Automated Identification of Media Bias by Word Choice and Labeling in News Articles

@article{Hamborg2019AutomatedIO,
  title={Automated Identification of Media Bias by Word Choice and Labeling in News Articles},
  author={Felix Hamborg and Anastasia Zhukova and Bela Gipp},
  journal={2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
  year={2019},
  pages={196-205}
}
Media bias can strongly impact the individual and public perception of news events. One difficult-to-detect, yet powerful form of slanted news coverage is bias by word choice and labeling (WCL). Bias by WCL can occur when journalists refer to the same concept, yet use different terms, which results in different sentiments being sparked in the readers, such as the terms "economic migrants" vs. "refugees." We present an automated approach to identify bias by WCL that employs models and manual… Expand
Automated identification of bias inducing words in news articles using linguistic and context-oriented features
TLDR
This work identifies and engineer various linguistic, lexical, and syntactic features that can potentially be media bias indicators and presents a way to detect bias-inducing words in news articles automatically, which outperforms current media bias detection methods based on features. 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
Machine-Learning media bias
TLDR
An automated method for measuring media bias is presented, by analyzing roughly a million articles from roughly a hundred newspapers for bias in dozens of news topics, that agrees well with previous bias classifications based on human judgement. Expand
MBIC - A Media Bias Annotation Dataset Including Annotator Characteristics
TLDR
MBIC (Media Bias Including Characteristics) is the first available dataset about media bias reporting detailed information on annotator characteristics and their individual background providing unique and more reliable insights into the perception of bias. Expand
Newsalyze: Effective Communication of Person-Targeting Biases in News Articles
TLDR
A system for bias identification, which combines state-of-the-art methods from natural language understanding and bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers is presented and it is suggested that groups of similarly slanted news articles due to substantial biases present in individual news articles are detected. Expand
How Can the Perception of Media Bias in News Articles Be Objectively Measured? Best Practices and Recommendations Using User Studies
Media coverage possesses a substantial effect on the public perception of events. The way media frames events can significantly alter the beliefs and perceptions of our society. Nevertheless, nearlyExpand
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
Modeling, Quantifying and Visualizing Media Bias on Twitter
TLDR
A principled approach to quantify media bias along with insightful visualizations for popular media sources using their tweets are presented along with a statistically consistent model of neutral tweet counts and polarity rates. 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
Study of Detecting the Political Bias in News Articles
Nowadays news audiences are experiencing an "echo chamber" due to news biased coverage, which causes individuals to shape views with only one side of the story in mind. Media is considered theExpand
...
1
2
3
...

References

SHOWING 1-10 OF 59 REFERENCES
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
DEIM Forum 2018 C 1-3 Towards Bias Inducing Word Detection by Linguistic Cue Analysis in News Articles
Biased news still exists even though the balance, fairness and accuracy are important qualities in news reporting. The bias in news causes political and social bipolarization and what is worse, itExpand
Automated identification of media bias in news articles: an interdisciplinary literature review
TLDR
It is suggested 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
Understanding Characteristics of Biased Sentences in News Articles
TLDR
Ground truth dataset created with the help of crowd-sourcing for fostering research on bias detection and removal from news content is described and the results indicate that determining bias-induced words is subjective to certain degree and that a high agreement on all bias-inducing words of all readers is hard to obtain. Expand
Linguistic Models for Analyzing and Detecting Biased Language
TLDR
The analysis of real instances of human edits designed to remove bias from Wikipedia articles uncovers two classes of bias: framing bias, such as praising or perspective-specific words, which is linked to the literature on subjectivity; and epistemological bias, related to whether propositions that are presupposed or entailed in the text are uncontroversially accepted as true. Expand
NewsCube: delivering multiple aspects of news to mitigate media bias
TLDR
This paper presents NewsCube, a novel Internet news service aiming at mitigating the effect of media bias, which is the first to develop a news service as a solution and study its effect. Expand
Bias-aware news analysis using matrix-based news aggregation
Media bias describes differences in the content or presentation of news. It is an ubiquitous phenomenon in news coverage that can have severely negative effects on individuals and society.Expand
Testing and Comparing Computational Approaches for Identifying the Language of Framing in Political News
TLDR
Results show that the best performing classifiers achieve performance comparable to that of human annotators, and they indicate which aspects of language most pertain to framing. Expand
Sidelines: An Algorithm for Increasing Diversity in News and Opinion Aggregators
TLDR
The Sidelines algorithm – which temporarily suppresses a voter’s preferences after a preferred item has been selected – is presented as one approach to increase the diversity of result sets and can help build news and opinion aggregators that present users with a broader range of topics and opinions. Expand
A Measure of Media Bias
In this paper we estimate ADA (Americans for Democratic Action) scores for major media outlets such as the New York Times, USA Today, Fox News Special Report, and all three network television newsExpand
...
1
2
3
4
5
...