Abstract
In recent years, the use of nontraditional data sources in statistical production has been increasing, given the additional need for more timely and disaggregated data. In the scope of nontraditional sources, citizen science represents an innovative approach to filling data gaps and including citizens as part of the recent innovation processes of national statistical offices (NSOs) in both the production of statistics and its role as data stewards of the national statistical systems (NSSs). The National Statistical Office of Colombia (DANE, acronym in Spanish) has structured a project within the framework of the Data4Now initiative for Sustainable Development Goal (SDG) indicators from SDG 16, using social networks as an alternative source of data generated by citizens, and natural language processing (NLP) to extract actionable intelligence from this data. In this paper, we describe the proposed strategy followed by DANE to estimate SDG indicators 16.b.1 and 16.7.2 as a pilot exercise in the framework of experimental statistics. Preliminary results illustrate potential use cases of unconventional information streams to analyze social phenomena through virtual environments such as online social media, and raise compelling challenges regarding representativity and quality assurance based on the statistical standards used by NSOs.