Handbook on the Use of the Amsterdam Matrix
FOR THE EVALUATION OF THE TRUSTWORTHINESS OF OSINT ON SOCIAL MEDIA PLATFORMS
Authors
Johanna Hiebl, Janthe Van Schaik, Fernando Tabárez Rienzi, Guillen Torres, Deniz Dirisu
Designer
Tommaso Prinetti
Within the last few years, Open Source Intelligence (OSINT) has emerged as a transformative tool for information gathering. There has been a dramatic increase in user-generated content on social media, including material explicitly intended to analyze and clarify current events.
However, the changing dynamics of social media, with access to Open Source Information (OSINF), introduce challenges in assessing the trustworthiness of publicly shared content, challenging its usefulness and role within investigations. The handbook on the use of the Amsterdam Matrix for the evaluation of OSINT on social media provides a comprehensive framework for systematically evaluating the reliability of OSINT-labeled posts published on Twitter/X.
Central to this handbook is the Amsterdam Matrix, a tool designed to assess tweet trustworthiness, which a group of scholars and OSINT practitioners and students developed through iterative coding. The project group qualitatively identified 23 parameters categorized by information type, argumentative qualities, textual communication style, and OSINT linguistic traits. Through the development parameters, the goal of the manual is to develop an approach in assessing trustworthiness in the field of OSINT.
The Matrix’s application on Twitter/X demonstrates the potential to address challenges faced by practitioners, academics, and journalists to verify OSINT content. However, it’s framework can potentially be applied to any other digital platform where OSINT is communicated to open audiences.
Developed during the Digital Methods Winter School in 2024, the Matrix and handbook highlight that evaluating OSINT content trustworthiness depends fundamentally on evidence-based criteria rather than stylistic elements.
The Matrix was further developed into the handbook by OSINT For Ukraine (OFU), highlighting that the methodology could likely serve as a foundation for developing AI-driven tools to automate reliability evaluations, enhancing the speed of the OSINT verification and avoiding online misinformation.
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