A Bayesian Framework for Reputation in Citizen Science

A Bayesian Framework for Reputation in Citizen Science

Proceedings of the Second Workshop on Data Science for Social Good co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Dicovery in Databases (ECML-PKDD 2017), 18 September 2017, Skopje, Macedonia.—18 pages, 6 figures, 8 tables

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Bibliographic Details
Main Authors: Garriga, Joan, Piera, Jaume, Bartumeus, Frederic
Other Authors: Ministerio de Economía y Competitividad (España)
Format: comunicación de congreso biblioteca
Language:English
Published: CEUR Workshop Proceedings 2017
Subjects:Reputation system, Bayesian networks, Citizen science,
Online Access:http://hdl.handle.net/10261/157884
http://dx.doi.org/10.13039/501100003329
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spelling dig-icm-es-10261-1578842021-01-21T13:31:04Z A Bayesian Framework for Reputation in Citizen Science Garriga, Joan Piera, Jaume Bartumeus, Frederic Ministerio de Economía y Competitividad (España) Fundación "la Caixa" Reputation system Bayesian networks Citizen science Proceedings of the Second Workshop on Data Science for Social Good co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Dicovery in Databases (ECML-PKDD 2017), 18 September 2017, Skopje, Macedonia.—18 pages, 6 figures, 8 tables The viability of any Citizen Science (CS) research program is absolutely conditioned to the engagement of the citizen. In a CS framework in which participants are expected to perform actions that can be later on validated, the incorporation of a reputation system can be a successful strategy to increase the overall data quality and the likelihood of engagement, and also to evaluate how close citizens fulfill the goals of the CS research program. Under the assumption that participant actions are validated using a simple discrete rating system, current reputation models, thoroughly applied in e-platform services, can be easily adapted to be used in CS frameworks. However, current reputation models implicitly assume that rated items and scored agents are the same entity, and this does not necessarily hold in a CS framework, where one may want to rate actions but score the participants generating it. We present a simple approach based on a Bayesian network representing the flow described above (user, action, validation), where participants are aggregated in a discrete set of user classes and we use the global evidence in the data base to estimate both the prior and the posterior distribution of the user classes. Afterwards, we evaluate the expertise of each participant by computing the user-class likelihood of the sequence of actions/validations observed for that user. As a proof of concept we implement our model in a real CS case, namely the Mosquito Alert project. This work is part of Mosquito Alert CS program research funded by the Spanish Ministry of Economy and Competitiveness (MINECO, Plan Estatal I+D+I CGL2013-43139-R) and la Caixa Banking Foundation. Mosquito Alert is currently promoted by la Caixa Banking Foundation. Peer reviewed 2017-11-30T09:37:05Z 2017-11-30T09:37:05Z 2017 comunicación de congreso http://purl.org/coar/resource_type/c_5794 CEUR Workshop Proceedings 1960: 1-18 (2017) 1613-0073 http://hdl.handle.net/10261/157884 http://dx.doi.org/10.13039/501100003329 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CGL2013-43139-R Publisher's version http://ceur-ws.org/Vol-1960/paper6.pdf Sí open CEUR Workshop Proceedings
institution ICM ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-icm-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del ICM España
language English
topic Reputation system
Bayesian networks
Citizen science
Reputation system
Bayesian networks
Citizen science
spellingShingle Reputation system
Bayesian networks
Citizen science
Reputation system
Bayesian networks
Citizen science
Garriga, Joan
Piera, Jaume
Bartumeus, Frederic
A Bayesian Framework for Reputation in Citizen Science
description Proceedings of the Second Workshop on Data Science for Social Good co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Dicovery in Databases (ECML-PKDD 2017), 18 September 2017, Skopje, Macedonia.—18 pages, 6 figures, 8 tables
author2 Ministerio de Economía y Competitividad (España)
author_facet Ministerio de Economía y Competitividad (España)
Garriga, Joan
Piera, Jaume
Bartumeus, Frederic
format comunicación de congreso
topic_facet Reputation system
Bayesian networks
Citizen science
author Garriga, Joan
Piera, Jaume
Bartumeus, Frederic
author_sort Garriga, Joan
title A Bayesian Framework for Reputation in Citizen Science
title_short A Bayesian Framework for Reputation in Citizen Science
title_full A Bayesian Framework for Reputation in Citizen Science
title_fullStr A Bayesian Framework for Reputation in Citizen Science
title_full_unstemmed A Bayesian Framework for Reputation in Citizen Science
title_sort bayesian framework for reputation in citizen science
publisher CEUR Workshop Proceedings
publishDate 2017
url http://hdl.handle.net/10261/157884
http://dx.doi.org/10.13039/501100003329
work_keys_str_mv AT garrigajoan abayesianframeworkforreputationincitizenscience
AT pierajaume abayesianframeworkforreputationincitizenscience
AT bartumeusfrederic abayesianframeworkforreputationincitizenscience
AT garrigajoan bayesianframeworkforreputationincitizenscience
AT pierajaume bayesianframeworkforreputationincitizenscience
AT bartumeusfrederic bayesianframeworkforreputationincitizenscience
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