ABSTRACT
Social data analytics have become a vital asset for organizations and governments. For example, over the last few years, governments started to extract knowledge and derive insights from vastly growing social data to personalize the advertisements in elections, improve government services, predict intelligence activities, as well as to enhance national security and public health. A key challenge in analyzing social data is to transform the raw data generated by social actors into curated data, i.e., contextualized data and knowledge that is maintained and made available for use by end-users and applications. The field of social data curation enables us to breathe meaning into raw data generated on social networks and transform it into contextualized knowledge for adequate consumption in social analytics and insight discovery. Data curation includes all the tasks needed for controlled data creation, maintenance, and management, together with the capacity to add value to data. This chapter gives an overview of essential tasks in social data curation pipelines, including identifying relevant data sources, ingesting data and knowledge, cleaning, integration, transformation, and adding value (e.g., extraction, enrichment, linking, and summarization).
