Predicting the locations of unrest using social media - Advances in Digital Forensics XVII Access content directly
Conference Papers Year : 2021

Predicting the locations of unrest using social media

Kam-Pui Chow
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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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hal-03764379 , version 1 (31-08-2022)

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Shengzhi Qin, Qiaokun Wen, Kam-Pui Chow. Predicting the locations of unrest using social media. 17th IFIP International Conference on Digital Forensics (DigitalForensics), Feb 2021, Virtual, China. pp.177-191, ⟨10.1007/978-3-030-88381-2_9⟩. ⟨hal-03764379⟩
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