Resources & Publications
Resources
Publications
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/2021/09/Neural_Media_Bias_Detection_Using_Distant_Supervision_With_BABE___Bias_Annotations_By_Experts_MBG.pdf},
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},
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},
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/2021/07/Spinde2021c.pdf},
year = {2021},
date = {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/2021/08/Spinde2021e.pdf},
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/2021/07/spinde2021d.pdf},
year = {2021},
date = {2021-09-01},
booktitle = {Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)},
address = {Virtual Event},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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/Identification-of-Biased-Terms-in-News-Articles-by-Comparison-of-Outlet-specific-Word-Embeddings.pdf},
year = {2021},
date = {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/MBIC-–-A-Media-Bias-Annotation-Dataset-Including-Annotator-Characteristics.pdf},
year = {2021},
date = {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, 58 (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, 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.
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 = {xxxx},
year = {2020},
date = {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}
}
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.