快速信息流中的可疑事件检测
Detecting Suspicious Events in Fast Information Flows
摘要：
我们描述了一个计算羽毛轻，直观，但可证明有效的算法，名为HALFADO。HALFADO的设计目的是根据相对较少的人类判断实例，在复杂条目的高频流中检测可疑事件。操作足够精确的检测系统对于现代数字社会许多不同领域的{ \em assisting}人类专家团队至关重要。这些系统本身具有深远的规范作用，公众了解这种技术的运作方式应是一项人权。在概念层面上，本方法扩展了一个最经典的学习算法的分类，继承其理论属性。然而，它以半监督的方式工作，将人类和计算智能结合在一起。在实践层面上，该算法超越了现有的方法（专家系统），通过管理和提高其性能到一个单一的全球检测器。我们说明了HALFADO在两个具有挑战性的应用中的功效：（1）用于检测从社交媒体平台收集的文本消息流中的{ \em仇恨言论}消息，以及（2）用于金融科技中的交易监控系统（TMS）检测金融交易流中的欺诈交易。该算法表明，与流行的观点相反，机器学习的高级方法既不需要高级的计算能力，也不需要昂贵的注释工作。
英文摘要：
We describe a computational feather-light and intuitive, yet provably efficient algorithm, named HALFADO. HALFADO is designed for detecting suspicious events in a high-frequency stream of complex entries, based on a relatively small number of examples of human judgement. Operating a sufficiently accurate detection system is vital for {\em assisting} teams of human experts in many different areas of the modern digital society. These systems have intrinsically a far-reaching normative effect, and public knowledge of the workings of such technology should be a human right. On a conceptual level, the present approach extends one of the most classical learning algorithms for classification, inheriting its theoretical properties. It however works in a semi-supervised way integrating human and computational intelligence. On a practical level, this algorithm transcends existing approaches (expert systems) by managing and boosting their performance into a single global detector. We illustrate HALFADO's efficacy on two challenging applications: (1) for detecting {\em hate speech} messages in a flow of text messages gathered from a social media platform, and (2) for a Transaction Monitoring System (TMS) in FinTech detecting fraudulent transactions in a stream of financial transactions. This algorithm illustrates that - contrary to popular belief - advanced methods of machine learning need not require neither advanced levels of computation power nor expensive annotation efforts.
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