{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T03:11:36Z","timestamp":1790737896153,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T00:00:00Z","timestamp":1570147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1416509"],"award-info":[{"award-number":["1416509"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Social media platforms have become a critical virtual community where people share information and discuss issues. Their capabilities for fast dissemination and massive participation have placed under scrutiny the way in which they influence people\u2019s perceptions over time and space. This paper investigates how El Ni\u00f1o, an extreme recurring weather phenomenon, was discussed on Twitter in the United States from December 2015 to January 2016. A multiple-dimensional analysis, including spatial, social, temporal, and semantic perspectives, is conducted to comprehensively understand Twitter users\u2019 discussion of such weather phenomenon. We argue that such multi-dimensional analysis can reveal complicated patterns of Twitter users\u2019 online discussion and answers questions that cannot be addressed with a single-dimension analysis. For example, a significant increase in tweets about El Ni\u00f1o was noted when a series of rainstorms inundated California in January 2016. Some discussions on natural disasters were influenced by their geographical distances to the disasters and the prevailing geopolitical environment. The popular tweets generally discussing El Ni\u00f1o were overall negative, while tweets talking about how to prepare for the California rainstorms were more positive.<\/jats:p>","DOI":"10.3390\/ijgi8100436","type":"journal-article","created":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T10:54:58Z","timestamp":1570186498000},"page":"436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Multi-Dimensional Analysis of El Ni\u00f1o on Twitter: Spatial, Social, Temporal, and Semantic Perspectives"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8838-9476","authenticated-orcid":false,"given":"Xinyue","family":"Ye","sequence":"first","affiliation":[{"name":"Department of Informatics, New Jersey Institute of Technology, Newark, NJ 07102, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2197-5184","authenticated-orcid":false,"given":"Xuebin","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Integrated Science, James Madison University, Harrisonburg, VA 22807, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/S0959-3780(00)00063-7","article-title":"Public understandings of air pollution: the \u2018localisation\u2019of environmental risk","volume":"11","author":"Bickerstaff","year":"2001","journal-title":"Glob. 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