Establishing Trust in Crowdsourced Data

TL;DR

This paper systematically surveys trust management across crowdsourced data platforms, spanning VGI, wiki ecosystems, social media, mobile crowd sensing, specialised environmental crowdsourcing, and review crowdsourcing. It combines a state-of-practice analysis with a PRISMA-guided literature review to identify strengths, limitations, and emerging solutions, highlighting automated moderation, community oversight, and transparent reputation mechanisms as core assets, while noting challenges such as elite dominance, data influx, and opaque metrics. The work outlines concrete opportunities—ranging from deep learning–based trust assessments and AI-assisted trust indicators to decentralised governance and privacy-preserving verification—and proposes a soft, adaptive strategy for continuous trust improvement. By integrating technical innovations with participatory governance, the paper argues for scalable, transparent, and equitable trust management that can enhance reliability and decision usefulness of crowdsourced data in diverse application domains.

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.