Abstract
The main purpose of Social Business Intelligence is to help companies in making decisions by performing multidimensional analysis of the relevant information disseminated on social networks. Although data quality is a general issue in SBI, few approaches have aimed at assessing it for any data collection, being this a context dependent task. In this paper, we define a quality indicator as a metric that serves to assess the overall quality of a collection, and that integrates the measures obtained by several quality criteria applied to filter the posts relevant for a SBI project. The selection of the best quality criteria to include in each quality indicator is a complex task that requires a deep understanding of both the context and objectives of analysis. In this paper, we propose a new methodology to design quality indicators for SBI projects whose quality criteria consider contents coherence and data provenance. Thus, for the context defined by an objective of analysis, this methodology helps users to find the quality criteria that best suit both the users and the available data, and then integrate them into a valid quality indicator.
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