Detecting Suspicious Events in Fast Information Flows

TL;DR

HALFADO is a lightweight, semi-supervised algorithm designed to detect suspicious events in high-frequency data streams, effectively aiding human experts in social media hate speech detection and financial fraud monitoring.

Contribution

The paper introduces HALFADO, a novel semi-supervised detection algorithm that combines classical learning principles with computational efficiency for real-time suspicious event detection.

Findings

Effective in detecting hate speech in social media streams

Successfully identifies fraudulent transactions in FinTech applications

Operates with minimal computational resources and annotation effort

Abstract

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.