ; The Internet ; Machine Learning ; Social Media Analysis |
URL | https://www.rand.org/pubs/research_reports/RRA676-1.html |
来源智库 | RAND Corporation (United States) |
引用统计 | |
资源类型 | 智库出版物 |
条目标识符 | http://119.78.100.153/handle/2XGU8XDN/524433 |
推荐引用方式 GB/T 7714 | William Marcellino,Todd C. Helmus,Joshua Kerrigan,et al. Detecting Conspiracy Theories on Social Media: Improving Machine Learning to Detect and Understand Online Conspiracy Theories. 2021. |
条目包含的文件 | ||||||
文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
RAND_RRA676-1.pdf(968KB) | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
1640181477042.jpg(6KB) | 智库出版物 | 限制开放 | CC BY-NC-SA | ![]() 浏览 |
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