Uncovering the essence of diverse media biases from the semantic embedding space
Bibliographic record
Abstract
Abstract Media bias widely exists in the articles published by news media, influencing their readers’ perceptions, and bringing prejudice or injustice to society. However, current analysis methods usually rely on human efforts or only focus on a specific type of bias, which cannot capture the varying magnitudes, connections, and dynamics of multiple biases, thus remaining insufficient to provide a deep insight into media bias. Inspired by the Cognitive Miser and Semantic Differential theories in psychology, and leveraging embedding techniques in the field of natural language processing, this study proposes a general media bias analysis framework that can uncover biased information in the semantic embedding space on a large scale and objectively quantify it on diverse topics. More than 8 million event records and 1.2 million news articles are collected to conduct this study. The findings indicate that media bias is highly regional and sensitive to popular events at the time, such as the Russia-Ukraine conflict. Furthermore, the results reveal some notable phenomena of media bias among multiple U.S. news outlets. While they exhibit diverse biases on different topics, some stereotypes are common, such as gender bias. This framework will be instrumental in helping people have a clearer insight into media bias and then fight against it to create a more fair and objective news environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".