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

__________________________________________________________________________________________________________________________________ 渴望:一部SaTC:里核心:小:社交媒体误传话题的转变:操纵者伪装成humansThe错误信息在社交媒体上的传播健康会导致严重的后果,幸福,和一般公众的稳定。广泛的主题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。传播错误信息的账户可以大致分为两类：(1)无意中这样做的账户（即，相信他们传播的错误信息的个人）和(2)那些以故意欺骗为目的的账户（即，虚假信息的代理人“伪装”成人类）。前一类人通常在有限数量的主题上传播错误信息（即，要么是医疗，要么是政治，但不是两者都有），专注于他们作为个人关心的事情。然而，虚假信息的代理人可能会受到恶意第三方行为者的激励，在不受约束的各种主题中传播虚假信息，其目的是在普通公众中引发广泛的不稳定。该项目分析了社交媒体上传播的错误信息，以区分第三方激励的虚假信息代理人和其他更良性的账户。为了实现这一目标，该团队将检查推特上的数据，以确定在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迅速切换的账户。将设计一个机器学习框架，从这部分账户的独特语言特征中学习，然后将其用于开发一个分类工具，将更广泛的twitter圈（即2022年前和2022年后）的账户标记为“潜在的虚假信息代理人”。该团队还将描述2022年上半年传播的错误信息中有多少可归因于此类第三方激励代理商。在项目过程中开发的所有算法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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

__________________________________________________________________________________________________________________________________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.