%0 Conference Proceedings
%T Predicting the locations of unrest using social media
%+ The University of Hong Kong (HKU)
%A Qin, Shengzhi
%A Wen, Qiaokun
%A Chow, Kam-Pui
%Z Part 4: Novel Applications
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 17th IFIP International Conference on Digital Forensics (DigitalForensics)
%C Virtual, China
%Y Gilbert Peterson
%Y Sujeet Shenoi
%I Springer International Publishing
%3 Advances in Digital Forensics XVII
%V AICT-612
%P 177-191
%8 2021-02-01
%D 2021
%R 10.1007/978-3-030-88381-2_9
%K Social media analysis
%K location extraction
%K named entity recognition
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%2 https://inria.hal.science/hal-03764379/document
%2 https://inria.hal.science/hal-03764379/file/522103_1_En_9_Reference.pdf
%L hal-03764379
%U https://inria.hal.science/hal-03764379
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC11
%~ IFIP-DF
%~ IFIP-WG11-9
%~ IFIP-AICT-612