基于可持续众包的内容审核策略任务建议设计
On the Design of Strategic Task Recommendations for Sustainable Crowdsourcing-Based Content Moderation
摘要：
基于众包的内容审核是一个平台，它为众包工作者提供内容审核任务，以审查用户提交的内容（例如文本、图像和视频），并就发布内容的可接受性做出决定，以及一系列其他任务，例如图像标记和语音到文本转换。为了减少工人的认知负荷并提高系统效率，这些平台根据工人的偏好提供个性化的任务建议。然而，目前最先进的推荐系统忽视了对工作者心理健康的影响，特别是当他们反复暴露于具有极端内容（例如暴力图像、仇恨言论）的内容审核任务时。在本文中，我们提出了一种新的，战略推荐系统的众包平台，推荐工作的基础上工人的精神状态。具体而言，本文模型之间的互动众包平台的推荐系统（领导者）和工人（追随者）作为贝叶斯Stackelberg游戏的追随者的类型对应于工人的认知萎缩率和任务偏好。我们讨论了如何设计奖励和成本，以引导游戏在最大限度地提高平台的生产力方面达到预期的结果，同时改善人群工作人员的工作条件。
英文摘要：
Crowdsourcing-based content moderation is a platform that hosts content moderation tasks for crowd workers to review user submissions (e.g. text, images and videos) and make decisions regarding the admissibility of the posted content, along with a gamut of other tasks such as image labeling and speech-to-text conversion. In an attempt to reduce cognitive overload at the workers and improve system efficiency, these platforms offer personalized task recommendations according to the worker's preferences. However, the current state-of-the-art recommendation systems disregard the effects on worker's mental health, especially when they are repeatedly exposed to content moderation tasks with extreme content (e.g. violent images, hate-speech). In this paper, we propose a novel, strategic recommendation system for the crowdsourcing platform that recommends jobs based on worker's mental status. Specifically, this paper models interaction between the crowdsourcing platform's recommendation system (leader) and the worker (follower) as a Bayesian Stackelberg game where the type of the follower corresponds to the worker's cognitive atrophy rate and task preferences. We discuss how rewards and costs should be designed to steer the game towards desired outcomes in terms of maximizing the platform's productivity, while simultaneously improving the working conditions of crowd workers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
