IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i3p29-d760377.html
   My bibliography  Save this article

H-Prop and H-Prop-News: Computational Propaganda Datasets in Hindi

Author

Listed:
  • Deptii Chaudhari

    (Department of CS & IT, Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), Pune 412115, India)

  • Ambika Vishal Pawar

    (Department of CS & IT, Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), Pune 412115, India)

  • Alberto Barrón-Cedeño

    (Department of Interpreting and Translation, Alma Mater Studiorum-Università di Bologna, Corso della Repubblica 136, 47121 Forlì, Italy)

Abstract

In this digital era, people rely on the internet for their news consumption. As people are free to express their opinions on social media, much information shared on the internet is loaded with propaganda. Propagandist contents are intended to influence public opinion. In the mainstream media or prominent news agencies, the authors’ and news agencies’ own bias may impact in the news contents. Hence, it is required to detect such propaganda spread through news articles. Detection and classification of propagandist text require standard, high-quality, annotated datasets. A few datasets are available for propaganda classification. However, these datasets are mostly in English. Hindi is the most spoken language in India, and efforts are needed to detect its propagandist contents. This research work introduces two new datasets: H-Prop and H-Prop-News, which consist of news articles in Hindi annotated as propaganda or non-propaganda. The H-Prop dataset is generated by translating 28,630 news articles from the QProp dataset. The H-Prop-News dataset contains 5500 news articles collected from 32 prominent Hindi news websites. We experiment with the proposed datasets using four supervised machine learning models combined with different feature vectors and word embeddings. Our experiments achieve 87% accuracy using Logistic Regression with TF-IDF feature vectors. The datasets provide high-quality labeled news articles in Hindi and open new avenues for researchers to explore techniques for analyzing and classifying propaganda in Hindi text.

Suggested Citation

  • Deptii Chaudhari & Ambika Vishal Pawar & Alberto Barrón-Cedeño, 2022. "H-Prop and H-Prop-News: Computational Propaganda Datasets in Hindi," Data, MDPI, vol. 7(3), pages 1-11, February.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:3:p:29-:d:760377
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/3/29/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/7/3/29/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adel R. Alharbi & Amer Aljaedi, 2019. "Predicting Rogue Content and Arabic Spammers on Twitter," Future Internet, MDPI, vol. 11(11), pages 1-21, October.
    2. Denis Stukal & Sergey Sanovich & Joshua A. Tucker & Richard Bonneau, 2019. "For Whom the Bot Tolls: A Neural Networks Approach to Measuring Political Orientation of Twitter Bots in Russia," SAGE Open, , vol. 9(2), pages 21582440198, April.
    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. Manuel Chaves-Maza & Eugenio M. Fedriani Martel, 2020. "Entrepreneurship support ways after the COVID-19 crisis," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 662-681, December.
    2. Müller-Hansen, Finn & Lee, Yuan Ting & Callaghan, Max & Jankin, Slava & Minx, Jan C., 2022. "The German coal debate on Twitter: Reactions to a corporate policy process," Energy Policy, Elsevier, vol. 169(C).
    3. Ioannis Kosmas & Theofanis Papadopoulos & Georgia Dede & Christos Michalakelis, 2023. "The Use of Artificial Neural Networks in the Public Sector," FinTech, MDPI, vol. 2(1), pages 1-15, March.

    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:gam:jdataj:v:7:y:2022:i:3:p:29-:d:760377. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.