Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions

Abstract

Terror attacks have been linked in part to online extremist content. Online conversations are cloaked in religious ambiguity, with deceptive intentions, often twisted from mainstream meaning to serve a malevolent ideology. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Radicalization is a subtle long-running persuasive process that occurs over time. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. Based on prior empirical and qualitative research in social sciences, particularly political science, we model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. The significant sensitivity of the Islamist extremist ideology and its local and global security implications require reliable algorithms for modelling such communications on Twitter. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate, which reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our context-aware approach outperforms a competitive baseline by 10.2% in precision, thereby enhancing the potential of such tools for use in human review.

Pemetaan Tren Belajar Agama Melalui Media Sosial

“…Efisiensi dan efektivitas media sosial juga menjadi pengaruh bagi pengguna untuk memanfaatkan platform media sosial (Mathew et al, 2019). Terutama selektif dari ujaran kebencian dan hal-hal negatif dari dampak penggunaan media sosial sebagai sumber belajar agama (Kursuncu et al, 2019 ;Miller, 2017;Yar, 2018). Proses selektif ini berfungsi menelusuri konten yang valid, sebagaimana ditemukan berbagai pelanggaran kasus terhadap ketidakabsahan konten keagamaan (Myers, 2018).…”

Semantically Modeling Cyber Influence Campaigns (CICs): Ontology Model and Case Studies

“…Other influence modeling examines security threats in Social Gaming Networks (SGN) [52], using influence modeling to identify why certain players are targeted by scams or cyber attacks. Influence modeling has also been employed to improve the detection of subtle and long-running radicalization of individuals [53]. These examples show that there are many forms of influence modeling and methods of defining a campaign.…”

When the Bad is Good and the Good is Bad: Understanding Cyber Social Health Through Online Behavioral Change

“…For instance, in the context of Islamist extremism, the true meaning of "jihad" can be propagated as harming others in the name of religion; on the other hand, in the context of the religion of Islam, it can be self-struggle to become a better person or fight for self-defense. 13 Hence, distinguishing these semantic differences in the assessment of such narratives and incorporating prior knowledge in contemporary models is essential. It is unlikely that a language model built from a large corpus will provide a clear context as it would be possible by using a knowledge graph or ontology.…”

“…Hence, we need to better understand how the message with the misinformation is adopted and/or propagated by an individual. For instance, recent studies 13 of online radicalization by ISIS via persuasive tactics and strategies demonstrated the need for such an approach. These studies have observed that neurolinguistic, cognitive, and behavior changes were largely contextualized by religious, hate, and ideological dimensions.…”

“…The impact of such pitfalls in fair algorithmic design might be elevated when deployed in an application used by millions of people. For instance, a recent study 13 showcased how bias in verified data for extremism might lead to potentially unfair social discrimination against innocent individuals. While removing biases from data in entirety may not be possible, we need to explore solutions that will mitigate bias and promote fairness in the model.…”