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Expert Finding in Citizen Science Platform for Biodiversity Monitoring via Weighted PageRank Algorithm

机译：通过加权PageRank算法在公民科学平台中进行生物多样性监测的专家发现

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摘要

Numerous citizen science platforms aiming at monitoring biodiversity have emerged in the recent years. These platforms collect biodiversity data from participants and allow them to increase their scientific knowledge and share it with other participants, experts and scientists. One key aspect of such platforms is quality control on the data, a task usually performed by a limited number of co-opted experts. With the amount of data collected increasing steeply, finding new experts is needed. In this paper we propose a new graph-based expert finding approach for the citizen science platform SPIPOLL, aiming at collecting data on pollinator diversity across France. We exploit both users comments quality and users social relations to calculate users expertise for specific insect family. Experimental results show that the proposed method performs better than the state-of-the-art expert finding algorithms.
机译：近年来，已经出现了许多旨在监测生物多样性的公民科学平台。这些平台从参与者那里收集生物多样性数据，并允许他们增加其科学知识并与其他参与者，专家和科学家共享。此类平台的一个关键方面是对数据的质量控制，这通常是由有限数量的精选专家执行的任务。随着收集的数据量急剧增加，需要寻找新的专家。在本文中，我们为市民科学平台SPIPOLL提出了一种新的基于图的专家发现方法，旨在收集整个法国传粉媒介多样性的数据。我们利用用户的评论质量和用户的社会关系来计算特定昆虫家族的用户专业知识。实验结果表明，该方法的性能优于最新的专家发现算法。

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