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Surv."],"published-print":{"date-parts":[[2023,4,30]]},"abstract":"<jats:p>\n                    The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the \u201cbig picture\u201d\u2014the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas:\n                    <jats:italic>Crime<\/jats:italic>\n                    ,\n                    <jats:italic>Disasters<\/jats:italic>\n                    ,\n                    <jats:italic>Finance<\/jats:italic>\n                    ,\n                    <jats:italic>Physical Environment<\/jats:italic>\n                    ,\n                    <jats:italic>Politics<\/jats:italic>\n                    , and\n                    <jats:italic>Health and Population<\/jats:italic>\n                    . We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.\n                  <\/jats:p>","DOI":"10.1145\/3524498","type":"journal-article","created":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T09:37:39Z","timestamp":1647509859000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey"],"prefix":"10.1145","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2182-3001","authenticated-orcid":false,"given":"Rabindra","family":"Lamsal","sequence":"first","affiliation":[{"name":"School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron","family":"Harwood","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Rodriguez","family":"Read","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2014.29"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSC.2020.00073"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.2196\/19016"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOMW.2011.5928903"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/WI.2016.0089"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-163183"},{"key":"e_1_3_2_8_2","article-title":"Better fine-tuning by reducing representational collapse","author":"Aghajanyan Armen","year":"2020","unstructured":"Armen Aghajanyan, Akshat Shrivastava, Anchit Gupta, Naman Goyal, Luke Zettlemoyer, and Sonal Gupta. 2020. 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