Resources & Publications

@inproceedings{10.1145/3383583.3398585,

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 = {9781450375856},

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}

}

@inproceedings{10.1145/3383583.3398619,

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 = {9781450375856},

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}

}

@inproceedings{hamborg2019a,

title = {Automated Identification of Media Bias by Word Choice and Labeling in News Articles},

author = {Felix Hamborg and Anastasia Zhukova and Bela Gipp},

url = {https://doi.org/10.1109/JCDL.2019.00036},

doi = {10.1109/JCDL.2019.00036},

isbn = {9781728115474},

year  = {2020},

date = {2020-01-01},

booktitle = {Proceedings of the 18th Joint Conference on Digital Libraries},

pages = {196–205},

publisher = {IEEE Press},

address = {Champaign, Illinois},

series = {JCDL '19},

abstract = {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 analysis approaches from the social sciences, a research domain in which media bias has been studied for decades. This paper makes three contributions. First, we present NewsWCL50, the first open evaluation dataset for the identification of bias by WCL consisting of 8,656 manual annotations in 50 news articles. Second, we propose a method capable of extracting instances of bias by WCL while outperforming state-of-the-art methods, such as coreference resolution, which currently cannot resolve very broadly defined or abstract coreferences used by journalists. We evaluate our method on the NewsWCL50 dataset, achieving an F1=45.7% compared to F1=29.8% achieved by the best performing state-of-the-art technique. Lastly, we present a prototype demonstrating the effectiveness of our approach in finding frames caused by bias by WCL.},

keywords = {automated content analysis, automated frame analysis, CAQDAS, CAS, emotions, entity perception, news bias, news slant, NLP},

pubstate = {published},

tppubtype = {inproceedings}

}