构建网络社交平台任务黑名单

Trang Ha, Quyen Hoang, Kyumin Lee
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引用次数: 0

摘要

最近,众包平台(例如Amazon Mechanical Turk)的使用因其灵活性和成本效益而蓬勃发展,这对请求者和工人都有利。然而,一些请求者通过创建恶意任务来滥用众包平台的权力,这些任务的目标是操纵搜索结果,留下虚假评论等。众包操纵降低了网络社交媒体的质量,威胁到整个网络空间的社会价值和安全。为了帮助解决这个问题,我们建立了一个分类模型,从几个流行的众包平台抓取的大量活动中过滤出恶意活动。然后,我们构建一个任务黑名单web服务,它为用户提供基于关键字的搜索,以便他们能够理解、调节和消除来自web的潜在恶意活动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building a Task Blacklist for Online Social Platforms
Recently, the use of crowdsourcing platforms (e.g., Amazon Mechanical Turk) has boomed because of their flexible and cost-effective nature, which benefits both requestors and workers. However, some requestors misused power of the crowdsourcing platforms by creating malicious tasks, which targeted manipulating search results, leaving fake reviews, etc. Crowdsourced manipulation reduces the quality of online social media, and threatens the social values and security of the cyberspace as a whole. To help solve this problem, we build a classification model which filters out malicious campaigns from a large number of campaigns crawled from several popular crowdsourcing platforms. We then build a task blacklist web service, which provides users with a keyword-based search so that they can understand, moderate and eliminate potential malicious campaigns from the Web.
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