@inproceedings{Hamborg2020Newsa-51335,
  year={2020},
  doi={10.1145/3383583.3398561},
  title={Newsalyze : Enabling News Consumers to Understand Media Bias},
  isbn={978-1-4503-7585-6},
  publisher={ACM},
  address={New York, NY},
  booktitle={JCDL '20 : Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020},
  pages={455--456},
  author={Hamborg, Felix and Zhukova, Anastasia and Donnay, Karsten and Gipp, Bela}
}

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    <dc:creator>Donnay, Karsten</dc:creator>
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    <dcterms:title>Newsalyze : Enabling News Consumers to Understand Media Bias</dcterms:title>
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    <dc:contributor>Hamborg, Felix</dc:contributor>
    <dcterms:abstract xml:lang="eng">News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view).</dcterms:abstract>
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    <dc:contributor>Gipp, Bela</dc:contributor>
    <dc:creator>Gipp, Bela</dc:creator>
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