IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0256940.html
   My bibliography  Save this article

Improving fake news classification using dependency grammar

Author

Listed:
  • Kitti Nagy
  • Jozef Kapusta

Abstract

Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.

Suggested Citation

  • Kitti Nagy & Jozef Kapusta, 2021. "Improving fake news classification using dependency grammar," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-22, September.
    • Handle: RePEc:plo:pone00:0256940
    DOI: 10.1371/journal.pone.0256940
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256940
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0256940&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0256940?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
    ---><---

    References listed on IDEAS

    as
    1. Asad Masood Khattak & Rabia Batool & Fahad Ahmed Satti & Jamil Hussain & Wajahat Ali Khan & Adil Mehmood Khan & Bashir Hayat, 2020. "Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation," Complexity, Hindawi, vol. 2020, pages 1-11, December.
    2. Kai Shu & Deepak Mahudeswaran & Huan Liu, 2019. "FakeNewsTracker: a tool for fake news collection, detection, and visualization," Computational and Mathematical Organization Theory, Springer, vol. 25(1), pages 60-71, March.
    3. Dogan, Turgut & Uysal, Alper Kursat, 2020. "A novel term weighting scheme for text classification: TF-MONO," Journal of Informetrics, Elsevier, vol. 14(4).
    4. Talwar, Shalini & Dhir, Amandeep & Singh, Dilraj & Virk, Gurnam Singh & Salo, Jari, 2020. "Sharing of fake news on social media: Application of the honeycomb framework and the third-person effect hypothesis," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    5. Shanchun Zhou & Wei Liu & Wei Wang, 2021. "English Grammar Error Correction Algorithm Based on Classification Model," Complexity, Hindawi, vol. 2021, pages 1-11, 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. Jabeen, Fauzia & Tandon, Anushree & Azad, Nasreen & Islam, A.K.M. Najmul & Pereira, Vijay, 2023. "The dark side of social media platforms: A situation-organism-behaviour-consequence approach," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    2. Talwar, Manish & Talwar, Shalini & Kaur, Puneet & Tripathy, Naliniprava & Dhir, Amandeep, 2021. "Has financial attitude impacted the trading activity of retail investors during the COVID-19 pandemic?," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    3. Liu, Hongfei & Liu, Wentong & Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2021. "COVID-19 information overload and generation Z's social media discontinuance intention during the pandemic lockdown," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    4. Ghaznavi, Saeeda Naz & Ali, Syed Yousuf & Mahmood Khan, Neha & Fatima, Mahrukh & Shakoor, Iqra, 2022. "Factors That Motivates Fake News Sharing Among Social Media Users: A Case of an Emerging Economy," MPRA Paper 112302, University Library of Munich, Germany.
    5. Shankar, Amit & Dhir, Amandeep & Talwar, Shalini & Islam, Nazrul & Sharma, Piyush, 2022. "Balancing food waste and sustainability goals in online food delivery: Towards a comprehensive conceptual framework," Technovation, Elsevier, vol. 117(C).
    6. Yoo, Nari & Jang, Sou Hyun, 2024. "Does social empathy moderate fear-induced minority blaming during the COVID-19 pandemic?," Social Science & Medicine, Elsevier, vol. 346(C).
    7. Sheikh, Muhammad Ammad & Mumtaz, Talha & Sohail, Nabeel & Ahmed, Bilal & Noor, Zain, 2021. "Fake News Acceptance by Demographics and Culture On Social Media," MPRA Paper 108934, University Library of Munich, Germany.
    8. Ammara Malik & Faiza Bashir & Khalid Mahmood, 2023. "Antecedents and Consequences of Misinformation Sharing Behavior among Adults on Social Media during COVID-19," SAGE Open, , vol. 13(1), pages 21582440221, January.
    9. Kumar, Sushant & Shah, Arunima, 2021. "Revisiting food delivery apps during COVID-19 pandemic? Investigating the role of emotions," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    10. Alam, Faizan & Tao, Meng & Rastogi, Rashmi & Mendiratta, Aparna & Attri, Rekha, 2024. "Do social media influencers influence the vaccination drive? An application of source credibility theory and uses and gratification theory," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    11. Xuan Liu & Tianyi Shi & Guohui Zhou & Mingzhe Liu & Zhengtong Yin & Lirong Yin & Wenfeng Zheng, 2023. "Emotion classification for short texts: an improved multi-label method," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.
    12. Keegan, Brendan James & McCarthy, Ian P. & Kietzmann, Jan & Canhoto, Ana Isabel, 2024. "On your marks, headset, go! Understanding the building blocks of metaverse realms," Business Horizons, Elsevier, vol. 67(1), pages 107-119.
    13. Badghish, Saeed & Shaik, Aqueeb Sohail & Sahore, Nidhi & Srivastava, Shalini & Masood, Ayesha, 2024. "Can transactional use of AI-controlled voice assistants for service delivery pickup pace in the near future? A social learning theory (SLT) perspective," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    14. Talwar, Shalini & Luqman, Adeel & Kaur, Puneet & Srivastava, Pallavi & Mishra, Shreya, 2023. "How social networking ties mediate the associations between enterprise social media affordances and employee agility?," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    15. Raj, Chahat & Meel, Priyanka, 2022. "People lie, actions Don't! Modeling infodemic proliferation predictors among social media users," Technology in Society, Elsevier, vol. 68(C).
    16. Hsin‐Hui Lin & Ching‐Feng Chen & Chih‐Lun Wu, 2023. "The effects of news authenticity and social media tie strength on consumer dissemination behavior," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 2292-2313, June.
    17. Luis Cárdenas & Cristóbal Fernández-Robin & Diego Yáñez, 2023. "Influence of Social and Emotional Factors on the Intention to Use Instagram Stories," SAGE Open, , vol. 13(3), pages 21582440231, August.
    18. Mortimer, Gary & Wang, Shasha & Osorio Andrade, María Lucila, 2023. "Measuring customer aggression: Scale development and validation," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    19. Masood, Muhammad Ali & Abbasi, Rabeeh Ayaz, 2021. "Using graph embedding and machine learning to identify rebels on twitter," Journal of Informetrics, Elsevier, vol. 15(1).
    20. Kaur, Puneet & Dhir, Amandeep & Tandon, Anushree & Alzeiby, Ebtesam A. & Abohassan, Abeer Ahmed, 2021. "A systematic literature review on cyberstalking. An analysis of past achievements and future promises," Technological Forecasting and Social Change, Elsevier, vol. 163(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0256940. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.