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. 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).
    3. Shanchun Zhou & Wei Liu & Wei Wang, 2021. "English Grammar Error Correction Algorithm Based on Classification Model," Complexity, Hindawi, vol. 2021, pages 1-11, January.
    4. 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.
    5. Dogan, Turgut & Uysal, Alper Kursat, 2020. "A novel term weighting scheme for text classification: TF-MONO," Journal of Informetrics, Elsevier, vol. 14(4).
    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. Giandomenico Domenico & Annamaria Tuan & Marco Visentin, 2021. "Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic," Italian Journal of Marketing, Springer, vol. 2021(4), pages 351-369, December.
    6. Dhir, Amandeep & Khan, Sher Jahan & Islam, Nazrul & Ractham, Peter & Meenakshi, N., 2023. "Drivers of sustainable business model innovations. An upper echelon theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    7. Nanath, Krishnadas & Balasubramanian, Sreejith & Shukla, Vinaya & Islam, Nazrul & Kaitheri, Supriya, 2022. "Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    8. Raluca Buturoiu & Georgiana Udrea & Denisa-Adriana Oprea & Nicoleta Corbu, 2021. "Who Believes in Conspiracy Theories about the COVID-19 Pandemic in Romania? An Analysis of Conspiracy Theories Believers’ Profiles," Societies, MDPI, vol. 11(4), pages 1-16, November.
    9. Tandon, Anushree & Dhir, Amandeep & Talwar, Shalini & Kaur, Puneet & Mäntymäki, Matti, 2021. "Dark consequences of social media-induced fear of missing out (FoMO): Social media stalking, comparisons, and fatigue," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    10. Sharma, Manu & Kaushal, Deepak & Joshi, Sudhanshu, 2023. "Adverse effect of social media on generation Z user's behavior: Government information support as a moderating variable," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    11. 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).
    12. 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).
    13. 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.
    14. 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.
    15. Ye Sang & Heeseung Yu & Eunkyoung Han, 2022. "Understanding the Barriers to Consumer Purchasing of Zero-Waste Products," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    16. 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).
    17. Sarraf, Shagun & Kushwaha, Amit Kumar & Kar, Arpan Kumar & Dwivedi, Yogesh K. & Giannakis, Mihalis, 2024. "How did online misinformation impact stockouts in the e-commerce supply chain during COVID-19 – A mixed methods study," International Journal of Production Economics, Elsevier, vol. 267(C).
    18. 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).
    19. Farhan Shehzad & Abdur Rehman & Kashif Javed & Khalid A. Alnowibet & Haroon A. Babri & Hafiz Tayyab Rauf, 2022. "Binned Term Count: An Alternative to Term Frequency for Text Categorization," Mathematics, MDPI, vol. 10(21), pages 1-25, November.
    20. Kumar, Sushant & Talwar, Shalini & Krishnan, Satish & Kaur, Puneet & Dhir, Amandeep, 2021. "Purchasing natural personal care products in the era of fake news? The moderation effect of brand trust," Journal of Retailing and Consumer Services, Elsevier, vol. 63(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.