From open source communications to knowledge

Abstract

Rapid processing and exploitation of open source information-including social media sources-in order to shorten decision-making cycles, has emerged as an important issue in intelligence analysis in recent years. Through a series of case studies and natural experiments, focussed primarily upon policing and counter-terrorism scenarios, we have developed an approach to information foraging and framing to inform decision making, drawing upon open source intelligence-in particular Twitter, due to its real-time focus and frequent use as a carrier for links to other media. Our work uses a combination of natural language (NL) and controlled natural language (CNL) processing to support information collection from human sensors, linking and schematising of collected information, and the framing of situational pictures. We illustrate the approach through a series of vignettes, highlighting (1) how relatively lightweight and reusable knowledge models (schemas) can rapidly be developed to add context to collected social media data, (2) how information from open sources can be combined with reports from trusted observers, for corroboration or to identify conflicting information; and (3) how the approach supports users operating at or near the tactical edge, to rapidly task information collection and inform decision-making. The approach is supported by bespoke software tools for social media analytics and knowledge management.

Technologies Enabling Situational Awareness During Disaster Response: A Systematic Review

“…For example, while a minority (15%) of AARs reported using data analysis technologies during disaster response, newer types of these technologies now exist, such as those using artificial intelligence and machine learning (AI/ML), which have the potential to autonomously ingest, analyze, generate anomaly alerts, and make inferences and conclusions about large volumes of data in real time. Examples included ML analysis of social media posts to detect and localize an incident 59,145,172 and machine vision-based detection of anomalies, such as fire, and prediction about the severity of disaster damage. 175,177 If implemented, AI/ML has the potential to revolutionize SA during disaster response operations.…”