@article{Hamborg2020-06Biasa-43180,
  year={2020},
  doi={10.1007/s00799-018-0239-9},
  title={Bias-aware news analysis using matrix-based news aggregation},
  number={2},
  volume={21},
  issn={1432-5012},
  journal={International Journal on Digital Libraries},
  pages={129--147},
  author={Hamborg, Felix and Meuschke, Norman and Gipp, Bela}
}

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    <dcterms:abstract xml:lang="eng">Media bias describes differences in the content or presentation of news. It is an ubiquitous phenomenon in news coverage that can have severely negative effects on individuals and society. Identifying media bias is a challenging problem, for which current information systems offer little support. News aggregators are the most important class of systems to support users in coping with the large amount of news that is published nowadays. These systems focus on identifying and presenting important, common information in news articles, but do not reveal different perspectives on the same topic. Due to this analysis approach, current news aggregators cannot effectively reveal media bias. To address this problem, we present matrix-based news aggregation, a novel approach for news exploration that helps users gain a broad and diverse news understanding by presenting various perspectives on the same news topic. Additionally, we present NewsBird, an open-source news aggregator that implements matrix-based news aggregation for international news topics. The results of a user study showed that NewsBird more effectively broadens the user’s news understanding than the list-based visualization approach employed by established news aggregators, while achieving comparable effectiveness and efficiency for the two main use cases of news consumption: getting an overview of and finding details on current news topics.</dcterms:abstract>
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    <dcterms:title>Bias-aware news analysis using matrix-based news aggregation</dcterms:title>
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