Comparing Classification Trees to Discern Patterns of Terrorism*

Saiya, Nilay; Scime, Anthony

doi:10.1111/ssqu.12629

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…The results of the experiments show that the suggested method can be used to analyze and predict information about terrorist attacks thoroughly and accurately. Saiya et al [36] used a specific kind of algorithmic analysis known as C4.5 classification trees to examine social and political violence. Their proposed method has been applied based on four unique outline techniques for comparing classification models for the C4.5 classification method.…”

Section: A Machine Learning Approach For Enhancing Defense Against Gl...mentioning

confidence: 99%

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Abdalsalam,

Li,

Dahou

et al. 2024

CMES

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One of the biggest dangers to society today is terrorism, where attacks have become one of the most significant risks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) have become the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management, medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related, initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terrorist attacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database (GTD) can influence the accuracy of the model's classification of terrorist attacks, where each part of the data can provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomy has one or more tags attached to it, referred as "related tags." We applied machine learning classifiers to classify terrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts and learns contextual features from text attributes to acquire more information from text data. The extracted contextual features are combined with the "key features" of the dataset and used to perform the final classification. The study explored different experimental setups with various classifiers to evaluate the model's performance. The experimental results show that the proposed framework outperforms the latest techniques for classifying terrorist attacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

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Section: A Machine Learning Approach For Enhancing Defense Against Gl...mentioning

confidence: 99%

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Abdalsalam,

Li,

Dahou

et al. 2024

CMES

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show abstract

“…Terrorists, they contend, come in different varieties, have different undergirding ideologies, and use different tactics. The most important distinction between terrorist groups raised in the literature pertains to ideology, especially ideological differences between those groups motivated by religion and those that are not (Fox 2003; Hoffman 1995; Juergensmeyer 2003; Rapoport 1984; Saiya 2015a; Saiya 2018; Saiya and Scime 2019). Various studies have found that religious terrorists–because they hold non-negotiable demands and therefore regard violence as a divine duty–fight harder, last longer, and cause more destruction than their non-religious counterparts (Hassner 2003; Hassner 2009; Henne 2012; Hoffman 1995; Jasko et al 2022; Svensson 2007; Svensson and Harding 2011; Toft 2007).…”

Section: Theorizing the Differential Effects Of Democracy On Terrorismmentioning

confidence: 99%

Democracy’s Ambivalent Effect on Terrorism

Hand

Saiya

2022

Journal of Conflict Resolution

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Does democracy heighten or lessen the incidence of terrorism? This question has eluded a definitive answer for over three decades. Some terrorism researchers find that democracy has dampened terrorism; others that democracy has encouraged it; still others that there is a curvilinear or more nuanced relationship between the two. In this article, we contend that democracy has differential effects on terrorism, depending on the goals of particular terrorist groups. We claim that efforts to restrict democracy, by decreasing political rights and civil liberties, will effectively constrain terrorist activity from “strategic” groups but will have the opposite effect on “universalist” groups. The reason for this variance, we argue, involves the scope of ambition and underlying motivation distinguishing these very different types of terrorist movements. Because universalist groups have abstract ambitions and non-negotiable goals, they are unlikely to be deterred by crackdowns on democracy. Alternatively, because strategic groups do not see their missions as all-or-nothing campaigns of good versus evil and instead seek limited and concrete goals, anti-democratic measures are more likely to succeed against them. Our analysis of 200 countries from 1972 to 2016 finds strong support for this claim.

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“…Previous peer-reviewed studies have shown religious freedom to be positively related to a number of public goods, including (i) development (Alon & Chase, 2005;Grim, 2008a;Shah, 2018), (ii) security (Farr, 2008;Toft et al, 2011;Grim & Finke, 2011;Inboden, 2012;Saiya, 2014;Saiya, 2015a;Saiya, 2015b;Saiya and Scime, 2015;Saiya, 2017a;Saiya, 2017b;Saiya and Fidler, 2018;Saiya and Scime, 2019;Saiya, 2019a;Saiya, 2019b;Saiya, 2020;Saiya and Manchanda, 2020a;Saiya and Manchanda, 2020b;Henne, Saiya, and Hand, 2020;Saiya, 2021;Saiya and Hand, 2022;, (iii) business (Grim et al, 2014), and (iv) even health (Grim & Grim, 2019). Thus, religious freedom is believed to not only be an integral part of the "bundled commodity" of human freedoms, it is also closely related to the general betterment of people's lives (Grim, 2008b).…”

Section: Introductionmentioning

confidence: 99%

Did Religious Freedom Exacerbate COVID-19? A Global Analysis

Saiya

Manchanda

Wadidi³

2023

J Relig Health

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Did countries that became more repressive of religion during the COVID-19 pandemic, experience more COVID-19 cases and associated fatalities than countries that did not restrict religious freedom? As the pandemic raged across the world, many houses of worship defied governmental orders against public worship, leading many pundits, policy makers, and critics of religion to express concern that churches, mosques, synagogues, and other houses of worship would become incubators of COVID-19. In this view, religious freedom was seen as an obstacle to combatting the virus. In this article, we evaluate this proposition. We find that countries that maintained their levels of religious freedom throughout the pandemic were not more likely to witness higher rates of COVID-19 cases and deaths from COVID-19. The results are robust to a number of different model specifications.

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Comparing Classification Trees to Discern Patterns of Terrorism*

Cited by 6 publications

References 46 publications

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

Democracy’s Ambivalent Effect on Terrorism

Did Religious Freedom Exacerbate COVID-19? A Global Analysis

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