Identifying Malicious Players in GWAP-based Disaster Monitoring Crowdsourcing System

TL;DR

This paper presents a large-scale GWAP-based disaster monitoring system that detects malicious users and assesses disaster levels using satellite imagery and graph centrality algorithms.

Contribution

It introduces a novel crowdsourcing system for disaster monitoring that incorporates a directed graph centrality algorithm for malicious user detection and disaster level assessment.

Findings

Effective detection of malicious players using graph centrality.

System successfully aggregates satellite image tags for disaster assessment.

Algorithm applicable to other human computation platforms.

Abstract

Disaster monitoring is challenging due to the lake of infrastructures in monitoring areas. Based on the theory of Game-With-A-Purpose (GWAP), this paper contributes to a novel large-scale crowdsourcing disaster monitoring system. The system analyzes tagged satellite pictures from anonymous players, and then reports aggregated and evaluated monitoring results to its stakeholders. An algorithm based on directed graph centralities is presented to address the core issues of malicious user detection and disaster level calculation. Our method can be easily applied in other human computation systems. In the end, some issues with possible solutions are discussed for our future work.