Abstract
Preventing explosion of rumors on the Internet asks for a quick automatic detection mechanism that can detect rumors according to the given true information. Previous automatic rumors detection models are mainly built by training a supervised classification model on a labeled dataset containing rumor samples and true information samples. However, in many real cases, there is only one short piece of available true information sample given by an authority in form of an explanation or a correction of a rumor. The explanation sample is often short and very similar to rumor samples, making it difficult to train a discriminative classifier to check whether a piece of information is a rumor or an explanation of rumors. It is necessary to build a model to detect whether a short text is a rumor text or an explanation of rumors, which can be used to as evidences for detecting and refuting rumors. In this paper, we presented a sentence preprocessing method that extracts the leftmost longest common sequence to obtain the common and difference subsequences between the rumor text and its explanation text to compose samples and train a supervised model for classification between rumors and explanation of rumors. Experiments show the effectiveness of the proposed method.
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Acknowledgement
This work was partially supported by the Joint Project of CAS and Austria on ADaptive and Autonomous Data Performance Connectivity and Decentralized Transport Decision-Making Network (ADAPT, No. 881703) and the Innovation Funding Project “Internet Fake News Detection Method Research” (No. MS2021-05) granted by Institute of Scientific and Technical Information of China.
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Sun, X., Zhang, J., Sang, Y. (2022). Classification Between Rumors and Explanations of Rumors Based on Common and Difference Subsequences of Sentences. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_9
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DOI: https://doi.org/10.1007/978-3-031-03948-5_9
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