Using Machine Learning to Investigate Potential Image Bias in News Articles

Media bias refers to the deviation from objective reporting in media, where journalists introduce external beliefs or agendas into the journalistic process, altering the perception of an event or issue. A newspaper article may introduce bias in numerous ways, including image use. Analysing media bia...

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Bibliographic Details
Published in2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON) pp. 174 - 179
Main Authors Hili, Gabriel, Seychell, Dylan
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.06.2024
Subjects
Computer Vision
keyword extraction
Manuals
Media Monitoring
NER tagging
News Analysis
Philosophical considerations
Pipelines
Sentiment analysis
Technological innovation
Transforms
Visualization
VLP
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Summary:Media bias refers to the deviation from objective reporting in media, where journalists introduce external beliefs or agendas into the journalistic process, altering the perception of an event or issue. A newspaper article may introduce bias in numerous ways, including image use. Analysing media bias manually is challenging and time-consuming, as determining bias requires nuanced human judgement. This paper proposes a simple yet effective technique for investigating picture-related bias in online newspaper articles by utilising BLIP (Bootstrapping Language-Image Pretraining), a Vision-Language Pretraining model. We scraped six online newspapers to demonstrate this technique, achieving promising results for adopting this methodology to automate media bias research. The technique enables large-scale analysis of historical articles to uncover previously undetected biases, complementing modern studies reliant on manual work. It also facilitates monitoring to maintain news integrity. Furthermore, this research highlights innovative applications of AI for journalism to augment human analysis. This study illustrates how AI can transform the media landscape by automating bias analysis to improve news quality and reader trust.
ISSN:2158-8481
DOI:10.1109/MELECON56669.2024.10608508