Detecting terrorist influencers using reciprocal human-machine learning: The case of militant Jihadist Da’wa on the Darknet

Dafna Lewinsky*, Dov Te’eni*, Inbal Yahav-Shenberger*, David G. Schwartz*, Gahl Silverman*, Yossi Mann*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Over the past decade, social media has significantly impacted terrorism and counterterrorism, serving as a platform for incitement to violence under the guise of religious preaching. This study explores the critical role of preachers preaching righteous behavior, a process known as Da’wa in Islam. Focusing on Militant Jihadist Da’wa calling to violence, the research analyzes 6000 posts from Darknet forums associated with Jihadist groups from 2017 to 2018. The study improves the detection and understanding of militant Jihadist preaching by using an advanced method called Reciprocal Human-Machine Learning. The study demonstrates the feasibility of better detection and a deeper understanding of influencing terrorists.

Original languageEnglish
Article number1442
JournalHumanities and Social Sciences Communications
Volume11
Issue number1
DOIs
StatePublished - Dec 2024

Funding

FundersFunder number
Tel Aviv University
Ministry of Innovation, Science and Technology207076

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