Machine Learning Approaches for Classifying the Peace-War Orientations of Global News Organizations’ Social Media Posts
| dc.creator | Peter, Ada, Omole, Rose, Misra, Sanjay, Garg, Lalit, Jonathan, Oluranti | |
| dc.date | 2022 | |
| dc.date.accessioned | 2025-04-15T11:46:22Z | |
| dc.description | The study used the existing conceptualizations of peace and war journalism to create supervised machine learning text classifiers trained and tested to identify the war or peace orientations of news stories posted on social media. Peace-oriented journalists promote peace initiatives, ignore differences, and promote conflict resolution. In contrast, war-oriented journalists promote differences between opposing parties and instigate violence as means to resolving conflicts. Using Naïve Bayes, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM), the study trained and tested five computational models to detect the peace or war orientations of the news posted on social media. The results indicate that Random Forest has the highest predictive accuracy for predicting war or peace orientations of online news stories. Naïve Bayes ranked the least accurate algorithm for predicting peace or war orientations of online news stories. | |
| dc.format | application/pdf | |
| dc.identifier | http://eprints.covenantuniversity.edu.ng/18019/ | |
| dc.identifier.uri | https://repository.covenantuniversity.edu.ng/handle/123456789/48602 | |
| dc.language | en | |
| dc.subject | H Social Sciences (General), QA75 Electronic computers. Computer science | |
| dc.title | Machine Learning Approaches for Classifying the Peace-War Orientations of Global News Organizations’ Social Media Posts | |
| dc.type | Conference or Workshop Item |
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