Resources & Publications
Resources
Publications
2022
Krieger, David; Spinde, Timo; Ruas, Terry; Kulshrestha, Juhi; Gipp, Bela
A Domain-adaptive Pre-training Approach for Language Bias Detection in News Inproceedings
In: 2022 ACM/IEEE Joint Conference on Digital Libraries (JCDL), Cologne, Germany, 2022.
@inproceedings{Krieger2022,
title = {A Domain-adaptive Pre-training Approach for Language Bias Detection in News},
author = {David Krieger and Timo Spinde and Terry Ruas and Juhi Kulshrestha and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2022/06/Krieger2022_mbg.pdf},
doi = {10.1145/3529372.3530932},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
booktitle = {2022 ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
address = {Cologne, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Krieger, Jan-David; Ruas, Terry; Mitrović, Jelena; Götz-Hahn, Franz; Aizawa, Akiko; Gipp, Bela
Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles Inproceedings
In: Proceedings of the iConference 2022, Virtual event, 2022.
@inproceedings{Spinde2022a,
title = {Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles},
author = {Timo Spinde and Jan-David Krieger and Terry Ruas and Jelena Mitrović and Franz Götz-Hahn and Akiko Aizawa and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2022/03/Spinde2022a_mbg.pdf},
doi = {https://doi.org/10.1007/978-3-030-96957-8_20},
year = {2022},
date = {2022-03-04},
urldate = {2022-03-04},
booktitle = {Proceedings of the iConference 2022},
address = {Virtual event},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Jeggle, Christin; Haupt, Magdalena; Gaissmaier, Wolfgang; Giese, Helge
How do we raise media bias awareness effectively? Effects of visualizations to communicate bias Journal Article
In: PLOS ONE, vol. 17, no. 4, pp. 1-14, 2022.
Abstract | Links | BibTeX | Tags:
@article{10.1371/journal.pone.0266204,
title = {How do we raise media bias awareness effectively? Effects of visualizations to communicate bias},
author = {Timo Spinde and Christin Jeggle and Magdalena Haupt and Wolfgang Gaissmaier and Helge Giese},
url = {https://doi.org/10.1371/journal.pone.0266204},
doi = {10.1371/journal.pone.0266204},
year = {2022},
date = {2022-01-01},
journal = {PLOS ONE},
volume = {17},
number = {4},
pages = {1-14},
publisher = {Public Library of Science},
abstract = {Media bias has a substantial impact on individual and collective perception of news. Effective communication that may counteract its potential negative effects still needs to be developed. In this article, we analyze how to facilitate the detection of media bias with visual and textual aids in the form of (a) a forewarning message, (b) text annotations, and (c) political classifiers. In an online experiment, we randomized 985 participants to receive a biased liberal or conservative news article in any combination of the three aids. Meanwhile, their subjective perception of media bias in this article, attitude change, and political ideology were assessed. Both the forewarning message and the annotations increased media bias awareness, whereas the political classification showed no effect. Incongruence between an articles’ political position and individual political orientation also increased media bias awareness. Visual aids did not mitigate this effect. Likewise, attitudes remained unaltered.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Media bias has a substantial impact on individual and collective perception of news. Effective communication that may counteract its potential negative effects still needs to be developed. In this article, we analyze how to facilitate the detection of media bias with visual and textual aids in the form of (a) a forewarning message, (b) text annotations, and (c) political classifiers. In an online experiment, we randomized 985 participants to receive a biased liberal or conservative news article in any combination of the three aids. Meanwhile, their subjective perception of media bias in this article, attitude change, and political ideology were assessed. Both the forewarning message and the annotations increased media bias awareness, whereas the political classification showed no effect. Incongruence between an articles’ political position and individual political orientation also increased media bias awareness. Visual aids did not mitigate this effect. Likewise, attitudes remained unaltered.
Haak, Fabian; Schaer, Philipp
Auditing Search Query Suggestion Bias Through Recursive Algorithm Interrogation Inproceedings
In: WebSci '22: 14th ACM Web Science Conference 2022, ACM, 2022.
BibTeX | Tags: bias esupol haak myown schaer
@inproceedings{haak2022auditing,
title = {Auditing Search Query Suggestion Bias Through Recursive
Algorithm Interrogation},
author = {Fabian Haak and Philipp Schaer},
year = {2022},
date = {2022-01-01},
booktitle = {WebSci '22: 14th ACM Web Science Conference 2022},
publisher = {ACM},
keywords = {bias esupol haak myown schaer},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Spinde, Timo; Plank, Manuel; Krieger, Jan-David; Ruas, Terry; Gipp, Bela; Aizawa, Akiko
Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts Inproceedings
In: Findings of the Association for Computational Linguistics: EMNLP 2021, Dominican Republic, 2021.
@inproceedings{Spinde2021f,
title = {Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts},
author = {Timo Spinde and Manuel Plank and Jan-David Krieger and Terry Ruas and Bela Gipp and Akiko Aizawa},
url = {https://media-bias-research.org/wp-content/uploads/2022/01/Neural_Media_Bias_Detection_Using_Distant_Supervision_With_BABE___Bias_Annotations_By_Experts_MBG.pdf},
doi = {10.18653/v1/2021.findings-emnlp.101},
year = {2021},
date = {2021-11-01},
urldate = {2021-11-01},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
address = {Dominican Republic},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hinterreiter, Smilla
A Gamified Approach To Automatically Detect Biased Wording And Train Critical Reading Inproceedings
In: 2021 IEEE International Conference on Data Mining Workshops (ICDMW), 2021.
@inproceedings{hinterreiter2021a,
title = {A Gamified Approach To Automatically Detect Biased Wording And Train Critical Reading},
author = {Smilla Hinterreiter},
url = {https://media-bias-research.org/wp-content/uploads/2021/10/hinterreiter2021a.pdf},
doi = {10.1109/ICDMW53433.2021.00141},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
booktitle = {2021 IEEE International Conference on Data Mining Workshops (ICDMW)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo
An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles Inproceedings
In: 2021 IEEE International Conference on Data Mining Workshops (ICDMW), 2021.
Links | BibTeX | Tags: media bias, news analysis, slanted coverage, text retrieval
@inproceedings{spinde2021g,
title = {An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles},
author = {Timo Spinde},
url = {https://media-bias-research.org/wp-content/uploads/2021/09/Spinde2021g.pdf},
doi = {10.1109/ICDMW53433.2021.00144},
year = {2021},
date = {2021-09-30},
urldate = {2021-09-30},
booktitle = {2021 IEEE International Conference on Data Mining Workshops (ICDMW)},
keywords = {media bias, news analysis, slanted coverage, text retrieval},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Sinha, Kanishka; Meuschke, Norman; Gipp, Bela
TASSY - A Text Annotation Survey System Inproceedings
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2021.
@inproceedings{Spinde2021c,
title = {TASSY - A Text Annotation Survey System},
author = {Timo Spinde and Kanishka Sinha and Norman Meuschke and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2022/01/Spinde2021c.pdf},
doi = {10.1109/JCDL52503.2021.00052},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Kreuter, Christina; Gaissmaier, Wolfgang; Hamborg, Felix; Gipp, Bela; Giese, Helge
Do You Think It’s Biased? How To Ask For The Perception Of Media Bias Inproceedings
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2021.
@inproceedings{Spinde2021e,
title = {Do You Think It’s Biased? How To Ask For The Perception Of Media Bias},
author = {Timo Spinde and Christina Kreuter and Wolfgang Gaissmaier and Felix Hamborg and Bela Gipp and Helge Giese},
url = {https://media-bias-research.org/wp-content/uploads/2022/01/Spinde2021e.pdf},
doi = {10.1109/JCDL52503.2021.00018},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Krieger, David; Plank, Manu; Gipp, Bela
Towards A Reliable Ground-Truth For Biased Language Detection Inproceedings
In: Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), Virtual Event, 2021.
@inproceedings{Spinde2021d,
title = {Towards A Reliable Ground-Truth For Biased Language Detection},
author = {Timo Spinde and David Krieger and Manu Plank and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2022/01/Spinde2021d.pdf},
doi = {10.1109/JCDL52503.2021.00053},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
booktitle = {Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)},
address = {Virtual Event},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Haak, Fabian; Engelmann, Björn
IRCologne at GermEval 2021: Toxicity Classification Inproceedings
In: Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, pp. 47–53, Association for Computational Linguistics, Duesseldorf, Germany, 2021.
Abstract | Links | BibTeX | Tags: 2021 bias classification data engelmann haak nlp programming snorkel toxic
@inproceedings{haak-engelmann-2021-ircologne,
title = {IRCologne at GermEval 2021: Toxicity Classification},
author = {Fabian Haak and Björn Engelmann},
url = {https://aclanthology.org/2021.germeval-1.7},
year = {2021},
date = {2021-09-01},
booktitle = {Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments},
pages = {47--53},
publisher = {Association for Computational Linguistics},
address = {Duesseldorf, Germany},
abstract = {In this paper, we describe the TH Köln's submission for the Shared Task on the Identification of Toxic Comments at GermEval 2021. Toxicity is a severe and latent problem in comments in online discussions. Complex language model based methods have shown the most success in identifying toxicity. However, these approaches lack explainability and might be insensitive to domain-specific renditions of toxicity. In the scope of the GermEval 2021 toxic comment classification task (Risch et al., 2021), we employed a simple but promising combination of term-frequency-based classification and rule-based labeling to produce effective but to no lesser degree explainable toxicity predictions.},
keywords = {2021 bias classification data engelmann haak nlp programming snorkel toxic},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we describe the TH Köln's submission for the Shared Task on the Identification of Toxic Comments at GermEval 2021. Toxicity is a severe and latent problem in comments in online discussions. Complex language model based methods have shown the most success in identifying toxicity. However, these approaches lack explainability and might be insensitive to domain-specific renditions of toxicity. In the scope of the GermEval 2021 toxic comment classification task (Risch et al., 2021), we employed a simple but promising combination of term-frequency-based classification and rule-based labeling to produce effective but to no lesser degree explainable toxicity predictions.
Spinde, Timo; Rudnitckaia, Lada; Hamborg, Felix; Bela,; Gipp,
Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings Inproceedings
In: Proceedings of the iConference 2021, Beijing, China (Virtual Event), 2021.
@inproceedings{Spinde2021Embeddings,
title = {Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings},
author = {Timo Spinde and Lada Rudnitckaia and Felix Hamborg and Bela and Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2021/01/Spinde2021.pdf},
doi = {10.1007/978-3-030-71305-8_17},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
booktitle = {Proceedings of the iConference 2021},
address = {Beijing, China (Virtual Event)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Rudnitckaia, Lada; Kanishka, Sinha; Hamborg, Felix; Bela,; Gipp,; Donnay, Karsten
MBIC – A Media Bias Annotation Dataset Including Annotator Characteristics Inproceedings
In: Proceedings of the iConference 2021, Beijing, China (Virtual Event), 2021.
@inproceedings{Spinde2021MBIC,
title = {MBIC – A Media Bias Annotation Dataset Including Annotator Characteristics},
author = {Timo Spinde and Lada Rudnitckaia and Sinha Kanishka and Felix Hamborg and Bela and Gipp and Karsten Donnay},
url = {https://media-bias-research.org/wp-content/uploads/2021/01/Spinde2021a.pdf},
doi = {10.6084/m9.figshare.17192924},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
booktitle = {Proceedings of the iConference 2021},
address = {Beijing, China (Virtual Event)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Rudnitckaia, Lada; Mitrović, Jelena; Hamborg, Felix; Granitzer, Michael; Gipp, Bela; Donnay, Karsten
Automated identification of bias inducing words in news articles using linguistic and context-oriented features Journal Article
In: Information Processing & Management, vol. 58, no. 3, pp. 102505, 2021, ISSN: 0306-4573.
Abstract | Links | BibTeX | Tags: bias data set, context analysis, feature engineering, media bias, news analysis, text analysis
@article{SPINDE2021102505,
title = {Automated identification of bias inducing words in news articles using linguistic and context-oriented features},
author = {Timo Spinde and Lada Rudnitckaia and Jelena Mitrović and Felix Hamborg and Michael Granitzer and Bela Gipp and Karsten Donnay},
url = {https://www.sciencedirect.com/science/article/pii/S0306457321000157/pdfft?md5=64e81212b3bfa861d01a6fe3d5b979c3&pid=1-s2.0-S0306457321000157-main.pdf},
doi = {https://doi.org/10.1016/j.ipm.2021.102505},
issn = {0306-4573},
year = {2021},
date = {2021-01-01},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102505},
abstract = {Media has a substantial impact on public perception of events, and, accordingly, the way media presents events can potentially alter the beliefs and views of the public. One of the ways in which bias in news articles can be introduced is by altering word choice. Such a form of bias is very challenging to identify automatically due to the high context-dependence and the lack of a large-scale gold-standard data set. In this paper, we present a prototypical yet robust and diverse data set for media bias research. It consists of 1,700 statements representing various media bias instances and contains labels for media bias identification on the word and sentence level. In contrast to existing research, our data incorporate background information on the participants’ demographics, political ideology, and their opinion about media in general. Based on our data, we also present a way to detect bias-inducing words in news articles automatically. Our approach is feature-oriented, which provides a strong descriptive and explanatory power compared to deep learning techniques. We identify and engineer various linguistic, lexical, and syntactic features that can potentially be media bias indicators. Our resource collection is the most complete within the media bias research area to the best of our knowledge. We evaluate all of our features in various combinations and retrieve their possible importance both for future research and for the task in general. We also evaluate various possible Machine Learning approaches with all of our features. XGBoost, a decision tree implementation, yields the best results. Our approach achieves an F1-score of 0.43, a precision of 0.29, a recall of 0.77, and a ROC AUC of 0.79, which outperforms current media bias detection methods based on features. We propose future improvements, discuss the perspectives of the feature-based approach and a combination of neural networks and deep learning with our current system.},
keywords = {bias data set, context analysis, feature engineering, media bias, news analysis, text analysis},
pubstate = {published},
tppubtype = {article}
}
Media has a substantial impact on public perception of events, and, accordingly, the way media presents events can potentially alter the beliefs and views of the public. One of the ways in which bias in news articles can be introduced is by altering word choice. Such a form of bias is very challenging to identify automatically due to the high context-dependence and the lack of a large-scale gold-standard data set. In this paper, we present a prototypical yet robust and diverse data set for media bias research. It consists of 1,700 statements representing various media bias instances and contains labels for media bias identification on the word and sentence level. In contrast to existing research, our data incorporate background information on the participants’ demographics, political ideology, and their opinion about media in general. Based on our data, we also present a way to detect bias-inducing words in news articles automatically. Our approach is feature-oriented, which provides a strong descriptive and explanatory power compared to deep learning techniques. We identify and engineer various linguistic, lexical, and syntactic features that can potentially be media bias indicators. Our resource collection is the most complete within the media bias research area to the best of our knowledge. We evaluate all of our features in various combinations and retrieve their possible importance both for future research and for the task in general. We also evaluate various possible Machine Learning approaches with all of our features. XGBoost, a decision tree implementation, yields the best results. Our approach achieves an F1-score of 0.43, a precision of 0.29, a recall of 0.77, and a ROC AUC of 0.79, which outperforms current media bias detection methods based on features. We propose future improvements, discuss the perspectives of the feature-based approach and a combination of neural networks and deep learning with our current system.
Ehrhardt, Jonas; Spinde, Timo; Vardasbi, Ali; Hamborg, Felix
Omission of Information: Identifying Political Slant via an Analysis of Co-occurring Entities Incollection
In: Information between Data and Knowledge, vol. 74, pp. 80–93, Werner Hülsbusch, Glückstadt, 2021, (Session 2: Information Behavior and Information Literacy 2).
Abstract | Links | BibTeX | Tags: media bias; bias by omission; news articles; co-occurrences
@incollection{epub44939,
title = {Omission of Information: Identifying Political Slant via an Analysis of Co-occurring Entities},
author = {Jonas Ehrhardt and Timo Spinde and Ali Vardasbi and Felix Hamborg},
url = {https://epub.uni-regensburg.de/44939/},
year = {2021},
date = {2021-01-01},
booktitle = {Information between Data and Knowledge},
volume = {74},
pages = {80--93},
publisher = {Werner Hülsbusch},
address = {Glückstadt},
series = {Schriften zur Informationswissenschaft},
abstract = {Due to the strong impact the news has on society, the detection and analysis of bias within the media are important topics. Most approaches to bias detection focus on linguistic forms of bias or the evaluation and tracing of sources. In this paper, we present an approach that analyzes co-occurrences of entities across articles of different news outlets to indicate a strong but difficult to detect form of bias: bias by omission of information. Specifically, we present and evaluate different methods of identifying entity co-occurrences and then use the best performing method, reference entity detection, to analyze the coverage of nine major US news outlets over one year. We set a low performing but transparent baseline, which is able to identify a news outlet?s affiliation towards a political orientation. Our approach employing reference entity selection, i. e., analyzing how often one entity co-occurs with others across a set of documents, yields an F1-score of F1 = 0.51 compared to F1 = 0.20 of the TF-IDF baseline.},
note = {Session 2: Information Behavior and Information Literacy 2},
keywords = {media bias; bias by omission; news articles; co-occurrences},
pubstate = {published},
tppubtype = {incollection}
}
Due to the strong impact the news has on society, the detection and analysis of bias within the media are important topics. Most approaches to bias detection focus on linguistic forms of bias or the evaluation and tracing of sources. In this paper, we present an approach that analyzes co-occurrences of entities across articles of different news outlets to indicate a strong but difficult to detect form of bias: bias by omission of information. Specifically, we present and evaluate different methods of identifying entity co-occurrences and then use the best performing method, reference entity detection, to analyze the coverage of nine major US news outlets over one year. We set a low performing but transparent baseline, which is able to identify a news outlet?s affiliation towards a political orientation. Our approach employing reference entity selection, i. e., analyzing how often one entity co-occurs with others across a set of documents, yields an F1-score of F1 = 0.51 compared to F1 = 0.20 of the TF-IDF baseline.
Garz, Marcel; Martin, Gregory J.
Media Influence on Vote Choices: Unemployment News and Incumbents' Electoral Prospects Journal Article
In: American Journal of Political Science, vol. 65, no. 2, pp. 278-293, 2021.
Abstract | Links | BibTeX | Tags:
@article{https://doi.org/10.1111/ajps.12539,
title = {Media Influence on Vote Choices: Unemployment News and Incumbents' Electoral Prospects},
author = {Marcel Garz and Gregory J. Martin},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ajps.12539},
doi = {https://doi.org/10.1111/ajps.12539},
year = {2021},
date = {2021-01-01},
journal = {American Journal of Political Science},
volume = {65},
number = {2},
pages = {278-293},
abstract = {Abstract How does news about the economy influence voting decisions? We isolate the effect of the information environment from the effect of change in the underlying economic conditions themselves by taking advantage of left-digit bias. We show that unemployment figures crossing a round-number “milestone” cause a discontinuous increase in the amount of media coverage devoted to unemployment conditions, and we use this discontinuity to estimate the effect of attention to unemployment news on voting, holding constant the actual economic conditions on the ground. Milestone effects on incumbent U.S. governor vote shares are large and notably asymmetric: Bad milestone events hurt roughly twice as much as good milestone events help.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abstract How does news about the economy influence voting decisions? We isolate the effect of the information environment from the effect of change in the underlying economic conditions themselves by taking advantage of left-digit bias. We show that unemployment figures crossing a round-number “milestone” cause a discontinuous increase in the amount of media coverage devoted to unemployment conditions, and we use this discontinuity to estimate the effect of attention to unemployment news on voting, holding constant the actual economic conditions on the ground. Milestone effects on incumbent U.S. governor vote shares are large and notably asymmetric: Bad milestone events hurt roughly twice as much as good milestone events help.
Babaei, Mahmoudreza; Kulshrestha, Juhi; Chakraborty, Abhijnan; Redmiles, Elissa M.; Cha, Meeyoung; Gummadi, Krishna P.
Analyzing Biases in Perception of Truth in News Stories and Their Implications for Fact Checking Journal Article
In: IEEE Transactions on Computational Social Systems, 2021.
@article{Babaei2021Analy-56086,
title = {Analyzing Biases in Perception of Truth in News Stories and Their Implications for Fact Checking},
author = {Mahmoudreza Babaei and Juhi Kulshrestha and Abhijnan Chakraborty and Elissa M. Redmiles and Meeyoung Cha and Krishna P. Gummadi},
doi = {10.1109/TCSS.2021.3096038},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Computational Social Systems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Haak, Fabian; Schaer, Philipp
Perception-Aware Bias Detection for Query Suggestions Inproceedings
In: Boratto, Ludovico; Faralli, Stefano; Marras, Mirko; Stilo, Giovanni (Ed.): Advances in Bias and Fairness in Information Retrieval - Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021, Proceedings, Springer Nature, Switzerland, 2021, ISBN: 978-3-030-78817-9.
Links | BibTeX | Tags: 2021 bias esupol haak myown schaer
@inproceedings{haak2021perceptionaware,
title = {Perception-Aware Bias Detection for Query Suggestions},
author = {Fabian Haak and Philipp Schaer},
editor = {Ludovico Boratto and Stefano Faralli and Mirko Marras and Giovanni Stilo},
doi = {10.1007/978-3-030-78818-6_12},
isbn = {978-3-030-78817-9},
year = {2021},
date = {2021-01-01},
booktitle = {Advances in Bias and Fairness in Information Retrieval - Second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021, Proceedings},
volume = {1418},
publisher = {Springer Nature},
address = {Switzerland},
series = {Communications in Computer and Information Science},
keywords = {2021 bias esupol haak myown schaer},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Spinde, Timo; Hamborg, Felix; Gipp, Bela
Media Bias in German News Articles : A Combined Approach Inproceedings
In: Proceedings of the 8th International Workshop on News Recommendation and Analytics ( INRA 2020), Virtual event, 2020.
@inproceedings{Spinde2020,
title = {Media Bias in German News Articles : A Combined Approach},
author = {Timo Spinde and Felix Hamborg and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2021/01/Media-Bias-in-German-News-Articles-A-Combined-Approach.pdf},
doi = {10.1007/978-3-030-65965-3_41},
year = {2020},
date = {2020-09-01},
urldate = {2020-09-01},
booktitle = {Proceedings of the 8th International Workshop
on News Recommendation and Analytics ( INRA 2020)},
address = {Virtual event},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ganguly, Soumen; Kulshrestha, Juhi; An, Jisun; Kwak, Haewoon
Empirical Evaluation of Three Common Assumptions in Building Political Media Bias Datasets Inproceedings
In: pp. 939-943, 2020.
@inproceedings{Ganguly_Kulshrestha_An_Kwak_2020,
title = {Empirical Evaluation of Three Common Assumptions in Building Political Media Bias Datasets},
author = {Soumen Ganguly and Juhi Kulshrestha and Jisun An and Haewoon Kwak},
url = {https://ojs.aaai.org/index.php/ICWSM/article/view/7362},
year = {2020},
date = {2020-05-01},
urldate = {2020-05-01},
journal = {Proceedings of the International AAAI Conference on Web and Social Media},
volume = {14},
number = {1},
pages = {939-943},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Hamborg, Felix; Gipp, Bela
An Integrated Approach to Detect Media Bias in German News Articles Inproceedings
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, pp. 505–506, Association for Computing Machinery, Virtual Event, China, 2020, ISBN: 9781450375856.
Abstract | Links | BibTeX | Tags: content analysis, frame analysis, media bias, news bias, news slant
@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}
}
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.
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 Inproceedings
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, pp. 389–392, Association for Computing Machinery, Virtual Event, China, 2020, ISBN: 9781450375856.
Abstract | Links | BibTeX | Tags: bias visualization, news bias, news slant, perception of news
@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}
}
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.
Garz, Marcel; Sörensen, Jil; Stone, Daniel F.
Partisan selective engagement: Evidence from Facebook Journal Article
In: Journal of Economic Behavior & Organization, vol. 177, pp. 91-108, 2020, ISSN: 0167-2681.
Abstract | Links | BibTeX | Tags: Filter bubble, media bias, Polarization, Political immunity, Social media
@article{GARZ202091,
title = {Partisan selective engagement: Evidence from Facebook},
author = {Marcel Garz and Jil Sörensen and Daniel F. Stone},
url = {https://www.sciencedirect.com/science/article/pii/S0167268120302079},
doi = {https://doi.org/10.1016/j.jebo.2020.06.016},
issn = {0167-2681},
year = {2020},
date = {2020-01-01},
journal = {Journal of Economic Behavior & Organization},
volume = {177},
pages = {91-108},
abstract = {This study investigates the effects of variation in “congeniality” of news on Facebook user engagement (likes, shares, and comments). We compile an original data set of Facebook posts by 84 German news outlets on politicians that were investigated for criminal offenses from January 2012 to June 2017. We also construct an index of each outlet's media slant by comparing the language of the outlet with that of the main political parties, which allows us to measure the congeniality of the posts. We find that user engagement with congenial posts is higher than with uncongenial ones, especially in terms of likes. The within-outlet, within-topic design allows us to infer that the greater engagement with congenial news is likely driven by psychological and social factors, rather than a desire for accurate or otherwise instrumental information.},
keywords = {Filter bubble, media bias, Polarization, Political immunity, Social media},
pubstate = {published},
tppubtype = {article}
}
This study investigates the effects of variation in “congeniality” of news on Facebook user engagement (likes, shares, and comments). We compile an original data set of Facebook posts by 84 German news outlets on politicians that were investigated for criminal offenses from January 2012 to June 2017. We also construct an index of each outlet's media slant by comparing the language of the outlet with that of the main political parties, which allows us to measure the congeniality of the posts. We find that user engagement with congenial posts is higher than with uncongenial ones, especially in terms of likes. The within-outlet, within-topic design allows us to infer that the greater engagement with congenial news is likely driven by psychological and social factors, rather than a desire for accurate or otherwise instrumental information.
Garz, Marcel; Sood, Gaurav; Stone, Daniel F.; Wallace, Justin
The supply of media slant across outlets and demand for slant within outlets: Evidence from US presidential campaign news Journal Article
In: European Journal of Political Economy, vol. 63, pp. 101877, 2020, ISSN: 0176-2680.
Abstract | Links | BibTeX | Tags: Horse race news, media bias, Media slant, Motivated beliefs, Polarization, Selective exposure
@article{GARZ2020101877,
title = {The supply of media slant across outlets and demand for slant within outlets: Evidence from US presidential campaign news},
author = {Marcel Garz and Gaurav Sood and Daniel F. Stone and Justin Wallace},
url = {https://www.sciencedirect.com/science/article/pii/S0176268020300252},
doi = {https://doi.org/10.1016/j.ejpoleco.2020.101877},
issn = {0176-2680},
year = {2020},
date = {2020-01-01},
journal = {European Journal of Political Economy},
volume = {63},
pages = {101877},
abstract = {We conduct across-outlet and within-outlet (and within-topic) analyses of “congenially” slanted news. We study “horse race” news (news on candidates' chances in an upcoming election) from six major online outlets for the 2012 and 2016 US presidential campaigns. We find robust evidence that horse race headlines were slanted congenially with respect to the preferences of the outlets' typical readers. However, evidence of congenial slant in the timing and frequency of horse race stories is weaker. We also find limited evidence of greater within-outlet demand for headlines most congenial to outlets' typical readers, and somewhat stronger evidence of greater demand for relatively uncongenial headlines. We discuss how various aspects of our results are consistent with each of the major mechanisms driving slant studied in the theoretical literature, and may help explain when each mechanism is more likely to come into play. In particular, readers may be more likely to click on uncongenial headlines due to inferring that these stories are particularly informative when they stand in contrast to an outlet's typically congenial slant.},
keywords = {Horse race news, media bias, Media slant, Motivated beliefs, Polarization, Selective exposure},
pubstate = {published},
tppubtype = {article}
}
We conduct across-outlet and within-outlet (and within-topic) analyses of “congenially” slanted news. We study “horse race” news (news on candidates' chances in an upcoming election) from six major online outlets for the 2012 and 2016 US presidential campaigns. We find robust evidence that horse race headlines were slanted congenially with respect to the preferences of the outlets' typical readers. However, evidence of congenial slant in the timing and frequency of horse race stories is weaker. We also find limited evidence of greater within-outlet demand for headlines most congenial to outlets' typical readers, and somewhat stronger evidence of greater demand for relatively uncongenial headlines. We discuss how various aspects of our results are consistent with each of the major mechanisms driving slant studied in the theoretical literature, and may help explain when each mechanism is more likely to come into play. In particular, readers may be more likely to click on uncongenial headlines due to inferring that these stories are particularly informative when they stand in contrast to an outlet's typically congenial slant.
2019
Bonart, Malte; Samokhina, Anastasiia; Heisenberg, Gernot; Schaer, Philipp
An investigation of biases in web search engine query suggestions Journal Article
In: Online Information Review, vol. 44, no. 2, pp. 365-381, 2019, ISSN: 1468-4527.
Abstract | Links | BibTeX | Tags: bias bonart esupol myown schaer
@article{bonart_investigation_2019,
title = {An investigation of biases in web search engine query suggestions},
author = {Malte Bonart and Anastasiia Samokhina and Gernot Heisenberg and Philipp Schaer},
url = {https://www.emerald.com/insight/content/doi/10.1108/OIR-11-2018-0341/full/html},
doi = {10.1108/OIR-11-2018-0341},
issn = {1468-4527},
year = {2019},
date = {2019-12-01},
urldate = {2020-01-02},
journal = {Online Information Review},
volume = {44},
number = {2},
pages = {365-381},
abstract = {Purpose
Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017.
Design/methodology/approach
This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time.
Findings
By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often.
Originality/value
This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.},
keywords = {bias bonart esupol myown schaer},
pubstate = {published},
tppubtype = {article}
}
Purpose
Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017.
Design/methodology/approach
This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time.
Findings
By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often.
Originality/value
This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.
Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017.
Design/methodology/approach
This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time.
Findings
By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often.
Originality/value
This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.
Kulshrestha, Juhi; Eslami, Motahhare; Messias, Johnnatan; Zafar, Muhammad Bilal; Ghosh, Saptarshi; Gummadi, Krishna P.; Karahalios, Karrie
Search bias quantification : investigating political bias in social media and web search Journal Article
In: Information Retrieval Journal, vol. 22, no. 1-2, pp. 188–227, 2019, ISSN: 1386-4564.
@article{Kulshrestha2019-04Searc-53924,
title = {Search bias quantification : investigating political bias in social media and web search},
author = {Juhi Kulshrestha and Motahhare Eslami and Johnnatan Messias and Muhammad Bilal Zafar and Saptarshi Ghosh and Krishna P. Gummadi and Karrie Karahalios},
doi = {10.1007/s10791-018-9341-2},
issn = {1386-4564},
year = {2019},
date = {2019-01-01},
journal = {Information Retrieval Journal},
volume = {22},
number = {1-2},
pages = {188--227},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2018
Babaei, Mahmoudreza; Kulshrestha, Juhi; Chakraborty, Abhijnan; Benevenuto, Fabrício; Gummadi, Krishna P.; Weller, Adrian
Purple Feed: Identifying High Consensus News Posts on Social Media Inproceedings
In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 10–16, Association for Computing Machinery, New Orleans, LA, USA, 2018, ISBN: 9781450360128.
Abstract | Links | BibTeX | Tags: audience leaning based features, consensus, news consumption in social media, Polarization, purple feed
@inproceedings{10.1145/3278721.3278761,
title = {Purple Feed: Identifying High Consensus News Posts on Social Media},
author = {Mahmoudreza Babaei and Juhi Kulshrestha and Abhijnan Chakraborty and Fabrício Benevenuto and Krishna P. Gummadi and Adrian Weller},
url = {https://doi.org/10.1145/3278721.3278761},
doi = {10.1145/3278721.3278761},
isbn = {9781450360128},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
pages = {10–16},
publisher = {Association for Computing Machinery},
address = {New Orleans, LA, USA},
series = {AIES '18},
abstract = {Although diverse news stories are actively posted on social media, readers often focus on the news which reinforces their pre-existing views, leading to 'filter bubble' effects. To combat this, some recent systems expose and nudge readers toward stories with different points of view. One example is the Wall Street Journal's 'Blue Feed, Red Feed' system, which presents posts from biased publishers on each side of a topic. However, these systems have had limited success. We present a complementary approach which identifies high consensus 'purple' posts that generate similar reactions from both 'blue' and 'red' readers. We define and operationalize consensus for news posts on Twitter in the context of US politics. We show that high consensus posts can be identified and discuss their empirical properties. We present a method for automatically identifying high and low consensus news posts on Twitter, which can work at scale across many publishers. To do this, we propose a novel category of audience leaning based features, which we show are well suited to this task. Finally, we present our 'Purple Feed' system which highlights high consensus posts from publishers on both sides of the political spectrum.},
keywords = {audience leaning based features, consensus, news consumption in social media, Polarization, purple feed},
pubstate = {published},
tppubtype = {inproceedings}
}
Although diverse news stories are actively posted on social media, readers often focus on the news which reinforces their pre-existing views, leading to 'filter bubble' effects. To combat this, some recent systems expose and nudge readers toward stories with different points of view. One example is the Wall Street Journal's 'Blue Feed, Red Feed' system, which presents posts from biased publishers on each side of a topic. However, these systems have had limited success. We present a complementary approach which identifies high consensus 'purple' posts that generate similar reactions from both 'blue' and 'red' readers. We define and operationalize consensus for news posts on Twitter in the context of US politics. We show that high consensus posts can be identified and discuss their empirical properties. We present a method for automatically identifying high and low consensus news posts on Twitter, which can work at scale across many publishers. To do this, we propose a novel category of audience leaning based features, which we show are well suited to this task. Finally, we present our 'Purple Feed' system which highlights high consensus posts from publishers on both sides of the political spectrum.
Ribeiro, Filipe N.; Henrique, Lucas; Benevenuto, Fabricio; Chakraborty, Abhijnan; Kulshrestha, Juhi; Babaei, Mahmoudreza; Gummadi, Krishna P.
Media Bias Monitor : Quantifying Biases of Social Media News Outlets at Large-Scale Inproceedings
In: Twelfth International AAAI Conference on Web and Social Media, pp. 290–299, AAAI Press, Palo Alto, California, 2018, ISBN: 978-1-57735-798-8.
@inproceedings{Ribeiro2018-06-15Media-53950,
title = {Media Bias Monitor : Quantifying Biases of Social Media News Outlets at Large-Scale},
author = {Filipe N. Ribeiro and Lucas Henrique and Fabricio Benevenuto and Abhijnan Chakraborty and Juhi Kulshrestha and Mahmoudreza Babaei and Krishna P. Gummadi},
url = {https://aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17878},
isbn = {978-1-57735-798-8},
year = {2018},
date = {2018-01-01},
booktitle = {Twelfth International AAAI Conference on Web and Social Media},
pages = {290--299},
publisher = {AAAI Press},
address = {Palo Alto, California},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Bonart, Malte; Schaer, Philipp
Intertemporal Connections Between Query Suggestions and Search Engine Results for Politics Related Queries Inproceedings
In: EuroCSS 2018 Dataset Challenge, Cologne, 2018.
Links | BibTeX | Tags: bias bonart esupol myown schaer
@inproceedings{bonart2018intertemporal,
title = {Intertemporal Connections Between Query Suggestions and Search Engine Results for Politics Related Queries},
author = {Malte Bonart and Philipp Schaer},
url = {https://arxiv.org/abs/1812.08585},
year = {2018},
date = {2018-01-01},
booktitle = {EuroCSS 2018 Dataset Challenge},
address = {Cologne},
keywords = {bias bonart esupol myown schaer},
pubstate = {published},
tppubtype = {inproceedings}
}
2014
Garz, Marcel
Good news and bad news: evidence of media bias in unemployment reports Journal Article
In: Public Choice, vol. 161, no. 3/4, pp. 499–515, 2014, ISSN: 00485829, 15737101.
Abstract | Links | BibTeX | Tags:
@article{10.2307/24507505,
title = {Good news and bad news: evidence of media bias in unemployment reports},
author = {Marcel Garz},
url = {http://www.jstor.org/stable/24507505},
issn = {00485829, 15737101},
year = {2014},
date = {2014-01-01},
journal = {Public Choice},
volume = {161},
number = {3/4},
pages = {499--515},
publisher = {Springer},
abstract = {This study employs information obtained from media content analyses, as well as economic and political data, to investigate negativity in unemployment news between 2001 and 2010 in Germany. The data indicate a substantial bias in terms of the amounts of negative and positive reports, compared with the actual development of unemployment. Moreover, the media tend to place negative unemployment reports more prominently than positive ones. The estimates suggest that the bias is not the consequence of journalists asymmetrically interpreting the official unemployment numbers. Instead, it is associated with the exploitation of often non-economic information and structural influences in the process of news production.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study employs information obtained from media content analyses, as well as economic and political data, to investigate negativity in unemployment news between 2001 and 2010 in Germany. The data indicate a substantial bias in terms of the amounts of negative and positive reports, compared with the actual development of unemployment. Moreover, the media tend to place negative unemployment reports more prominently than positive ones. The estimates suggest that the bias is not the consequence of journalists asymmetrically interpreting the official unemployment numbers. Instead, it is associated with the exploitation of often non-economic information and structural influences in the process of news production.