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Analysis and Early Detection of Rumors in a Post Disaster Scenario

Author

Listed:
  • Tamal Mondal

    (Kalyani Government Engineering College)

  • Prithviraj Pramanik

    (National Institute of Technology Durgapur)

  • Indrajit Bhattacharya

    (Kalyani Government Engineering College)

  • Naiwrita Boral

    (Kalyani Government Engineering College)

  • Saptarshi Ghosh

    (Indian Institute of Technology)

Abstract

The use of online social media for post-disaster situation analysis has recently become popular. However, utilizing information posted on social media has some potential hazards, one of which is rumor. For instance, on Twitter, thousands of verified and non-verified users post tweets to convey information, and not all information posted on Twitter is genuine. Some of them contain fraudulent and unverified information about different facts/incidents - such information are termed as rumors. Identification of such rumor tweets at early stage in the aftermath of a disaster is the main focus of the current work. To this end, a probabilistic model is adopted by combining prominent features of rumor propagation. Each feature has been coded individually in order to extract tweets that have at least one rumor propagation feature. In addition, content-based analysis has been performed to ensure the contribution of the extracted tweets in terms of probability of being a rumor. The proposed model has been tested over a large set of tweets posted during the 2015 Chennai Floods. The proposed model and other four popular baseline rumor detection techniques have been compared with human annotated real rumor data, to check the efficiency of the models in terms of (i) detection of belief rumors and (ii) accuracy at early stage. It has been observed that around 70% of the total endorsed belief rumors have been detected by proposed model, which is superior to other techniques. Finally, in terms of accuracy, the proposed technique also achieved 0.9904 for the considered disaster scenario, which is better than the other methods.

Suggested Citation

  • Tamal Mondal & Prithviraj Pramanik & Indrajit Bhattacharya & Naiwrita Boral & Saptarshi Ghosh, 2018. "Analysis and Early Detection of Rumors in a Post Disaster Scenario," Information Systems Frontiers, Springer, vol. 20(5), pages 961-979, October.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:5:d:10.1007_s10796-018-9837-8
    DOI: 10.1007/s10796-018-9837-8
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    References listed on IDEAS

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    Cited by:

    1. Saptarshi Ghosh & Kripabandhu Ghosh & Debasis Ganguly & Tanmoy Chakraborty & Gareth J. F. Jones & Marie-Francine Moens & Muhammad Imran, 2018. "Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(5), pages 901-907, October.
    2. Bin Xia & Yuxuan Bai & Junjie Yin & Yun Li & Jian Xu, 0. "LogGAN: a Log-level Generative Adversarial Network for Anomaly Detection using Permutation Event Modeling," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    3. Bin Xia & Yuxuan Bai & Junjie Yin & Yun Li & Jian Xu, 2021. "LogGAN: a Log-level Generative Adversarial Network for Anomaly Detection using Permutation Event Modeling," Information Systems Frontiers, Springer, vol. 23(2), pages 285-298, April.
    4. Milad Mirbabaie & Deborah Bunker & Stefan Stieglitz & Annika Deubel, 2020. "Who Sets the Tone? Determining the Impact of Convergence Behaviour Archetypes in Social Media Crisis Communication," Information Systems Frontiers, Springer, vol. 22(2), pages 339-351, April.
    5. A. Geethapriya & S. Valli, 2021. "An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis," Information Systems Frontiers, Springer, vol. 23(3), pages 791-805, June.
    6. Zu, Xu & Diao, Xinyi & Meng, Zhiyi, 2019. "The impact of social media input intensity on firm performance: Evidence from Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    7. Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
    8. Jyoti Prakash Singh & Abhinav Kumar & Nripendra P. Rana & Yogesh K. Dwivedi, 2022. "Attention-Based LSTM Network for Rumor Veracity Estimation of Tweets," Information Systems Frontiers, Springer, vol. 24(2), pages 459-474, April.
    9. Shihang Wang & Zongmin Li & Yuhong Wang & Qi Zhang, 2019. "Machine Learning Methods to Predict Social Media Disaster Rumor Refuters," IJERPH, MDPI, vol. 16(8), pages 1-16, April.
    10. Milad Mirbabaie & Deborah Bunker & Stefan Stieglitz & Annika Deubel, 0. "Who Sets the Tone? Determining the Impact of Convergence Behaviour Archetypes in Social Media Crisis Communication," Information Systems Frontiers, Springer, vol. 0, pages 1-13.

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