# Inference of Media Bias and Content Quality Using Natural-Language Processing

arXiv: https://arxiv.org/abs/2212.00237 · HTML: https://mlsanity.com/paper/2212.00237/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 77 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 17 of 17)

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- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Recent Advances in Recurrent Neural Networks](https://mlsanity.com/paper/1801.01078/) (2017)
- [Comparative Study of CNN and RNN for Natural Language Processing](https://mlsanity.com/paper/1702.01923/) (2017)
- [Quantifying social organization and political polarization in online platforms](https://mlsanity.com/paper/2010.00590/) (2020)
- [Detecting Political Biases of Named Entities and Hashtags on Twitter](https://mlsanity.com/paper/2209.08110/) (2022)
- [Opinion Dynamics of Online Social Network Users: A Micro-Level Analysis](https://mlsanity.com/paper/2011.00864/) (2020)
- [Online reactions to the 2017 'Unite the Right' rally in Charlottesville: Measuring polarization in Twitter networks using media followership](https://mlsanity.com/paper/1905.07755/) (2019)
- [A Model for the Influence of Media on the Ideology of Content in Online Social Networks](https://mlsanity.com/paper/1904.09238/) (2019)
- [COVID, BLM, and the polarization of US politicians on Twitter](https://mlsanity.com/paper/2008.03263/) (2020)
- [News-sharing on Twitter reveals emergent fragmentation of media agenda and persistent polarization](https://mlsanity.com/paper/2112.09763/) (2021)
- [Information flow estimation: a study of news on Twitter](https://mlsanity.com/paper/2205.06029/) (2022)
- [Predicting Political Ideology from Digital Footprints](https://mlsanity.com/paper/2206.00397/) (2022)

## Abstract (arXiv)

Media bias can significantly impact the formation and development of opinions and sentiments in a population. It is thus important to study the emergence and development of partisan media and political polarization. However, it is challenging to quantitatively infer the ideological positions of media outlets. In this paper, we present a quantitative framework to infer both political bias and content quality of media outlets from text, and we illustrate this framework with empirical experiments with real-world data. We apply a bidirectional long short-term memory (LSTM) neural network to a data set of more than 1 million tweets to generate a two-dimensional ideological-bias and content-quality measurement for each tweet. We then infer a ``media-bias chart'' of (bias, quality) coordinates for the media outlets by integrating the (bias, quality) measurements of the tweets of the media outlets. We also apply a variety of baseline machine-learning methods, such as a naive-Bayes method and a support-vector machine (SVM), to infer the bias and quality values for each tweet. All of these baseline approaches are based on a bag-of-words approach. We find that the LSTM-network approach has the best performance of the examined methods. Our results illustrate the importance of leveraging word order into machine-learning methods in text analysis.