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A deep learning framework for clickbait detection on social area network using natural language cues

Author

Listed:
  • Bilal Naeem

    (National University of Computer and Emerging Sciences)

  • Aymen Khan

    (National University of Computer and Emerging Sciences)

  • Mirza Omer Beg

    (National University of Computer and Emerging Sciences)

  • Hasan Mujtaba

    (National University of Computer and Emerging Sciences)

Abstract

Social networks are generating huge amounts of complex textual data which is becoming increasingly difficult to process intelligently. Misinformation on social media networks, in the form of fake news, has the power to influence people, sway opinions and even have a decisive impact on elections. To shield ourselves against manipulative misinformation, we need to develop a reliable mechanism to detect fake news. Yellow journalism along with sensationalism has done a lot of damage by misrepresenting facts and manipulating readers into believing false narratives through hyperbole. Clickbait does exactly this by using characteristics of natural language to entice users into clicking a link and can hence be classified as fake news. In this paper, we present a deep learning framework for clickbait detection. The framework is trained to model the intrinsic characteristics of clickbait for knowledge discovery and then used for decision making by classifying headlines as either clickbait or legitimate news. We focus our attention on the linguistic analysis during the knowledge discovery phase as we investigate the underlying structure of clickbait headlines using our Part of Speech Analysis Module. The decision-making task of classification is carried out using long short-term memory. We believe that it is our framework’s architecture that has played a pivotal role to outperform the current state of the art with a classification accuracy of 97%.

Suggested Citation

  • Bilal Naeem & Aymen Khan & Mirza Omer Beg & Hasan Mujtaba, 2020. "A deep learning framework for clickbait detection on social area network using natural language cues," Journal of Computational Social Science, Springer, vol. 3(1), pages 231-243, April.
    • Handle: RePEc:spr:jcsosc:v:3:y:2020:i:1:d:10.1007_s42001-020-00063-y
    DOI: 10.1007/s42001-020-00063-y
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    Citations

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    Cited by:

    1. Itishree Panda & Jyoti Prakash Singh & Gayadhar Pradhan & Khushi Kumari, 2024. "A deep learning framework for clickbait spoiler generation and type identification," Journal of Computational Social Science, Springer, vol. 7(1), pages 671-693, April.
    2. Olga Papadopoulou & Evangelia Kartsounidou & Symeon Papadopoulos, 2022. "COVID-Related Misinformation Migration to BitChute and Odysee," Future Internet, MDPI, vol. 14(12), pages 1-22, November.
    3. Anna Ruelens, 2022. "Analyzing user-generated content using natural language processing: a case study of public satisfaction with healthcare systems," Journal of Computational Social Science, Springer, vol. 5(1), pages 731-749, May.
    4. Muhammad Saad Javed & Hammad Majeed & Hasan Mujtaba & Mirza Omer Beg, 2021. "Fake reviews classification using deep learning ensemble of shallow convolutions," Journal of Computational Social Science, Springer, vol. 4(2), pages 883-902, November.
    5. Tobias Blanke & Tommaso Venturini, 2022. "A network view on reliability: using machine learning to understand how we assess news websites," Journal of Computational Social Science, Springer, vol. 5(1), pages 69-88, May.

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