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Rumor Detection over Varying Time Windows

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  • Sejeong Kwon
  • Meeyoung Cha
  • Kyomin Jung

Abstract

This study determines the major difference between rumors and non-rumors and explores rumor classification performance levels over varying time windows—from the first three days to nearly two months. A comprehensive set of user, structural, linguistic, and temporal features was examined and their relative strength was compared from near-complete date of Twitter. Our contribution is at providing deep insight into the cumulative spreading patterns of rumors over time as well as at tracking the precise changes in predictive powers across rumor features. Statistical analysis finds that structural and temporal features distinguish rumors from non-rumors over a long-term window, yet they are not available during the initial propagation phase. In contrast, user and linguistic features are readily available and act as a good indicator during the initial propagation phase. Based on these findings, we suggest a new rumor classification algorithm that achieves competitive accuracy over both short and long time windows. These findings provide new insights for explaining rumor mechanism theories and for identifying features of early rumor detection.

Suggested Citation

  • Sejeong Kwon & Meeyoung Cha & Kyomin Jung, 2017. "Rumor Detection over Varying Time Windows," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-19, January.
    • Handle: RePEc:plo:pone00:0168344
    DOI: 10.1371/journal.pone.0168344
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    Cited by:

    1. 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.
    2. Wingyan Chung & Yinqiang Zhang & Jia Pan, 2023. "A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media," Information Systems Frontiers, Springer, vol. 25(2), pages 473-492, April.
    3. Bei Bi & Yaojun Wang & Haicang Zhang & Yang Gao, 2022. "Microblog-HAN: A micro-blog rumor detection model based on heterogeneous graph attention network," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-18, April.
    4. Zhu, He & Ma, Jing & Li, Shan, 2019. "Effects of online and offline interaction on rumor propagation in activity-driven networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1124-1135.
    5. Lingnan He & Haoshen Yang & Xiling Xiong & Kaisheng Lai, 2019. "Online Rumor Transmission Among Younger and Older Adults," SAGE Open, , vol. 9(3), pages 21582440198, September.
    6. Na Ye & Dingguo Yu & Yijie Zhou & Ke-ke Shang & Suiyu Zhang, 2023. "Graph Convolutional-Based Deep Residual Modeling for Rumor Detection on Social Media," Mathematics, MDPI, vol. 11(15), pages 1-11, August.
    7. Serveh Lotfi & Mitra Mirzarezaee & Mehdi Hosseinzadeh & Vahid Seydi, 2021. "Rumor conversations detection in twitter through extraction of structural features," Information Technology and Management, Springer, vol. 22(4), pages 265-279, December.
    8. Kathrin Eismann, 2021. "Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter," Journal of Business Economics, Springer, vol. 91(9), pages 1299-1329, November.
    9. Xiaohui Zhang & Qianzhou Du & Zhongju Zhang, 2022. "A theory‐driven machine learning system for financial disinformation detection," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3160-3179, August.
    10. Abderrazek Azri & Cécile Favre & Nouria Harbi & Jérôme Darmont & Camille Noûs, 2023. "Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning," Information Systems Frontiers, Springer, vol. 25(5), pages 1795-1810, October.
    11. Lian, Ying & Liu, Yijun & Dong, Xuefan, 2020. "Strategies for controlling false online information during natural disasters: The case of Typhoon Mangkhut in China," Technology in Society, Elsevier, vol. 62(C).

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