Anglia Ruskin Research Online (ARRO)
Browse
DOCUMENT
FINAL Article.pdf (5.07 MB)
DOCUMENT
Data_Driven_Model_to_Investigate_Political_Bias_in_Mainstream_Media.pdf (1.84 MB)
1/0
2 files

Data Driven Model To Investigate Political Bias In Mainstream Media

Download all (6.9 MB)
journal contribution
posted on 2023-08-30, 20:32 authored by Eric Ness, Arooj Fatima, Mahdi Maktab Dar Oghaz
Media bias refers to the tendency of mainstream media outlets to report news in a way that reflects their own political, social, or ideological beliefs or preferences. Such bias may obfuscate facts, manipulate public beliefs, misinform readers, narrow perspectives and viewpoints, and result in greater polarization and division. To counter this issue, this study presents a model for quantifying media bias, aimed at enabling individuals to make more informed media choices. The proposed media analysis model includes a pipeline that gathers articles from three distinct sources: mainstream media news outlets, known conservative outlets, and known liberal media outlets. The collected articles were subjected to a range of text pre-processing operations and subsequently, curated n-gram and topic lists were generated. Several classification models including BERT, logistic regression, random forest, multinomial, and long short-term memory (LSTM) were created and fine-tuned on polarized news sources and used for analyzing news articles from the mainstream media. Among the various classification models that we investigated in this study, BERT achieved overall higher accuracy across the majority of topics. The analysis of mainstream media on various topics yielded different results, with some being balanced and others leaning left or right, depending on the topic. The research also suggests the effectiveness of using highly polarized news sources for developing models to predict media bias.

History

Refereed

  • Yes

Volume

11

Page range

41880-41893

Publication title

IEEE Access

ISSN

2169-3536

Publisher

IEEE

File version

  • Accepted version

Language

  • eng

Legacy posted date

2023-05-19

Legacy creation date

2023-05-19

Legacy Faculty/School/Department

Faculty of Science & Engineering

Usage metrics

    ARU Outputs

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC