Definition and analysis of strategic predictive indicators for social networks

Abstract

This thesis delves into a novel research area in Social Business Intelligence (SBI) focusing on social indicators. It proposes advanced methodologies for discovering and describing social indicators, using social network metrics and dynamic contexts. The main contribution is the proposal of a comprehensive methodological framework for the development of SBI projects, addressing the necessity for a semantic infrastructure. The framework abstracts various methodological processes that can be adapted and extended to suit diverse analysis tasks. The implementation of an author profiling method based on multidimensional business perspectives allows the identification of business roles within user profiles on social media. This finding introduces a novel quality indicator for assessing content reliability. Finally, the feasibility of developing quality indicators semi-automatically to identify relevant content is demonstrated. In summary, the proposed infrastructure enables the extraction of valuable insights for organizations seeking to measure the impact of their actions in social media.