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Rumour Stance Classification on Textual Social Media Content Using Machine Learning

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Advancements in Interdisciplinary Research (AIR 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1738))

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Abstract

Rumour stance classification involves the analysis of how the users react to the rumours linked with the news on social media platforms. It involves the identification of the attitude of the users towards the veracity of the rumour they are conversing about. Spreading of misinformation is a dangerous tendency and it has been widely recognized that it is important to prevent its spreading. Stance classification is a key step toward the verification of rumours. People on social media display various stances, perspectives and judgement towards rumours being spread on a social media platform. These various reactions may be unambiguous, i.e., they either deny the rumour in consideration or support the rumour. While there may be others who just comment on the rumour or ask for its veracity or claim its proof. This paper is focused on predicting user stance, ie, Accepting (S), Rejecting (D), Info-requesting (Q), or Opinion-giving (C). The paper focuses on a user stance classification analysis of rumour and non-rumour events using support vector machines, Gaussian process, multi-layer perceptron, decision tree, adaptive boosting, random forests and naive Bayes.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, India

    Anahita Singla, Arunima Jaiswal, Shruti Aggarwal, Parvati Sohni & Pragya Arora

  2. IT Department, Galgotia College of Engineering and Technology, Greater Noida, India

    Nitin Sachdeva

Authors
  1. Anahita Singla
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  2. Arunima Jaiswal
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  3. Shruti Aggarwal
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  4. Parvati Sohni
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  5. Pragya Arora
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  6. Nitin Sachdeva
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Corresponding author

Correspondence to Arunima Jaiswal .

Editor information

Editors and Affiliations

  1. Oakland University, Rochester, NY, USA

    Vijayan Sugumaran

  2. ABES Engineering College, Ghaziabad, India

    Divya Upadhyay

  3. ABES Engineering College, Ghaziabad, India

    Shanu Sharma

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Singla, A., Jaiswal, A., Aggarwal, S., Sohni, P., Arora, P., Sachdeva, N. (2022). Rumour Stance Classification on Textual Social Media Content Using Machine Learning. In: Sugumaran, V., Upadhyay, D., Sharma, S. (eds) Advancements in Interdisciplinary Research. AIR 2022. Communications in Computer and Information Science, vol 1738. Springer, Cham. https://doi.org/10.1007/978-3-031-23724-9_29

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  • DOI: https://doi.org/10.1007/978-3-031-23724-9_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23723-2

  • Online ISBN: 978-3-031-23724-9

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