Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Subject: search.filters.subject.Fake news detection

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Fake News Detection for Hindi Language
    (CEUR-WS, 2022) Madathil, K.T.; Mirji, N.; Charan, R.; Anand Kumar, A.M.
    The understanding of the term “Fake news†varies from one individual to the other. If we look into the basic meaning of “Fake news†, it refers to inappropriate and made up news. In most cases, the news is made up of baseless sources and facts. These news generally mislead the reader and are generally published for one’s own benefit or to defame others. In recent years, a large population is active on various social media platforms and hence they have become the major medium through which fake news is circulated. A lot of fake news is been circulated in local languages as well. Also most of the existing work is based on the English language and only very little work is done using resource scare language for fake news identification like Indic Languages. So this paper focuses to define false news and suggest an effective method for detecting fake news in Hindi using standard machine learning algorithms like Multi-layer Perceptron and Naive Bayes and deep learning techniques like transforms - mainly mBERT. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
  • No Thumbnail Available
    Item
    Detecting Fake News: A Comparative Evaluation of Machine Learning Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2024) Aishwarya, C.; Venkatesan, M.; Prabhavathy, P.; Shetty, A.S.
    Fake news is a significant and well-acknowledged problem in contemporary society due to its rapid spread via social media and various online networking platforms, thereby making it difficult to determine the validity of information. In this study, we examine literature for this issue, prevalent datasets like LIAR, Politifact, and COVID-19, as well as classical machine learning and deep learning models such as SVM, BiLSTM, and CNN- BiGRU for fake news detection, and analyze their effectiveness and scope of application for fake news detection. © 2024 IEEE.
  • No Thumbnail Available
    Item
    Applying Multi-Modal Quantum Deep Learning Algorithms for Enhanced Fake News Detection
    (Jagiellonian University, 2025) Aishwarya, C.; Venkatesan, M.; Prabhavathy; Akanksha, D.
    The pervasive spread of fake news across digital platforms has prompted the development of advanced detection systems. This review surveys and compares state-of-the-art multimodal deep learning models, including SpotFake, BDANN, MVAE, EANN, and the attention-based model by Guo et al., across benchmark datasets such as Twitter and Weibo. We present detailed performance comparisons, with SpotFake achieving an accuracy of 86.1% on the Twitter dataset. Key contributions of this review include the introduction of taxonomy tables based on fusion strategy and model architecture, a critical comparison of early, late, and hybrid fusion mechanisms, and a comprehensive evaluation of cross-modal generalization capabilities. In addition, we explore recent efforts in Quantum Machine Learning (QML), highlighting variational quantum circuits and hybrid quantum-classical models as promising approaches for enhancing scalability and efficiency. This work serves as a roadmap for building robust, interpretable, and scalable fake news detection systems that integrate both classical and quantum techniques. Povzetek: Pregled primerja multimodalne modele za zaznavanje lažnih novic (SpotFake, BDANN, MVAE, EANN, Guo) na Twitterju in Weibou ter predstavi taksonomije fuzije in arhitektur. Obravnava tudi obetavne kvantne pristope, ki lahko izboljšajo skalabilnost in u?inkovitost prihodnjih sistemov. © (2026). All right reserved.