An Application of Natural Language Processing to Classify What Terrorists Say They Want

Bridgelall, Raj

doi:10.3390/socsci11010023

Cited by 6 publications

(3 citation statements)

References 48 publications

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“…GloVe was trained explicitly on Twitter data and all relevant tweet information. Bridgelall [28] used two AI approaches to determine perpetrators' motives from a massive database of terrorist acts globally. First, they categorize attackers' motivations into six categories using NLP and ML techniques: hate, protest, revenge, vulnerability, strength, and intimidation.…”

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

confidence: 99%

“…Future studies should incorporate a more comprehensive analysis of the spatial-temporal aspects of terrorism by considering the neighboring pixel values within the raster datasets. [28] Descriptive Analysis: ML models used for topic modeling and text classification to identify the perpetrator's aim of the attacks Aims of the attacks are identified in six categories: Protest, retaliate, intimidate, weaken, force, and despise GTD Manual effort required for empirical topic modeling.…”

Section: Gtd Acled Icewsmentioning

confidence: 99%

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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%

Section: Gtd Acled Icewsmentioning

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

“…One might assume that perpetrators consistently choose large cities with numerous targets such as public transportation, resulting in severe consequences. However, due to terrorism's adaptive nature and multifaceted motives (Bridgelall, 2022), targeted locations can vary widely. Applied intelligence to identify attributes more closely related to attacked locations can assist policymakers in prioritizing risk mitigation and countermeasure resources for locations at elevated risk.…”

Section: Introductionmentioning

confidence: 99%

Identifying factors associated with terrorist attack locations by data mining and machine learning

Bridgelall

2023

Int Social Sci J

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While studies typically investigate the socio‐economic factors of perpetrators to comprehend terrorism motivations, there was less emphasis placed on factors related to terrorist attack locations. Addressing this knowledge gap, this study conducts a multivariate analysis to determine attributes that are more associated with terrorist attacked locations than others. To tackle the challenge of identifying pertinent attributes, the methodology merges a Global Terrorism Database with relevant socio‐economic attributes from the literature. The workflow then trains 11 machine learning (ML) models on the combined data set. Among the 75 attributes assessed, 10 improved the predictability of targeted locations, with population and public transportation infrastructure being key factors. After optimizing hyperparameters, a multi‐layer perceptron—a type of artificial neural network—exhibited superior predictive performance, achieving an area under the curve score of 89.3%, classification accuracy of 88.1% and a harmonically balanced precision and recall score of 87.3%. In contrast, support vector machines demonstrated the poorest performance. The study also revealed that race, age, gender, marital status, income level and home values did not improve predictive performance. The ML workflow developed can aid policymakers in quantifying risks and making objective decisions regarding resource allocation to safeguard public health.

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Open source intelligence extraction for terrorism‐related information: A review

Chaudhary

Bansal

2022

WIREs Data Min & Knowl

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In this contemporary era, where a large part of the world population is deluged by extensive use of the internet and social media, terrorists have found it a potential opportunity to execute their vicious plans. They have got a befitting medium to reach out to their targets to spread propaganda, disseminate training content, operate virtually, and further their goals. To restrain such activities, information over the internet in context of terrorism needs to be analyzed to channel it to appropriate measures in combating terrorism. Open Source Intelligence (OSINT) accounts for a felicitous solution to this problem, which is an emerging discipline of leveraging publicly accessible sources of information over the internet by effectively utilizing it to extract intelligence. The process of OSINT extraction is broadly observed to be in three phases (i) Data Acquisition, (ii) Data Enrichment, and (iii) Knowledge Inference. In the context of terrorism, researchers have given noticeable contributions in compliance with these three phases. However, a comprehensive review that delineates these research contributions into an integrated workflow of intelligence extraction has not been found. The paper presents the most current review in OSINT, reflecting how the various state‐of‐the‐art tools and techniques can be applied in extracting terrorism‐related textual information from publicly accessible sources. Various data mining and text analysis‐based techniques, that is, natural language processing, machine learning, and deep learning have been reviewed to extract and evaluate textual data. Additionally, towards the end of the paper, we discuss challenges and gaps observed in different phases of OSINT extraction. This article is categorized under: Application Areas > Government and Public Sector Commercial, Legal, and Ethical Issues > Social Considerations Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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An Application of Natural Language Processing to Classify What Terrorists Say They Want

Cited by 6 publications

References 48 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

Identifying factors associated with terrorist attack locations by data mining and machine learning

Open source intelligence extraction for terrorism‐related information: A review

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