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PREDICTING THE LOCATIONS OF UNREST USING SOCIAL MEDIA

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Advances in Digital Forensics XVII (DigitalForensics 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 612))

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Abstract

The public often relies on social media to discuss and organize activities such as rallies and demonstrations. Monitoring and analyzing open-source social media platforms can provide insights into the locations and scales of rallies and demonstrations, and help ensure that they are peaceful and orderly.

This chapter describes a dictionary-based, semi-supervised learning methodology for obtaining location information from Chinese web forums. The methodology trains a named entity recognition model using a small amount of labeled data and employs n-grams and association rule mining to validate the results. The validated data becomes the new training dataset; this step is performed iteratively to train the named entity recognition model. Experimental results demonstrate that the iteratively-trained model has much better performance than other models described in the research literature.

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Correspondence to Kam-Pui Chow .

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Qin, S., Wen, Q., Chow, KP. (2021). PREDICTING THE LOCATIONS OF UNREST USING SOCIAL MEDIA. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XVII. DigitalForensics 2021. IFIP Advances in Information and Communication Technology, vol 612. Springer, Cham. https://doi.org/10.1007/978-3-030-88381-2_9

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  • DOI: https://doi.org/10.1007/978-3-030-88381-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88380-5

  • Online ISBN: 978-3-030-88381-2

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