Establishing Trust in Crowdsourced Data

TL;DR

This paper reviews trust management practices in crowdsourced data platforms, identifying strengths and limitations, and proposes AI-driven, transparent, and decentralized solutions to improve data reliability and community engagement.

Contribution

It systematically analyzes trust practices across diverse crowdsourcing platforms and introduces innovative solutions for enhancing trust and data quality.

Findings

Automated moderation and community validation are effective strengths.

Limitations include rapid data influx and opaque trust metrics.

Proposed solutions involve AI tools, transparent reputation systems, and decentralised moderation.

Abstract

Crowdsourced data supports real-time decision-making but faces challenges like misinformation, errors, and contributor power concentration. This study systematically examines trust management practices across platforms categorised as Volunteered Geographic Information, Wiki Ecosystems, Social Media, Mobile Crowdsensing, and Specialised Review and Environmental Crowdsourcing. Identified strengths include automated moderation and community validation, while limitations involve rapid data influx, niche oversight gaps, opaque trust metrics, and elite dominance. Proposed solutions incorporate advanced AI tools, transparent reputation metrics, decentralised moderation, structured community engagement, and a ``soft power'' strategy, aiming to equitably distribute decision-making authority and enhance overall data reliability.