Publications
2020
Spinde, Timo; Hamborg, Felix; Donnay, Karsten; Becerra, Angelica; Gipp, Bela
Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias Proceedings Article
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, S. 389–392, Association for Computing Machinery, Virtual Event, China, 2020, ISBN: 978-1-4503-7585-6.
Abstract | Links | BibTeX | Schlagwörter: bias visualization, news bias, news slant, perception of news
@inproceedings{spinde_enabling_2020,
title = {Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias},
author = {Timo Spinde and Felix Hamborg and Karsten Donnay and Angelica Becerra and Bela Gipp},
url = {https://doi.org/10.1145/3383583.3398619},
doi = {10.1145/3383583.3398619},
isbn = {978-1-4503-7585-6},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020},
pages = {389–392},
publisher = {Association for Computing Machinery},
address = {Virtual Event, China},
series = {JCDL '20},
abstract = {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 in-stances 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.},
keywords = {bias visualization, news bias, news slant, perception of news},
pubstate = {published},
tppubtype = {inproceedings}
}
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 in-stances 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.
Spinde, Timo; Hamborg, Felix; Gipp, Bela
An Integrated Approach to Detect Media Bias in German News Articles Proceedings Article
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, S. 505–506, Association for Computing Machinery, Virtual Event, China, 2020, ISBN: 978-1-4503-7585-6.
Abstract | Links | BibTeX | Schlagwörter: content analysis, frame analysis, media bias, news bias, news slant
@inproceedings{spinde_integrated_2020,
title = {An Integrated Approach to Detect Media Bias in German News Articles},
author = {Timo Spinde and Felix Hamborg and Bela Gipp},
url = {https://doi.org/10.1145/3383583.3398585},
doi = {10.1145/3383583.3398585},
isbn = {978-1-4503-7585-6},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020},
pages = {505–506},
publisher = {Association for Computing Machinery},
address = {Virtual Event, China},
series = {JCDL '20},
abstract = {Media bias may often affect individuals' opinions on reported topics. Many existing methods that aim to identify such bias forms employ individual, specialized techniques and focus only on English texts. We propose to combine the state-of-the-art in order to further improve the performance in bias identification. Our prototype consists of three analysis components to identify media bias words in German news articles. We use an IDF-based component, a component utilizing a topic-dependent bias dictionary created using word embeddings, and an extensive dictionary of German emotional terms compiled from multiple sources. Finally, we discuss two not yet implemented analysis components that use machine learning and network analysis to identify media bias. All dictionary-based analysis components are experimentally extended with the use of general word embeddings. We also show the results of a user study.},
keywords = {content analysis, frame analysis, media bias, news bias, news slant},
pubstate = {published},
tppubtype = {inproceedings}
}
Media bias may often affect individuals' opinions on reported topics. Many existing methods that aim to identify such bias forms employ individual, specialized techniques and focus only on English texts. We propose to combine the state-of-the-art in order to further improve the performance in bias identification. Our prototype consists of three analysis components to identify media bias words in German news articles. We use an IDF-based component, a component utilizing a topic-dependent bias dictionary created using word embeddings, and an extensive dictionary of German emotional terms compiled from multiple sources. Finally, we discuss two not yet implemented analysis components that use machine learning and network analysis to identify media bias. All dictionary-based analysis components are experimentally extended with the use of general word embeddings. We also show the results of a user study.
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