An Integrated Approach to Detect Media Bias in German News Articles

Spinde, Timo; Hamborg, Felix; Gipp, Béla

doi:10.1145/3383583.3398585

Cited by 19 publications

(11 citation statements)

References 7 publications

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“…We also plan to check differences between asking for bias directly and indirectly. Lastly, we plan to automate the underlying annotation process [15,17,19] and expand the approach to other languages [20].…”

Section: Discussionmentioning

confidence: 99%

Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias

Spinde

Hamborg

Donnay

et al. 2020

Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020

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Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Many researchers focus on automatically detecting and identifying media bias in the news, but only very few studies exist that systematically analyze how theses biases can be best visualized and communicated. We create three manually annotated datasets and test varying visualization strategies. The results show no strong effects of becoming aware of the bias of the treatment groups compared to the control group, although a visualization of hand-annotated bias communicated bias instances more effectively than a framing visualization. Showing participants an overview page, which opposes different viewpoints on the same topic, does not yield differences in respondents' bias perception. Using a multilevel model, we find that perceived journalist bias is significantly related to perceived political extremeness and impartiality of the article.

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Section: Discussionmentioning

confidence: 99%

Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias

Spinde

Hamborg

Donnay

et al. 2020

Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020

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“…News coverage is not just the communication of facts; it puts facts into context and transports specific opinions. The way how "the news cover a topic or issue can decisively impact public debates and affect our collective decision making" [12], slanted news can heavily influence the public opinion [11]. However, only a few research projects yet focus on automated methods to identify such bias.…”

Section: Introductionmentioning

confidence: 99%

Identification of Biased Terms in News Articles by Comparison of Outlet-Specific Word Embeddings

Spinde

Rudnitckaia

Hamborg

et al. 2021

Diversity, Divergence, Dialogue

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Slanted news coverage, also called media bias, can heavily influence how news consumers interpret and react to the news. To automatically identify biased language, we present an exploratory approach that compares the context of related words. We train two word embedding models, one on texts of leftwing, the other on right-wing news outlets. Our hypothesis is that a word's representations in both word embedding spaces are more similar for non-biased words than biased words. The underlying idea is that the context of biased words in different news outlets varies more strongly than the one of non-biased words, since the perception of a word as being biased differs depending on its context. While we do not find statistical significance to accept the hypothesis, the results show the effectiveness of the approach. For example, after a linear mapping of both word embeddings spaces, 31% of the words with the largest distances potentially induce bias. To improve the results, we find that the dataset needs to be significantly larger, and we derive further methodology as future research direction. To our knowledge, this paper presents the first in-depth look at the context of bias words measured by word embeddings.

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“…Likewise, few approaches aim to specifically identify instances of WCL bias. For example, Lim et al (2018); Spinde et al (2020b) propose to investigate words with a low document frequency in a set of news articles reporting on the same event, to find potentially biasing words that are characteristic for a single article. NewsCube 2.0 employs crowdsourcing to estimate the bias of articles reporting on a topic.…”

Section: (Semi-)automated Approachesmentioning

confidence: 99%

Media Bias, the Social Sciences, and NLP: Automating Frame Analyses to Identify Bias by Word Choice and Labeling

Hamborg¹

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Media bias can strongly impact the public perception of topics reported in the news. A difficult to detect, yet powerful form of slanted news coverage is called bias by word choice and labeling (WCL). WCL bias can occur, for example, when journalists refer to the same semantic concept by using different terms that frame the concept differently and consequently may lead to different assessments by readers, such as the terms "freedom fighters" and "terrorists," or "gun rights" and "gun control." In this research project, I aim 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. To achieve this, I plan to research methods using natural language processing and deep learning while employing models and using analysis concepts from the social sciences, where researchers have studied media bias for decades. The first results indicate the effectiveness of this interdisciplinary research approach. My vision is to devise a system that helps news readers to become aware of the differences in media coverage caused by bias.

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An Integrated Approach to Detect Media Bias in German News Articles

Cited by 19 publications

References 7 publications

Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias

Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias

Identification of Biased Terms in News Articles by Comparison of Outlet-Specific Word Embeddings

Media Bias, the Social Sciences, and NLP: Automating Frame Analyses to Identify Bias by Word Choice and Labeling

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