# Une mesure d'expertise pour le crowdsourcing

## TL;DR

This paper introduces a new method for measuring expertise in crowdsourcing tasks using belief function theory and graph-based data structuring, specifically when gold standard data is available.

## Contribution

It proposes a novel expertise measurement approach that leverages belief functions and graph structures, applicable when gold data exists in crowdsourcing.

## Key findings

- Method effectively assesses expertise with gold data
- Graph-based data structuring improves evaluation accuracy
- Applicable to small, fast, non-automatable tasks

## Abstract

Crowdsourcing, a major economic issue, is the fact that the firm outsources internal task to the crowd. It is a form of digital subcontracting for the general public. The evaluation of the participants work quality is a major issue in crowdsourcing. Indeed, contributions must be controlled to ensure the effectiveness and relevance of the campaign. We are particularly interested in small, fast and not automatable tasks. Several methods have been proposed to solve this problem, but they are applicable when the "golden truth" is not always known. This work has the particularity to propose a method for calculating the degree of expertise in the presence of gold data in crowdsourcing. This method is based on the belief function theory and proposes a structuring of data using graphs. The proposed approach will be assessed and applied to the data.