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.

____EAGER：SATC：核心：小：社交媒体上错觉主题的转变：伪装成人类的操纵者在社交媒体上的传播可能会对普通公众的健康，福祉和稳定造成重大影响。各种各样的主题很容易受到误解，从医学上的错误到政治错误，不同。传播错误信息的帐户可以广泛地分为两类：（1）那些无意间这样做的人（即相信自己传播的错误信息的个人）和（2）那些这样做的人，目的是故意欺骗性的（即，虚假信息的主体是“伪造的人”作为人类）。前一种类别中的人通常会在有限的主题（即医学或政治上，但不是两者）上传播错误信息，重点是他们作为个人所关心的内容。但是，恶意的第三方演员可能会激励虚假信息的代理人在不受限制的主题中传播错误信息，目的是引起公众的宽敞不稳定。该项目分析了在社交媒体上传播的错误信息，以区分第三方引起的虚假信息与其他更良性的帐户区分。要实现这一目标，团队将检查来自Twitter的数据，以识别迅速切换的帐户，在传播医疗信息传播中传播医疗信息以在2022年的上半年进行分散的范围，以从2022的上半年进行分类。一种分类工具，用于将跨越更广泛的Twittersphere（即2022年前和2022年后）标记为“虚假信息的潜在药物”。该团队还将描述2022年上半年的错觉差异的一部分归因于这样的第三方引起的代理商。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，以促进应对社交媒体上的虚假信息的努力。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估值得支持。