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A semi-supervised model for Persian rumor verification based on content information

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Abstract

Rumor is a collective attempt to interpret a vague but attractive situation by using the power of words. In social networks, false-rumors may have significantly different contextual characteristics from true-rumors at lexical, syntactic, semantic levels. Therefore, this study presents the BERT-SAWS semi-supervised learning model for early verification of Persian rumor by investigating content-based and context features at three views: Contextual Word Embeddings (CWE), speech act, and Writing Style (WS). This model is built by loading pre-trained Bidirectional Encoder Representations from Transformers (BERT) as an unsupervised language representation, fine-tuning it using a small Persian rumor dataset, and combining with a supervised learning model to provide an enriched text representation of the content of the rumor. This text representation enables the model to have a better comprehending of the rumor language to verify rumors better than baseline models for two reasons: (i) early rumor verification by focusing on content-based and context-based features of the source rumor. (ii) overcoming the problem of the shortcoming of the dataset in deep neural networks by loading pre-trained BERT, fine-tuning it using the Persian rumor dataset, and combining with speech act and WS-based features. The empirical results of applying the model on Twitter and Telegram datasets demonstrated that BERT-SAWS can enhance the performance of the classifier from 2% to 18%. It indicates that speech act and WS alongside semantic contextual vectors are helpful features in the rumor verification task.

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Notes

  1. https://www.difference.wiki/context-vs-content/

  2. Part of Speech

  3. http://www.sobhe.ir/hazm/

  4. https://github.com/ICTRC/Parsivar

  5. https://github.com/JKhakpour/virastar.py

  6. www.dadegan.ir

  7. www.pldb.ihcs.ac.ir

  8. http://www.rcisp.com

  9. It is a social media. https://Telegram.org

  10. www.cominsys.ir

  11. http://nunchucker.co.uk/ksoxne/bert-python.html

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Zoleikha Jahanbakhsh-Nagadeh & Arash Sharifi

  2. ComInSys Laboratory, Department of Computer Engineering, University of Tabriz, Tabriz, Iran

    Mohammad-Reza Feizi-Derakhshi

Authors
  1. Zoleikha Jahanbakhsh-Nagadeh
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  2. Mohammad-Reza Feizi-Derakhshi
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  3. Arash Sharifi
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Correspondence to Mohammad-Reza Feizi-Derakhshi.

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Jahanbakhsh-Nagadeh, Z., Feizi-Derakhshi, MR. & Sharifi, A. A semi-supervised model for Persian rumor verification based on content information. Multimed Tools Appl 80, 35267–35295 (2021). https://doi.org/10.1007/s11042-020-10077-3

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