Disinformation for Hire

The replication material includes the do files and datasets to replicate the results in tables, figures and text of the main manuscript and the appendix. The code constructs the results from the field data and additional experiments we ran on Prolific and MTurk. The material contains 4 code files, all ending with “.do”. The code was last run using Stata (version 18.0) on MacOS. The replicator should expect the code to run under 5 minutes on a standard (2024) desktop machine.<b>Background</b> The spread of misinformation has been linked to increased social divisions and adverse health outcomes, but less is known about the production of disinformation, which is misinformation intended to mislead.<b>Method</b> The main data used in this paper has been collected by the authors using the Mturk interface (Field Experiment) or Qualtrics (Manipulation Check, Downstream Consequences, and Platform Interventions). It is available in the replication package. Our survey design and selection eligibility are included in the Supplementary Document in this depository.<b>Results</b> In a field experiment on MTurk (N=1,197), we found that while 70% of workers accepted a control job, 61% accepted a disinformation job requiring them to manipulate COVID-19 data. To quantify the trade-off between ethical and financial considerations in job acceptance, we introduced a lower-pay condition offering half the wage of the control job; 51% of workers accepted this job, suggesting that the ethical compromise in the disinformation task reduced the acceptance rate by about the same amount as a 25% wage reduction.A survey experiment with a nationally representative sample shows that viewing a disinformation graph from the field experiment negatively affected people’s beliefs and behavioral intentions related to the COVID-19 pandemic, including increased vaccine hesitancy.<b>Conclusion</b> Using a “wisdom-of-crowds” approach, we highlight how online labor markets can introduce features, such as increased worker accountability, to reduce the likelihood of workers engaging in the production of disinformation. Our findings emphasize the importance of addressing the supply side of disinformation in online labor markets to mitigate its harmful societal effects.

本复现材料包含用于复现正文与附录中表格、插图及文本结果的do文件（do file）与数据集。本代码基于实地实验数据以及我们在Prolific与MTurk（亚马逊土耳其机器人，Mechanical Turk）平台上开展的额外实验生成全部结果。本材料共包含4个代码文件，均以".do"为后缀。本代码最后一次运行是在MacOS系统下，使用Stata 18.0版本完成。复现者在2024年款标准台式机上运行该代码，预计耗时不超过5分钟。

<b>研究背景</b>：虚假信息（misinformation）的传播已被证实与社会分化加剧及不良健康后果存在关联，但针对蓄意虚假信息（disinformation，即旨在误导他人的虚假信息）的生产环节，当前学界的相关研究仍较为匮乏。

<b>研究方法</b>：本文所用核心数据由作者通过MTurk（亚马逊土耳其机器人，Mechanical Turk）平台界面（用于实地实验）或Qualtrics平台（用于操纵检验、下游效应研究与平台干预实验）收集，可在本复现包中获取。本研究的问卷设计与招募筛选标准已收录于本存档库的补充材料中。

<b>研究结果</b>：在MTurk平台开展的实地实验中（样本量N=1197），我们发现：70%的参与者接受了对照任务，而61%的参与者接受了要求篡改新冠（COVID-19）数据的蓄意虚假信息制作任务。为量化求职决策中伦理考量与经济收益间的权衡关系，我们设置了薪资为对照任务一半的低薪组别，结果有51%的参与者接受了该任务。这表明，参与蓄意虚假信息制作任务所需的伦理妥协，其对任务接受率的负面影响约等同于25%的薪资降幅。针对全国代表性样本开展的调查实验显示，观看本实地实验中的蓄意虚假信息图表后，受访者对新冠（COVID-19）疫情相关的认知与行为意向均受到负面影响，其中包括疫苗犹豫（vaccine hesitancy）程度上升。

<b>研究结论</b>：本研究采用“群体智慧（wisdom-of-crowds）”范式，阐明了在线零工市场可通过增设工人问责机制等功能，降低参与者参与蓄意虚假信息制作的可能性。本研究结果凸显了针对在线零工市场蓄意虚假信息供给端进行治理的重要性，以缓解其对社会造成的有害影响。