AUTOMATED POLITICAL BIAS CLASSIFICATION IN NEWS AGENCIES: A SPARSITY-INDUCING FEATURE SELECTION APPROACH
AuthorMohseni, Sayyed Faraz
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThis study offers a new solution to political bias classification in news agencies. Our method uses search engine score functions to develop a measure of the relevance of each word in text scrapped from news websites. With these scores, we train models using existing feature selection methods and a custom feature selec-tion algorithm that we developed. The result-ing models are contrasted with each other and BERT-based counterparts. Models trained using our proposed method and custom algorithm outperformed others by achieving macro F1 scores of 0.81 and 0.78 on right-wing and left-wing bias detection respectively, which outper-form transformer-based classifiers by over 0.30.
Degree ProgramComputer Science