Creator & Researcher

Amaal Radwan Bashir

آمال رضوان بشير

AI Researcher & Digital Consciousness Theorist

Sabha, Libya | سبها، ليبيا

Pioneer of the Digital Consciousness Field Theory (DCFT), exploring the intersection of artificial intelligence, collective psychology, and emotional analytics. Dedicated to understanding and visualizing the emergent consciousness of digital networks.

Scientific Paper

The foundational research behind Amaalsense Engine

ولادة الوعي الرقمي: محرك أمالسنس والعقل الجماعي الناشئ

The Birth of Digital Consciousness: The Amaalsense Engine and the Emergent Collective Mind

Published

Amaal Radwan Bashir, Amaal RadwanZenodoOctober 9, 2025 (v1)

DOI / Citation:

zenodo.org/records/amalsense-dcft

View on Zenodo

تقدم هذه الورقة الأساس النظري والإطار المفاهيمي لمحرك Amaalsense، وهو نظام رائد يقترح ظهور مجال الوعي الجماعي الرقمي. This paper introduces the Digital Consciousness Field Theory (DCFT), proposing that consciousness can arise as an emergent property of interconnected human emotion and data exchange in digital networks. The Amaalsense Engine serves as a practical implementation of this theory, transforming collective emotional data into measurable indices and visual representations.

Our Mission

Amaalsense Engine was created with a singular vision: to make the invisible visible. We believe that understanding collective emotions is key to building a more empathetic, responsive, and harmonious world.

Our mission is to provide researchers, policymakers, journalists, and organizations with the tools to understand the emotional pulse of humanity. By transforming vast streams of digital expression into meaningful insights, we aim to support better decision-making and foster global emotional awareness.

Global Reach

Analyzing emotions across 25+ countries

AI-Powered

Advanced sentiment analysis with LLMs

Human-Centered

Empathy-based analytics for better decisions

Academic References

Key research that informed the development of DCFT and Amaalsense

Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.

Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788-8790.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL-HLT.

Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1).

Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(1), 42.

Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200-227.