Unveiling the Relationship Between News Recommendation Algorithms and Media Bias: A Simulation-Based Analysis of the Evolution of Bias Prevalence Article Swipe
Qin Ruan
,
Brian Mac Namee
,
Ruihai Dong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-031-47994-6_17
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-031-47994-6_17
Media bias has significant negative effects, such as influencing elections and shaping people's perceptions. However, the relationship between media bias and personalised news recommendation algorithms (widely adopted by many news platforms) remains unclear. In this study, we describe a novel framework that simulates user interactions with recommendation algorithms, allowing us to explore how the degree of bias in the news articles presented to users by personalized recommendation systems changes over time. Our experiments show that leading personalized news recommendation algorithms are sensitive to media bias, causing shifts in the proportion of biased news articles they recommend over time. These findings emphasize the importance of recognizing the influence of media bias on personalized news recommendation algorithms and the need to raise user awareness about media bias to encourage more diverse and balanced news consumption. The source code is available at https://github.com/ruanqin0706/UserRecSimulation.git .
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Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.1007/978-3-031-47994-6_17
- OA Status
- hybrid
- Cited By
- 4
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388468543
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388468543Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/978-3-031-47994-6_17Digital Object Identifier
- Title
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Unveiling the Relationship Between News Recommendation Algorithms and Media Bias: A Simulation-Based Analysis of the Evolution of Bias PrevalenceWork title
- Type
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book-chapterOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
-
Qin Ruan, Brian Mac Namee, Ruihai DongList of authors in order
- Landing page
-
https://doi.org/10.1007/978-3-031-47994-6_17Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1007/978-3-031-47994-6_17Direct OA link when available
- Concepts
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Computer science, Recommender system, Media bias, Algorithm, News media, Code (set theory), Information retrieval, Source code, Advertising, Political science, Set (abstract data type), Programming language, Law, Operating system, Politics, BusinessTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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13Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sensitive | 81 |
| abstract_inverted_index.simulates | 42 |
| abstract_inverted_index.algorithms | 24, 79, 114 |
| abstract_inverted_index.importance | 102 |
| abstract_inverted_index.platforms) | 30 |
| abstract_inverted_index.proportion | 89 |
| abstract_inverted_index.algorithms, | 47 |
| abstract_inverted_index.experiments | 72 |
| abstract_inverted_index.influencing | 8 |
| abstract_inverted_index.recognizing | 104 |
| abstract_inverted_index.significant | 3 |
| abstract_inverted_index.consumption. | 132 |
| abstract_inverted_index.interactions | 44 |
| abstract_inverted_index.perceptions. | 13 |
| abstract_inverted_index.personalised | 21 |
| abstract_inverted_index.personalized | 65, 76, 111 |
| abstract_inverted_index.relationship | 16 |
| abstract_inverted_index.recommendation | 23, 46, 66, 78, 113 |
| abstract_inverted_index.https://github.com/ruanqin0706/UserRecSimulation.git | 139 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5101533097 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I100930933 |
| citation_normalized_percentile.value | 0.98753706 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |