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An approach to selecting keywords to track on twitter during a disaster., , and . ISCRAM, ISCRAM Association, (2014)Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages., , , , , , , , and . CoRR, (2023)NeuroTPR: A neuro-net toponym recognition model for extracting locations from social media messages., , and . Trans. GIS, 24 (3): 719-735 (2020)Exploring Patterns of Identity Usage in Tweets: A New Problem, Solution and Case Study., , and . WWW, page 401-412. ACM, (2016)Leveraging media repertoires to create new social ties., , and . UbiComp, page 798-799. ACM, (2012)Polarized, Together: Comparing Partisan Support for Trump's Tweets Using Survey and Platform-Based Measures., , , , and . ICWSM, page 290-301. AAAI Press, (2019)A Bayesian Graphical Model to Discover Latent Events from Twitter., , , and . ICWSM, page 503-512. AAAI Press, (2015)Girls Rule, Boys Drool: Extracting Semantic and Affective Stereotypes from Twitter., , and . CSCW, page 1362-1374. ACM, (2017)Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics"., , and . UbiComp, page 919-926. ACM, (2012)MDR Cluster-Debias: A Nonlinear Word Embedding Debiasing Pipeline., and . SBP-BRiMS, volume 12268 of Lecture Notes in Computer Science, page 45-54. Springer, (2020)