EAGER: SaTC: Shifts in Misinformation Topics on Social Media: Manipulators Masquerading as Humans

EAGER：SaTC：社交媒体上错误信息主题的转变：伪装成人类的操纵者

• 批准号: 2230083
• 负责人: Helen Piontkivska
• 金额: $ 19.98万
• 依托单位: Kent State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230083&HistoricalAwards=false
• 关键词: EAGER; SaTC; Shifts; Misinformation; Topics

__________________________________________________________________________________________________________________________________EAGER: SaTC: CORE: Small: Shifts in misinformation topics on social media: manipulators masquerading as humansThe spread of misinformation on social media can result in major consequences to the health, wellbeing, and stability of the general public. A wide range of topics are vulnerable to misinformation, varying from medical misinformation to political misinformation. Accounts that spread misinformation can be broadly classified into two categories: (1) those who do so unintentionally (i.e., individuals who believe in the misinformation that they spread) and (2) those who do so with the aim of being deliberately deceptive (i.e., agents of disinformation “masquerading” as humans). Those in the former category typically spread misinformation on a constrained number of topics (i.e., either medical or political, but not both), focusing on what they care about as individuals. However, agents of disinformation may be incentivized by malicious third-party actors to spread misinformation across an unconstrained variety of topics, with the objective of prompting widespread instability among the general public. This project analyzes misinformation spread on social media to distinguish third-party-incentivized agents of disinformation from other, more benign accounts.To achieve this goal, the team will examine data from Twitter to identify accounts that switched rapidly between spreading medical misinformation to spreading political misinformation during the first half of 2022. A machine learning framework will be designed to learn from linguistic features that are unique to this subset of accounts, which will then be used to develop a classification tool to label accounts across the broader Twittersphere (i.e., pre-2022 and post-2022) as “potential agents of disinformation”. The team will also characterize what fraction of misinformation spread during the first half of 2022 was attributable to such third-party-incentivized agents. All algorithms developed over the course of the project will be shared openly with the broader scientific community to facilitate efforts towards countering disinformation on social media.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

________________________________________________________________________________________________________________________________ 渴望：SaTC：核心：小：社交媒体上错误信息主题的转变：伪装成人类的操纵者社交媒体上错误信息的传播可能会对公众的健康、福祉和稳定造成重大影响。容易受到错误信息的影响，从医疗错误信息到政治错误信息，传播错误信息的帐户大致可分为两类：(1) 无意传播的帐户。 （即相信自己传播的错误信息的个人）和（2）出于故意欺骗的目的而这样做的人（即“伪装”为人类的虚假信息代理人）。前一类的人通常会传播错误信息。主题数量有限（即医学或政治，但不能两者兼而有之），重点关注他们作为个人所关心的内容。但是，恶意第三方行为者可能会激励虚假信息传播者在各处传播错误信息。该项目结合了社交媒体上传播的错误信息，以将第三方激励的虚假信息代理人与其他更良性的账户区分开来。将检查 Twitter 的数据，以确定 2022 年上半年在传播医疗错误信息和传播政治错误信息之间快速切换的帐户。将设计一个机器学习框架来学习该帐户子集特有的语言特征，然后将其被使用开发一种分类工具，将更广泛的 Twitter 领域（即 2022 年之前和 2022 年之后）的账户标记为“潜在的虚假信息传播者”。该团队还将描述 2022 年上半年传播的错误信息中哪些部分是由哪些因素造成的。此类第三方激励代理。在该项目过程中开发的所有算法都将与更广泛的科学界公开共享，以促进打击社交媒体上的虚假信息的努力。该奖项由 NSF 授予。法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。