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Uncovering the essence of diverse media biases from the semantic embedding space

Computer Science

Uncovering the essence of diverse media biases from the semantic embedding space

H. Huang, H. Zhu, et al.

This study, conducted by Hong Huang, Hua Zhu, Wenshi Liu, Hua Gao, Hai Jin, and Bang Liu, reveals a groundbreaking media bias analysis framework that utilizes embedding techniques to quantify bias across diverse topics. With an analysis of over 8 million event records and 1.2 million news articles, findings indicate that media bias varies regionally and is influenced by current events, shedding light on important stereotypes like gender bias.

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Playback language: English
Abstract
Media bias significantly influences public perception and can lead to societal injustices. Existing analysis methods often rely on human effort or focus on specific bias types, failing to capture the complexities of multiple biases. This study proposes a general media bias analysis framework using embedding techniques to objectively quantify bias across various topics. Analyzing over 8 million event records and 1.2 million news articles, the findings reveal that media bias is regionally diverse and sensitive to current events. The framework reveals notable biases among US news outlets, highlighting both diverse biases on various topics and common stereotypes, such as gender bias. This framework aims to improve understanding and combat media bias for a more objective news environment.
Publisher
Humanities & Social Sciences Communications
Published On
May 22, 2024
Authors
Hong Huang, Hua Zhu, Wenshi Liu, Hua Gao, Hai Jin, Bang Liu
Tags
media bias
bias analysis framework
embedding techniques
regional diversity
current events
US news outlets
gender bias
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