Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents  

作　　者：Shynar Mussiraliyeva Batyrkhan Omarov Paul Yoo Milana Bolatbek 

机构地区：[1]Al-Farabi Kazakh National University,Almaty,Kazakhstan [2]CSIS,Birkbeck College,University of London,London,UK

出　　处：《Computers, Materials & Continua》2022年第1期915-934,共20页计算机、材料和连续体（英文）

基　　金：This work was supported by the grant“Development of models,algorithms for semantic analysis to identify extremist content in web resources and creation the tool for cyber forensics”funded by the Ministry of Digital Development,Innovations and Aerospace industry of the Republic of Kazakhstan.Grant No.IRN AP06851248.Supervisor of the project is Shynar Mussiraliyeva,email:[e-mail].

摘　　要：In this research paper,we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus,thereby checking whether it is possible to detect extremist messages in the Kazakh language.To do this,the authors trained models using six classic machine-learning algorithms such as Support Vector Machine,Decision Tree,Random Forest,K Nearest Neighbors,Naive Bayes,and Logistic Regression.To increase the accuracy of detecting extremist texts,we used various characteristics such as Statistical Features,TF-IDF,POS,LIWC,and applied oversampling and undersampling techniques to handle imbalanced data.As a result,we achieved 98%accuracy in detecting religious extremism in Kazakh texts for the collected dataset.Testing the developed machine learningmodels in various databases that are often found in everyday life“Jokes”,“News”,“Toxic content”,“Spam”,“Advertising”has also shown high rates of extremism detection.

关 键 词：EXTREMISM religious extremism machine learning social media social network natural language processing NLP 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]