Understanding Bias in Twitter-Based Intelligence Analysis

Twitter has been lately engaged by the community of intelligence services worldwide that for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the Intelligence Community (IC) has identified bias as the major obstacle in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. Both communities share the interest to accurately trace bias and ideally eradicate or moderate its effects. In this paper we introduce systemic parallels between Intelligence Analysis (IA) and Twitter Analytics (TA), to highlight similarities/dissimilarities, and investigate the feasibility of adapting and adjusting methodologies from the first field to the latter. Furthermore, we briefly introduce a novel framework for AI-augmented bias mitigation in the IC, proposing methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation methodologies adapted by the IC.