IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v8y2025i2d10.1007_s42001-025-00372-0.html
   My bibliography  Save this article

Unraveling media perspectives: a comprehensive methodology combining large language models, topic modeling, sentiment analysis, and ontology learning to analyse media bias

Author

Listed:
  • Orlando Jähde

    (FOM University of Applied Sciences in Economics and Management)

  • Thorsten Weber

    (FOM University of Applied Sciences in Economics and Management)

  • Rüdiger Buchkremer

    (FOM University of Applied Sciences in Economics and Management)

Abstract

Biased news reporting poses a significant threat to informed decision-making and the functioning of democracies. This study introduces a novel methodology for scalable, minimally biased analysis of media bias in political news. The proposed approach examines event selection, labeling, word choice, and commission and omission biases across news sources by leveraging natural language processing techniques, including hierarchical topic modeling, sentiment analysis, and ontology learning with large language models. Through three case studies related to current political events, we demonstrate the methodology’s effectiveness in identifying biases across news sources at various levels of granularity. This work represents a significant step towards scalable, minimally biased media bias analysis, laying the groundwork for tools to help news consumers navigate an increasingly complex media landscape.

Suggested Citation

  • Orlando Jähde & Thorsten Weber & Rüdiger Buchkremer, 2025. "Unraveling media perspectives: a comprehensive methodology combining large language models, topic modeling, sentiment analysis, and ontology learning to analyse media bias," Journal of Computational Social Science, Springer, vol. 8(2), pages 1-56, May.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-025-00372-0
    DOI: 10.1007/s42001-025-00372-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-025-00372-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-025-00372-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bernhardt, Dan & Krasa, Stefan & Polborn, Mattias, 2008. "Political polarization and the electoral effects of media bias," Journal of Public Economics, Elsevier, vol. 92(5-6), pages 1092-1104, June.
    2. Baron, David P., 2006. "Persistent media bias," Journal of Public Economics, Elsevier, vol. 90(1-2), pages 1-36, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Federico Vaccari, 2023. "Influential news and policy-making," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 76(4), pages 1363-1418, November.
    2. Ozerturk, Saltuk, 2022. "Media access, bias and public opinion," European Economic Review, Elsevier, vol. 147(C).
    3. Piolatto, Amedeo & Schuett, Florian, 2015. "Media competition and electoral politics," Journal of Public Economics, Elsevier, vol. 130(C), pages 80-93.
    4. Ascensión Andina Díaz, 2011. "Mass Media in Economics: Origins and Subsequent Contributions," Working Papers 2011-02, Universidad de Málaga, Department of Economic Theory, Málaga Economic Theory Research Center.
    5. John Duggan & César Martinelli, 2008. "The Role of Media Slant in Elections and Economics," Wallis Working Papers WP54, University of Rochester - Wallis Institute of Political Economy.
    6. Julia Cagé & Moritz Hengel & Nicolas Hervé & Camille Urvoy, 2022. "Hosting Media Bias: Evidence from the Universe of French Broadcasts, 2002-2020," SciencePo Working papers Main hal-03878119, HAL.
    7. repec:tiu:tiucen:2013072 is not listed on IDEAS
    8. Anja Prummer, 2016. "Spatial Advertisement in Political Campaigns," Working Papers 805, Queen Mary University of London, School of Economics and Finance.
    9. Petrova, Maria, 2012. "Mass media and special interest groups," Journal of Economic Behavior & Organization, Elsevier, vol. 84(1), pages 17-38.
    10. Strömberg, David & Prat, Andrea, 2011. "The Political Economy of Mass Media," CEPR Discussion Papers 8246, C.E.P.R. Discussion Papers.
    11. Takanori Adachi & Yoichi Hizen, 2014. "Political Accountability, Electoral Control and Media Bias," The Japanese Economic Review, Japanese Economic Association, vol. 65(3), pages 316-343, September.
    12. Paul E. Fischer & Mirko S. Heinle & Kevin C. Smith, 2020. "Constrained listening, audience alignment, and expert communication," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1037-1062, December.
    13. Bruns, Christian & Himmler, Oliver, 2016. "Mass media, instrumental information, and electoral accountability," Journal of Public Economics, Elsevier, vol. 134(C), pages 75-84.
    14. Trombetta, Federico & Rossignoli, Domenico, 2021. "The price of silence: Media competition, capture, and electoral accountability," European Journal of Political Economy, Elsevier, vol. 69(C).
    15. Warren, Patrick L., 2012. "Independent auditors, bias, and political agency," Journal of Public Economics, Elsevier, vol. 96(1), pages 78-88.
    16. Jung, Hanjoon Michael, 2007. "Strategic Information Transmission through the Media," MPRA Paper 5556, University Library of Munich, Germany, revised Oct 2007.
    17. Riccardo Puglisi & James M. Snyder Jr., 2015. "The Balanced Us Press," Journal of the European Economic Association, European Economic Association, vol. 13(2), pages 240-264, April.
    18. Eraslan, Hulya & Ozerturk, Saltuk, 2017. "Information Gatekeeping and Media Bias," Working Papers 17-001, Rice University, Department of Economics.
    19. Chan, Jimmy & Suen, Wing, 2009. "Media as watchdogs: The role of news media in electoral competition," European Economic Review, Elsevier, vol. 53(7), pages 799-814, October.
    20. Archishman Chakraborty & Parikshit Ghosh, 2016. "Character Endorsements and Electoral Competition," American Economic Journal: Microeconomics, American Economic Association, vol. 8(2), pages 277-310, May.
    21. Jacopo Perego & Sevgi Yuksel, 2022. "Media Competition and Social Disagreement," Econometrica, Econometric Society, vol. 90(1), pages 223-265, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-025-00372-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.