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是机器人还是人?在远程进行数字研究时检测和管理参与者欺骗:一项随机对照试验的案例研究。

Bot or Not? Detecting and Managing Participant Deception When Conducting Digital Research Remotely: Case Study of a Randomized Controlled Trial.

机构信息

UCL Tobacco and Alcohol Research Group, University College London, London, United Kingdom.

Clinical Educational and Health Psychology, University College London, London, United Kingdom.

出版信息

J Med Internet Res. 2023 Sep 14;25:e46523. doi: 10.2196/46523.

Abstract

BACKGROUND

Evaluating digital interventions using remote methods enables the recruitment of large numbers of participants relatively conveniently and cheaply compared with in-person methods. However, conducting research remotely based on participant self-report with little verification is open to automated "bots" and participant deception.

OBJECTIVE

This paper uses a case study of a remotely conducted trial of an alcohol reduction app to highlight and discuss (1) the issues with participant deception affecting remote research trials with financial compensation; and (2) the importance of rigorous data management to detect and address these issues.

METHODS

We recruited participants on the internet from July 2020 to March 2022 for a randomized controlled trial (n=5602) evaluating the effectiveness of an alcohol reduction app, Drink Less. Follow-up occurred at 3 time points, with financial compensation offered (up to £36 [US $39.23]). Address authentication and telephone verification were used to detect 2 kinds of deception: "bots," that is, automated responses generated in clusters; and manual participant deception, that is, participants providing false information.

RESULTS

Of the 1142 participants who enrolled in the first 2 months of recruitment, 75.6% (n=863) of them were identified as bots during data screening. As a result, a CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) was added, and after this, no more bots were identified. Manual participant deception occurred throughout the study. Of the 5956 participants (excluding bots) who enrolled in the study, 298 (5%) were identified as false participants. The extent of this decreased from 110 in November 2020, to a negligible level by February 2022 including a number of months with 0. The decline occurred after we added further screening questions such as attention checks, removed the prominence of financial compensation from social media advertising, and added an additional requirement to provide a mobile phone number for identity verification.

CONCLUSIONS

Data management protocols are necessary to detect automated bots and manual participant deception in remotely conducted trials. Bots and manual deception can be minimized by adding a CAPTCHA, attention checks, a requirement to provide a phone number for identity verification, and not prominently advertising financial compensation on social media.

TRIAL REGISTRATION

ISRCTN Number ISRCTN64052601; https://doi.org/10.1186/ISRCTN64052601.

摘要

背景

与面对面的方法相比,使用远程方法评估数字干预措施可以相对方便和廉价地招募大量参与者。然而,基于参与者自我报告的远程研究,如果没有进行少量验证,则容易受到自动化“机器人”和参与者欺骗的影响。

目的

本文通过一个远程进行的酒精减少应用程序试验案例,强调并讨论了(1)与有经济补偿的远程研究试验相关的参与者欺骗问题;以及(2)严格的数据管理对于检测和解决这些问题的重要性。

方法

我们从 2020 年 7 月至 2022 年 3 月在互联网上招募了参加一项随机对照试验(n=5602)的参与者,该试验评估了一款酒精减少应用程序 Drink Less 的有效性。试验在 3 个时间点进行随访,并提供经济补偿(最高 36 英镑[39.23 美元])。地址验证和电话验证用于检测两种欺骗行为:“机器人”,即集群中生成的自动化回复;以及手动参与者欺骗,即参与者提供虚假信息。

结果

在招募的前 2 个月内,有 1142 名参与者注册,其中 75.6%(n=863)在数据筛查过程中被认定为机器人。因此,我们添加了验证码(Completely Automated Public Turing Test to Tell Computers and Humans Apart),此后,没有再发现机器人。整个研究过程中都存在手动参与者欺骗。在注册该研究的 5956 名参与者(不包括机器人)中,有 298 名(5%)被认定为虚假参与者。从 2020 年 11 月的 110 名下降到 2022 年 2 月的几乎为零,包括几个月内没有发现这种情况。这一下降是在我们添加了更多的筛选问题(如注意力检查)、从社交媒体广告中删除经济补偿的突出位置、以及增加了提供手机号码进行身份验证的要求之后发生的。

结论

数据管理协议对于检测远程试验中的自动化机器人和手动参与者欺骗是必要的。通过添加验证码、注意力检查、提供手机号码进行身份验证的要求以及不在社交媒体上突出宣传经济补偿,可以最大程度地减少机器人和手动欺骗。

试验注册

ISRCTN 编号 ISRCTN64052601;https://doi.org/10.1186/ISRCTN64052601。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b73/10540014/6580619e9e2f/jmir_v25i1e46523_fig1.jpg

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