Automated identification of media bias in news articles: an interdisciplinary literature review

@article{Hamborg2018AutomatedIO,
  title={Automated identification of media bias in news articles: an interdisciplinary literature review},
  author={Felix Hamborg and Karsten Donnay and Bela Gipp},
  journal={International Journal on Digital Libraries},
  year={2018},
  pages={1-25}
}
Media bias, i.e., slanted news coverage, can strongly impact the public perception of the reported topics. [...] Key Result Our review suggests that suitable, automated methods from computer science, primarily in the realm of natural language processing, are already available for each of the discussed forms of media bias, opening multiple directions for promising further research in computer science in this area.Expand
Automated Identification of Media Bias by Word Choice and Labeling in News Articles
TLDR
An automated approach to identify bias by WCL is presented that employs models and manual analysis approaches from the social sciences and outperforming state-of-the-art methods, such as coreference resolution, which currently cannot resolve very broadly defined or abstract coreferences used by journalists. Expand
Illegal Aliens or Undocumented Immigrants? Towards the Automated Identification of Bias by Word Choice and Labeling
TLDR
Newsalyze is proposed, a work-in-progress prototype that imitates a manual analysis concept for media bias established in the social sciences, which aims to find instances of bias by word choice and labeling in a set of news articles reporting on the same event. Expand
How biased are American media outlets? A framework for presentation bias regression
  • M. Tran
  • Computer Science
  • 2020 IEEE International Conference on Big Data (Big Data)
  • 2020
TLDR
A novel unsupervised framework to estimate presentation bias of news sources is proposed and it is found that this approach can provide reliable bias ratings, with a Pearson correlation coefficient of up to 0.92. Expand
Assessing Media Bias in Cross-Linguistic and Cross-National Populations
TLDR
A methodology based on word embeddings, lexicon translation, and document similarity to assess media bias in news articles published in different idioms, using translated versions of subjectivity lexicons that were originally constructed for measuring subjectivity in the Brazilian Portuguese language. Expand
Media Bias Everywhere? A Vision for Dealing with the Manipulation of Public Opinion
TLDR
This paper shows three fields of actions that result from using machine learning to analyze media bias: the evaluation principles of media bias, the information presentation of media biased evaluation, and the transparency ofMedia bias evaluation. Expand
Media Bias, the Social Sciences, and NLP: Automating Frame Analyses to Identify Bias by Word Choice and Labeling
TLDR
This research project aims to devise methods that identify instances of WCL bias and estimate the frames they induce, e.g., not only is “terrorists” of negative polarity but also ascribes to aggression and fear. Expand
Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias
TLDR
Using a multilevel model, it is found that perceived journalist bias is significantly related to perceived political extremeness and impartiality of the article. Expand
The POLUSA Dataset: 0.9M Political News Articles Balanced by Time and Outlet Popularity
TLDR
POLUSA is presented, a dataset that represents the online media landscape as perceived by an average US news consumer and enables studying a variety of subjects, e.g., media effects and political partisanship, and allows to utilize data-intense deep learning methods. Expand
A Multidimensional Dataset Based on Crowdsourcing for Analyzing and Detecting News Bias
TLDR
This paper proposes a methodology based on crowdsourcing for obtaining a large data set for news bias analysis and identification and uses this methodology to create a dataset consisting of more than 2,000 sentences annotated with 43,000 bias and bias dimension labels. Expand
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing
TLDR
A novel news bias dataset is proposed which facilitates the development and evaluation of approaches for detecting subtle bias in news articles and for understanding the characteristics of biased sentences and can serve as resource for related researches including ones focusing on fake news detection. Expand
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